It appears that the RAM crisis is getting worse than ever as we head further into 2026. I've been closely watching and reporting on memory price hikes — which were soon followed by other PC component cost increases — since this crisis first began, and the worrying thing is, I can't recall a gloomier stream of negative news than I've witnessed over the past few weeks.

That includes Samsung recently declaring that the RAM crisis is going to become more severe in 2027, and rumors that even mighty Apple is floundering in its attempt to secure new RAM supplies from China (as other notebook makers consider this alternative route to the three main memory chip giants: Micron, Samsung and SK Hynix).

Previous to that, we saw laptop maker Framework break news of a truly eye-opening cost increase for mobile RAM, just as one gauge of DDR5 pricing saw it jump in price to a new all-time high (after plateauing earlier this year). On top of that, the boss of SK Hynix voiced the opinion that 2027 will be the worst year in the RAM industry's history, and that the crisis is likely to be drawn out to the next decade.

This isn't just about RAM, of course, but other components, including CPU price rises, and devices themselves — PCs and laptops (and phones). But most notably over the past month it's been graphics cards in the crisis limelight. We've witnessed the resurrection of old GPUs to try to bolster stock levels of more affordable cards, and more recently we've been subjected to what seems like an endless parade of negativity about imminent price hikes.

Apparently, Gigabyte is set to increase the price tags of its graphics card to the tune of 20% to 40% in Japan, and MSI is going to jack up prices of Nvidia GPUs in China by 20% or more. Asus is supposedly preparing a similar 20% hike (as Wccftech reported).

Another recent gloom nugget is the assertion that Nvidia RTX 5000 models will get hikes of 30% in South Korea this month, and all of this pain is mainly rooted in the increased price of video RAM, of course. (Those cost increases are the reason the RTX 5000 Super refreshes have been delayed, according to the grapevine, as those rumored cards are packed with VRAM).

While a good deal of this is individual pieces of regional activity with GPU pricing, it's obvious that these hikes are happening as part of a concerted shift, one that will surely be reflected globally — it's not like Asian markets are in a bubble of their own.

The overarching theme is a worsening of price hike misery, and that the pricing storm is likely to intensify this year, and probably in 2027, too. While previously the RAM crisis has been more of a light-and-shade affair, now it feels like the depression has been turned up a notch. Whereas before, there was certainly more darkness than light, we've had notable spots of relief where an exec in the memory industry stepped forward and theorized that maybe things aren't as bad as we think. But lately those embers of optimism appear to have burned out.

To me, it feels like there's a shift underway towards a full acknowledgement that we really are going to experience a lot more pricing pain in the foreseeable future. If it's so bad that even Apple is purportedly scrambling to secure RAM supplies, and is having trouble doing so, I think it's time to turn up the worry meter by a notch or two.

What should you do?

Here's my advice on the current landscape with PC components and hardware (season it appropriately). Obviously, worrying isn't going to help you, but awareness of what might be sensible upgrades or purchases to make now will, based on the likelihood of hikes (we could call it the 'hike-lihood' – or maybe not). Nothing is certain about the future of components, of course, but it does feel like there's a clear area in which the potential price hikes are a bigger threat: GPUs.

As I discussed above, there's a lot of recent evidence that graphics card pricing is going up. Yes, it's a collection of rumors in the main, but there's a lot of it all pointing in a very similar direction — and a video RAM toll was always going to be exacted in the end. We've seen it already with higher-end graphics cards, and now I believe we're going to see it in the mid-range, and even budget models. Indeed, top-end GPUs are likely to get even more expensive as well.

If you're thinking about a GPU upgrade for this year or next, given all this, I think now really is the time to buy — especially with a lower-tier model, as you can still get more affordable graphics cards at their MSRP. Ditto for mid-rangers, although it's tougher to recommend higher-end Nvidia boards when they are already so expensive.

As noted, though, that could get worse. And granted, you may still want to wait for Black Friday — it's not that far off, and there might be some GPU deals then. However, I wouldn't bank on anything hugely compelling (or discounts that aren't offset by the price rises which are predicted to take hold in the next few months).

So, if there's one PC component that I think you should buy now, it's a graphics card. I think the signs are pretty clear on that (and see here for my recommendations on different models to consider). By extension, higher-end gaming laptops could also be a wise move for purchasing sooner rather than later. That's for the same reason, really — they have beefy (mobile) GPUs (with plenty of VRAM in some cases).

I don't think it's a bad move to buy any laptop, by the way, gaming or not (away from the higher-end), come your earliest window of opportunity. I think we'll see further price rises here, too, and RAM is going to cause more upward pricing pressures for notebooks — just look at Framework's recent revelation as mentioned.

What about RAM and SSDs themselves? While system memory kits and storage have already seen huge price rises, it now looks like there's worse to come (somehow).

On the other hand, there's a ceiling as to how expensive this stuff can get before buyers withdraw from what they see as an increasingly unrealistic and out-of-touch market. This is a much more difficult call to make, but if you can find something that looks relatively reasonably priced, I don't think you'll regret the purchase in the next couple of years. But that said, I can't recommend a RAM upgrade in particular at current pricing levels, unless it's unavoidable, frankly.

Above all, though, consider a GPU upgrade if you're in the market for a new card, or you think you'll need one in the next couple of years (yes, I think it's wise to be looking quite far down the road here).

The other thing to bear in mind is that there's a danger there will be a rush for GPUs, a flurry of buying that puts further strain on supply and therefore prices. As VideoCardz reports, one Japanese retailer has warned of Nvidia RTX 5000 graphics card sales increasing "sharply", even just with the news of rumored price increases — let alone the confirmation. Prices are already rising and restocks of the RTX 5070 Ti and RTX 5080 are already being labelled as "unstable", hinting that inventory could start to dry up quickly.

That's just one report, of course, and it pertains to the high-end of the market, so it's not something to start panicking about yet. But it does make some sense that if pricing begins to creep up, more PC owners may start to act on GPU upgrades. I wouldn't blame them, frankly.

• Someone made a tool for Asus Astral RTX 5080 and 5090 GPUs
• It's designed to prevent a melting connector, shutting down the PC before this happens
• This third-party utility – and the response to it – shows the level of paranoia out there when it comes to this issue

Do you have a high-end Nvidia Blackwell GPU, and if so, are you worried about the perils of a melted power connector? It appears that definite concerns remain in this area, to the point that a new utility has been crafted to address those worries.

An enterprising Redditor has made their own open source tool for Windows which is designed for Asus Astral RTX 5090 and 5080 graphics cards, acting as a monitoring app that will (hopefully) step in to prevent any melting disasters before they happen.

The creator, Humza Khalid, tells us how the small utility, which is available to grab on GitHub, works: "It reads per pin 12VHPWR current straight from the card's own sensor chip and force shuts down your PC if any pin stays over 9.5A for 15 seconds, before the connector can melt."

The idea is that it's a lightweight and standalone app which is an easy way to defend against the situation where there's a sustained level of too much current flowing to the Nvidia graphics card.

As noted, the PC is forced to shut down in these cases (in a safe manner, with any running or interfering apps being force-closed, we're told), before any damage is done.

The creator has taken a suggestion on board from the comments, and Khalid notes that the tool will be changed from initiating a shutdown to dropping the power limit (via the Nvidia management API) for a much faster response. (Shutting down can take a while). That change will happen in a future version of the utility, Khalid tells us.

The creator also reminds us: "This only works on Astral 5080/5090 cards because those are the only ones with per pin sensors. Windows only, needs Python. And it doesn't replace seating your connector properly, it's a last line of defense not a first one."

Analysis: default paranoia

That's all good in theory, of course, but I'd always be careful about what you're installing on your PC. Not to say this isn't a trustworthy app – I don't know whether it is or not, and that's the point. However, it is open source, so that's good, as it provides visibility into what it's doing under the hood.

Another point to note is that a co-creator of the app is listed as 'Claude', meaning that this AI has been used in making the tool. Khalid explains that they wrote the original utility, but it was based on HWiNFO, so they wanted the app rejigged to be standalone, and Claude "did a lot of the rewrite".

At any rate, Asus Astral GPU owners can make their own decision about what's worth installing, or not, although I wouldn't personally recommend installing a third-party utility from an unknown developer. As ever, if you do install such software, you do so very much at your own risk.

Regardless, what this does show is the clear level of concern about Nvidia's RTX 5090 and 5080 GPUs. One Redditor sums it up: "As a 5090 owner, I can confirm the card comes with a default paranoia about bursting into flames."

That received some pushback from other Redditors who said their overclocked RTX 5090s were just fine, and in turn, there was a further story relayed about a melting incident. Obviously, all that is anecdotal (on both sides of the coin), but the paranoia is real enough.

Cable melting worries began with the RTX 4090, of course, before the RTX 5090, and when we looked into the issues with the Blackwell flagship back at the start of last year, Nvidia firmly denied that any of this is its fault. Cable melts remain a controversial topic, though, and more recently we've even had a report of an exploding RTX 5090, no less, which PC gamers who run these high-end graphics cards probably didn't take much comfort from.

• MSI's Nvidia GPUs are rumored to be getting price hikes
• They'll be 20% more expensive (or worse) in China, and that kind of increase will surely be seen elsewhere
• This is happening against a backdrop of wider price hikes for graphics cards – although Nvidia is offering a rare chance at an RTX 5080 or 5090 at MSRP

As fresh rumors reveal that another graphics card maker is set to hike prices, Nvidia has a plan to let you buy a Founders Edition Blackwell GPU at the MSRP, even in the case of the RTX 5090 — although you'll need to travel.

And you'll need to move very fast, attend QuakeCon in Texas, which starts today, and get yourself to the GeForce booth (hat tip to VideoCardz) before supplies of RTX 5070, 5080, and 5090 Founders Edition graphics cards run out. And I don't think that they will last for long (a matter of minutes, at a guess, although hopefully that won't be the case).

Nvidia describes this as 'Verified Priority Access' in real life, and at least it does give gamers a chance to buy an RTX 5080 and 5090 at the intended price, which is not something you can get anywhere near when purchasing one of these high-end Nvidia GPUs at retail.

As for the most recent news on GPU price hikes, Wccftech spotted that MSI is the latest graphics card maker to announce that its products will be considerably more expensive.

This is just a rumor, one that arrives via the Board Channels in China, and so should be served with some seasoning — but the theory is that MSI's Nvidia GPUs are set for price hikes of 20% or more. This is due to rising memory prices, of course, and what Nvidia charges its partners for video RAM modules (as Team Green is paying more, it passes that bill on, of course).

The price increase is in the order of $300 (£220 / AU$420) for the RTX 5080 over in China (converting from RMB), around $200 (£150 / AU$280) for the RTX 5070 Ti, and approximately $100 (£75 / AU$140) for RTX 5060 models (and not far off that for the RTX 5050).

Sadly, not only will current-gen Nvidia GeForce graphics cards be affected, but also RTX 4000 and 3000 models (with GDDR6 video RAM getting costlier, as well as GDDR7).

Analysis: pricey times

While this is a rumored cost increase for MSI products in China, these exact same pricing pressures are, of course, happening globally, so there's no reason to believe that MSI's GPUs in other regions won't follow suit.

What's more, this isn't just a problem that pertains to MSI, but Nvidia's other card-making partners, and those companies are also rumored to be implementing hefty price hikes. For example, just this week we've heard that Gigabyte GPU prices will jump between 20% and 40%. Also, on a regional basis, Nvidia RTX 5000 models in South Korea are looking at price hikes of up to 30%.

So, this is a coherent picture of hikes, and those percentages are all in a similar ballpark, indicating increases of at least 20% or a good deal more. They are also backed up by more general chatter from the grapevine about Nvidia's VRAM increases and AMD's Radeon GPUs becoming pricier.

I fear that getting any kind of current-gen graphics card at, or near, the MSRP could be a much tougher task before long, and those at QuakeCon better make the most of Nvidia's GeForce offerings at the firm's booth. As noted, I expect this to be the definition of a flash sale — as in they'll be gone in a flash — although you do have another shot at winning an RTX 5070 in the Quake III Arena RTX Remix and Doom: The Dark Ages challenges.

• Gigabyte graphics cards are rumored to be set for a big price rise
• It could be a hike of 20% to 40%, and other rumors are also pointing to major price hikes for GPUs more broadly
• This means now is likely a good time to make a purchase, particularly with certain cards which have avoided the worst of the hikes so far

Desktop GPU prices are set to rise if the rumor mill is right – with yet another prediction to that end being aired – and while graphics cards are already costly in some cases, now might be the time to buy certain models, based on a fresh roundup of average pricing.

First off, let's deal with the latest gloom nugget from the grapevine regarding GPU pricing, courtesy of @momomo_us on X (via Wccftech) who picked up a notice from CFD (a Japanese components supplier) warning of substantial price hikes on Gigabyte graphics cards.

The claimed rise (add seasoning, of course) is in the order of 20% to 40%, so at the latter end of that scale, we're looking at prices shooting up by almost a half in some cases. Existing orders from retailers buying boards from CFD may even be cancelled, the notice states.

I perhaps wouldn't put as much stock in this, except that it echoes a rash of recent reports contending that large price hikes are about to happen in the GPU world, which makes it all the more believable.

Only yesterday I reported on the situation in South Korea where RTX 5000 models are rumored to be getting price increases of up to 30%. So again, that's a hefty hike, and it follows other speculation about Nvidia charging its partners more for video RAM (which will push up costs, of course).

This apparent Gigabyte move will affect both Nvidia and AMD graphics cards in Japan, and there have been rumors about Team Red having Radeon price hikes in the pipeline due to more expensive VRAM, too. That only makes sense, as if VRAM is getting more costly for Nvidia, it will be for AMD too. And while some of these reports are based on regional activity, the cost issues will surely apply globally – none of these markets exist in a bubble.

Analysis: I think it's time to buy – especially with certain GPUs

The RAM crisis has a lot to answer for, and it very much looks like graphics card prices are going to be jacked up considerably during the remainder of 2026, with video memory being the primary driver here. Given that, it seems that if you are thinking about a GPU upgrade, now is likely the time to go for it, rather than waiting and being hit by price hikes.

That's especially true if you're considering a 16GB graphics card (as more gamers are these days), as the products with more VRAM are going to get hit the hardest for price rises as memory modules become even more expensive.

If we want a snapshot of the current GPU market as price tags stand, fortunately enough, TechSpot has just furnished us with an extensive (multi-region) pricing roundup as of the end of July which highlights some key points.

Firstly, low-end GPUs are still relatively unscathed, with offerings like the RTX 5050 and RTX 5060 8GB, and the 8GB spin on the 5060 Ti, not having been bumped up by all that much in the RAM crisis thus far. However, as for the 16GB incarnation of the RTX 5060 Ti, that has seen major price bumps, reinforcing that observation on 16GB pain (this card is up over 40% since November 2025 in the US, for example – and shows a 14% rise since April 2026).

In the tier above that, the RTX 5070 is holding at a reasonable price, and even if the sub-MSRP levels of late last year are well and truly gone, it remains a decent buy. (As long as you can stomach 12GB of VRAM at this price bracket, of course – and that's one of the reasons why this GPU hasn't been hit so hard, no doubt).

Above this level, Nvidia GPUs have witnessed some of the nastier price increases, as you've probably noticed.

On the AMD side, matters look more promising, particularly in the case of the Radeon RX 9070 and RX 9070 XT, with TechSpot finding that the 16GB models in the RDNA 4 family haven't suffered as much as Nvidia's GeForce offerings. The price of both these 9070 models has remained steady over the last few months, and in many regions, they're only up 10% or so compared to late 2025. (The 9070 XT is an exception in the US, mind, where it has risen by closing on 20% since last year).

If you're inclined towards one of these Radeon mid-rangers, or Nvidia's RTX 5070, I'd strongly suggest you buy now rather than wait based on what we're hearing from the rumor mill. Lower-priced graphics cards are likely worth grabbing, too, because they may be heading upwards in the pricing stakes before long – although notably the RX 9060 XT with 16GB has already become a good deal more costly, much like its RTX 5060 Ti 16GB rival.

As noted, Nvidia's higher-end graphics cards (RTX 5070 Ti and upwards) have already been hit with alarming price hikes, but they're likely to get even worse. So, this could be a case of keeping your wallet shut and holding out for possibly a lengthy time, as I can't really recommend them even at the current pricing.

The trouble is, we don't know how long the RAM crisis will continue, and some of the guesswork on that front is very bleak indeed – suggesting it might be the rest of this decade effectively. At this point, I'm inclined to believe we're in it for the long haul with PC component pricing woes, which is why I think it's best to buy a GPU now, at least with the mentioned models, assuming you're in a position to do so.

• The latest Steam survey shows GPUs with 16GB are the most popular
• They're on 25.9% and have overtaken 8GB GPUs which are just behind on 25.32%
• There's been quite a shift towards 16GB boards over the past year, but with news of more price hikes incoming, upgrading to this loadout could be a painful process for gamers

Graphics cards with 16GB of memory are the most popular GPU among Steam gamers now, although the way GPU prices are going means that people aiming for a board packing plenty of video RAM (VRAM) are looking at a rather depressing future.

TweakTown spotted that the latest Steam hardware survey for July shows that 16GB of VRAM is the most prevalent memory configuration at 25.9%, up 1.4% over last month. That means it just dethrones the previous top of the VRAM table, which was 8GB, a loadout that slipped slightly from last month down to 25.32%.

There's not much in it, of course, but nonetheless, it's a telling stat (bearing in mind the usual caveats about variance in Valve's hardware report based on how the survey is distributed).

Of course, the majority of Steam gamers are still running with less than 16GB, and only 36% have a GPU with 16GB or more. So, almost two-thirds are running less than 16GB, and just under half (47%) have a graphics card with 8GB or less.

But it's a hefty rise in the count of 16GB GPUs when you consider that less than a year ago, in August 2025, just 6.8% of Steam gamers had boards with that VRAM loadout (based on a Notebookcheck report from the time). Compared to July 2026, that's nearly quadruple the number of 16GB-equipped rigs, with a close to 20% increase — pretty eye-opening.

Analysis: 16GB ambitions and the ever-worsening RAM crisis

It seems that, at least based on Steam's stats, RTX 5060 Ti 16GB and RX 9060 XT 16GB graphics cards (and 9070 models) must have been selling well — and of course higher-end Nvidia cards. The RTX 5070 is doing well for itself, too, and is now in third position in Steam's GPU ranking (although that's a 12GB offering and it should really be 16GB).

The ranks of the 16GB GPUs on Steam will also be bolstered by older models, of course, from past generations. Also, the visibility of AMD Radeon graphics cards with 16GB of VRAM was improved by Valve earlier this year, when an issue was fixed whereby these cards were misreported (not detected and lumped in a generic Radeon category), so that will have boosted numbers too.

At any rate, the fact that more gamers are looking at future-proofing their GPU purchases only makes sense, as 8GB in particular is looking too lean at this point. But there's a problem with GPUs that have heavier VRAM configurations, and this is, of course, that the memory crisis hits these models hardest.

We've already seen that GPU price hikes are making themselves felt, and just a couple of weeks back, Nvidia seemingly told its card-making partners that the price it charges for memory supplied with its graphics chips is going up (for both GDDR6 and GDDR7). A fresh report from Tom's Hardware also talks about price hikes for RTX 5000 models in South Korea beginning this month, driven not just by the increased cost of video RAM, but also the GPU chips themselves.

We're told the upshot is that Nvidia's Blackwell GPUs could see price increases of up to 30% in the country, and similar hikes may happen elsewhere. AMD price hikes are expected for its Radeon line-up, too, and Team Red has already warned us that the second half of 2026 is going to be a tough one for PC gamers.

With those gamers craving 16GB of video RAM more often, and prices heading inexorably upwards — and the memory crisis expected to worsen as this year rolls on, with the misery to continue into 2027 by all accounts — it's a gloomy picture for beefier graphics card upgrades.

Enterprise DevOps teams have adopted AI faster than they have adapted the operational systems that support it. Large language models generate Infrastructure as Code, deployment pipelines, and configuration files in seconds.

Development teams are delivering infrastructure changes at a pace that would have been implausible five years ago. Production environments have simultaneously become harder to reproduce, more difficult to govern, and more expensive to operate.

The problem is easy to miss at first, because AI looks like a clear win in the early stages. A team asks an AI tool to set up some infrastructure. It works. The team expands how much they rely on it. More teams follow. More infrastructure gets created for testing, demos, experiments, and development. The organization ends up with far more moving parts than it had before, but far less ability to track them.

Not the full picture

The tools generating this infrastructure are good at describing what something should look like at the moment it's created. They're not built to watch what happens to it afterward, decide whether it's safe to make another change while one is already in progress, or notice when something has quietly drifted out of its intended state over the following weeks.

That kind of oversight used to come from experienced operations people making changes one at a time, in a predictable order, with a clear sense of what else was happening around them. AI removed the slow part of that process without replacing the judgment that made it manageable.

Now there are multiple people and multiple AI tools all making changes to the same systems around the same time, each one doing its job correctly in isolation, none of them able to see the full picture of what's happening across the environment as a whole.

The result looks familiar to anyone running infrastructure today. Environments that worked perfectly last week suddenly don't, and nobody can say exactly why. Cloud costs creep up steadily because nobody remembered to shut something down once it was no longer needed. Teams spend real, billable hours figuring out why two environments that were supposed to be identical have quietly diverged.

Security settings that were correct at launch slowly fall out of sync as everything around them keeps evolving independently. None of these are dramatic failures on their own. They're slow leaks, and slow leaks are much harder to notice and much more expensive to fix than sudden breaks.

Too much fragmentation

The teams responsible for infrastructure have been adjusting to this reality in real time. A few years ago, their main job was making things easy for developers, such as removing friction, giving people self-service access to what they needed, getting out of the way. That's still part of the job, but the role has expanded considerably.

Now they're expected to enforce rules across the organization, manage costs on increasingly expensive compute, track what's actually running where, and keep things consistent across a far more complicated set of environments than existed even two or three years ago. Bolting on another automated tool to handle some piece of that doesn't solve the underlying issue.

More often than not, it just adds another piece to a pile that's already too fragmented to manage well.

An environment isn't just the infrastructure sitting inside it. It's also everything connected to that infrastructure: who has access to what, what depends on what else, how much it actually costs to keep running, and how long it's supposed to stick around before someone should be cleaning it up.

Getting ahead of problems means building that kind of awareness directly into how infrastructure gets deployed and maintained, rather than discovering issues after the fact through documentation nobody reads regularly or incidents nobody wanted to have in the first place.

Processes should be built in

This becomes even more important as companies pour money into AI-specific hardware. Setting up GPU-powered environments isn't simply a matter of securing the hardware, it requires everything around that hardware, including networking, storage, security, and software, to come together correctly and stay that way.

Having the hardware on hand doesn't make that coordination problem any smaller. Only better processes do, and that process has to be built deliberately rather than assembled from whatever tools happened to be lying around.

The questions worth asking have changed as a result, even if a lot of organizations haven't caught up to that yet. How fast a team can generate infrastructure isn't the most useful thing to measure anymore and it was never really the hard part.

A better gauge is whether environments stay consistent over time, how often things drift without anyone noticing, how long unused environments sit around quietly costing money, and whether anyone in the organization can actually describe with confidence what's running in production at any given moment.

The companies that get the most lasting value out of AI won't necessarily be the ones using the most advanced or most numerous tools.

They'll be the ones that put just as much disciplined effort into managing what AI produces as they did into producing it in the first place and treating governance not as an afterthought, but as the thing that determines whether all that speed actually turns into something worth having.

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Racing to scale AI requires massive capital investment in data centers, with McKinsey recently estimating that global data center spending could reach $7 trillion by 2030. Perhaps more importantly, though, scaling AI requires massive architectural investment from a technology standpoint.

Modern data center infrastructure was not designed to power the cloud computing revolution and the massive surge in AI usage and development on a global scale simultaneously.

This architectural strain is driven by rising expectations – the more AI is adopted, the more demand of it there is. Enterprises are excited by AI’s promise as a time-saver that can automate and streamline employee workflows and increase productivity. To facilitate the growing complexity of AI models and datasets, GPU density and bandwidth have increased in data centers.

These increases mean AI models that can be trained faster, have lower latency interference and more. As these chips and data centers continue to increase the amount of data transferred between chips, though, cooling efforts are also needed to make sure the chips and data centers do not overheat.

As a result, while today’s data centers continue to power the AI boom, their operational runway is quickly shortening. Powering AI and the growing demand for AI capabilities is incredibly resource intensive. The technology relies heavily on electrons for computational capabilities and for moving data between chips in larger AI models. This requires vast amounts of electricity, water, rare materials, and overall technological innovation.

As operational bottlenecks persist, silicon photonics has proven an underappreciated solution to help further advance AI. Using light combination with electrons, silicon photonics enables faster, more energy-efficient data transmission. These abilities can both advance AI and scale GPU clusters and AI data centers.

The SiPh adoption delay

Photonics have been around for decades, spanning more than 40 years of research and innovation. In the 1980s, researchers realized the technology's potential due to its ability to allow the integration of photonics and electronics on a single chip.

Where before, photonic and electronic components lived on two separate chips that had to be combined, silicon photonics made it possible for both components to be functional on one single chip, resulting in increased speed, bandwidth, energy efficiency and more.

Today, silicon photonics are integral to the commercial production of communications transceivers in data centers. Fiber-optic communication predates the current AI wave by an entire generation, as does the basic principle of transmitting data as light rather than an electrical current.

The challenge of leveraging this technology in a broader setting, however, has always been its manufacturability and the need for optical components to be small and cheap enough to compete with legacy conventional solutions.

Complementary metal-oxide-semiconductor (CMOS) compatibility makes this possible.

CMOS

Silicon Photonics-based circuits converts electrical signals into optical ones, transmits them as infrared light pulses through microscopic waveguides etched into silicon, and converts them back to electrical signals at the destination.

CMOS platform unlocks efficient and high-scale integration of passive and active devices onto Photonics Integrated Circuits (PICs), used to make optical transceivers. The physics of light propagation delivers far lower loss than copper interconnects do at the data rates modern AI workloads demand.

With CMOS, silicon photonics can be fabricated using the same processes semiconductor fabs use for conventional chips. This process allows optical and electronic components to coexist on a single silicon die, with limited use of rare materials or separate production lines, enabling scalable production.

Discussions of silicon photonics tend to focus on the transceiver or circuit design. While these conversations are essential, the substrate is equally important, delivering bandwidth and energy efficiency. Leveraging substrates to help meet growing data demands has been shown to decrease energy consumption by 30-50% on average.

Engineered substrates are silicon-on-insulator (SOI) platforms designed for photonic or high-frequency applications that support a range of markets, including mobile communications, edge AI, cloud AI, Internet of Things and data centers. With the latter, there are a variety of engineered substrates that improve different performance metrics, including Photonics-SOI, PD-SOI and RF-SOI.

Substrate selection is foundational to silicon photonics performance, not incidental to it. The choices made early in the design process determine whether a photonic chip delivers its theoretical efficiency and performance or falls short in deployment.

Scaling data centers requires scaling silicon photonics—now

The semiconductor industry must accelerate silicon photonics production to keep pace with the growth of demand for tomorrow’s data centers.

The technology works. The physics is proven. The manufacturing pathway exists. What's missing is widespread industry alignment and investment to produce silicon photonics at the pace AI infrastructure will require.

Coordinated investment in silicon photonics must happen across chipmakers, substrate suppliers, packaging specialists, and hyperscalers whose data centers will ultimately run on this technology. That coordination is currently happening in pockets, and it needs to be prioritized — the AI buildout is not slowing down.

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The AI race started off with a pretty clear direction – bigger and better. The first waves were characterized by building bigger models, but it’s all change in the world of artificial intelligence and with enterprises, SMBs and consumers all finding use cases for the technology, the focus has shifted.

Now, AI firms and model developers are looking to realize a much tougher goal. Efficiency. Cost per token, performance per watt, output per input, it’s all about driving maximum efficiency.

One clear divide is between training and inference. While training models still requires huge amounts of resources, inference efficiency is starting to improve, and one company (Rebellions) now believes an opening for inference-first hardware could create a new market.

The company’s racks are said to consume around 16-20kW, compared with around 120kW for leading GPU-based inference systems that, for many use cases, are sheer overkill.

Rebellions’ rack costs are also said to be around one-third of the price, making AI inference more accessible and helping enterprises to deploy AI more widely.

This hardware shift could be the start of truly efficient AI

Memory is also another battleground, whereby huge trillion-parameter models are testing the limits of today’s hardware and the intertwined reliance on memory and compute. Something Rebellions is looking to fix by working with memory and foundry partners to align multiple roadmaps, instead of having to respond to shifts in architecture.

Ultimately, today’s black-and-white chip manufacturing landscape is now evolving, and Rebellions sees two key changes happening simultaneously. Firstly, training and inference hardware is starting to differ more drastically. Secondly, aligning multiple hardware roadmaps across memory, compute and more will drive more efficiency not just across deployments, but in terms of bringing new products to market.

I spoke to Rebellions CEO Sunghyun Park allows me to understand how and why inference and training hardware are starting to separate, as well as the importance of open standards and collaboration in the drive for all-round efficiency.

• The AI chip market seems to be splitting between training-first and inference-first architectures. Why is that happening, and why now?

This split exists because training and inference are fundamentally different problems.

Training is how you build a model. It happens once, involves a small number of organizations, and rewards raw computational flexibility because the workload keeps shifting as research moves forward.

Inference is how you actually use a model: every query answered, every transaction processed, every decision an AI system makes in production. That happens billions of times a day across nearly every industry, and it’s where AI moves from R&D into revenue.

Those two jobs need different physics. Training requires maximum FLOPS. Inference requires efficiency, reliability, and economics that hold up when you’re serving users at scale.

The industry forced a training chip into that second job because that’s what existed. Now that inference has become the larger, more urgent market, that compromise no longer holds. Enterprises and governments are asking how fast they can deploy. That’s why the conversation is splitting now.

• Where does Rebellions fit in that split, and what makes your approach to AI inference fundamentally different from your competitors?

We built for inference, from day one. Most first-generation AI chip companies emerged from the 2016-2017 training boom and adapted their architectures for inference afterward.

We started in 2020 – after that wave – with inference as the only target, which meant designing around what production AI actually needs instead of retrofitting a training chip.

The numbers reflect that choice. Our racks draw 16-20kW versus roughly 120kW for leading GPU-based inference systems, about a sixth of the power, in a market where power is the binding constraint for most operators.

Acquisition cost runs around $10 million per rack versus roughly $30 million, about a third of the cost. Our chiplet-based architecture also scales out rather than betting that a single device can handle a model’s full size, which matters now that production workloads are trillion-parameter mixture-of-experts models instead of the few-hundred-million-parameter models the first generation was built around.

It’s also why our architecture is memory-centric rather than compute-centric. The chiplet approach exists to keep memory close to logic as models scale, not just to add cores.

And we have three years of production deployments behind that architecture, not pilots. That’s the hardest part to replicate: real workloads, running at scale, today.

• A year ago, everyone in AI was talking about chiplets. Now the conversation has shifted to memory. What changed?

The chiplet conversation was about architecture: breaking a chip into modular pieces that scale independently, rather than betting everything on a single monolithic die.

That mattered because it let the industry move past an assumption the first generation of accelerators made in 2016 and 2017, that a single device would always be big enough to run any model.

That assumption broke once mixture-of-experts and trillion-parameter models arrived.

The memory conversation is the layer underneath that. Once the architecture problem is solved, the constraint becomes physical: can you actually get enough high-bandwidth memory (HBM) to build what you’ve designed?

HBM is 3D-stacked memory, and how closely you can physically stack it to compute is as much of a bottleneck as raw supply.

Every AI accelerator company is competing for the same limited supply right now, and demand has outpaced what memory makers can produce. That’s the memory-logic co-design problem: architecture and memory supply are no longer separable decisions.

We’re in a different position because our investor relationships were built around supply, not just capital. Our memory partners are also investors, and we co-design our memory architecture directly against their roadmaps rather than simply purchasing off them.

Our chiplet architecture also develops against our foundry partner’s process roadmap. When the rest of the industry was fighting for allocation, we already had a seat at the design table through those relationships.

That’s memory-logic co-design, not just secured supply. The shift from chiplets to memory tracks has moved the real constraint: from architecture to physical supply.

• Your stack runs on open-source frameworks like vLLM, PyTorch, Kubernetes, and OpenShift, tools many enterprises already use. Does that make adoption relatively plug-and-play, or is that an oversimplification?

It’s mostly true, but ‘plug-and-play’ undersells how deliberate that was, and oversimplifies in one specific way.

We built entirely on open standards: vLLM, PyTorch, Kubernetes, and Red Hat OpenShift. We’re one of only two chip companies in the PyTorch Foundation, and the only AI accelerator company fully integrated with OpenShift.

A developer who already knows how to run inference on existing infrastructure already knows how to run it on ours. There’s no proprietary runtime to learn and no migration project. That part really is close to plug-and-play.

The first generation of AI chip companies each built proprietary software stacks, and hundreds of millions of dollars went into software that didn’t survive. We came to market once the open source ecosystem had matured and chose to build on it instead of forking it.

Where it oversimplifies is assuming that means zero integration work. Production deployments still require validating performance at your specific workload and scale, and that takes real engineering time, no matter how compatible the stack is.

What open standards remove is lock-in risk and retraining cost, not the deployment work itself.

• What are the biggest challenges organizations face when trying to run AI inference outside of hyperscaler platforms?

Most organizations aren’t built like hyperscalers, and much of the available inference infrastructure assumes they are.

The first challenge is physical. Most enterprises, telcos, and governments already have data centers. They can’t wait two to three years or spend $600 million-plus on new ones, and they can’t retrofit for liquid cooling without major cost and disruption.

So the practical question is whether inference hardware runs on what they already have: standard racks, air cooling, and existing power budgets.

The second is sovereignty. Organizations increasingly want to bring compute to where their data already lives, rather than move sensitive data to wherever compute is hosted.

That’s partly regulatory, partly operational, but either way, cloud-only inference creates a dependency a lot of operators are no longer comfortable with.

We built specifically for that gap. Our systems run at 4-5kW per server on standard air-cooled infrastructure, no facility redesign required.

SK Telecom has run on our hardware for nearly three years, scaling from a small cluster to close to 100 racks and now processing 50 million API transactions a day, entirely inside their existing network.

KT Cloud runs real-time inference on highway CCTV systems nationally. Both show you don’t need hyperscaler-scale infrastructure to run AI at hyperscaler-relevant volume.

• Cerebras' IPO, and potential listings from others in the space, have put AI infrastructure in the spotlight. What do these IPOs signal about the market, and where might the hype be outpacing deployment reality?

It tells the public markets that AI infrastructure is a durable, investable asset class, not just a venture-backed bet. That validation benefits the whole sector, including us: it says purpose-built AI silicon is real, differentiated from general-purpose GPUs, and worth independent capital.

Capital flowing into the sector is necessary, but it doesn’t by itself determine who wins. The companies that define the next decade of this market won’t necessarily be the most-funded ones.

They’ll be the ones with real fundamentals: production customers, deployment scale, proven economics, durable supply chain relationships. That’s a different filter than fundraising size, and it’s the one that matters once public market scrutiny starts.

We’ve been building toward that filter since 2020, with production deployments and supply relationships with our foundry and memory partners that we secured before the rest of the industry was fighting over the same allocation. The capital is a tailwind for everyone serious about inference.

Whether it gets deployed well is a separate question, and one the market will answer over the next few years.

• Looking ahead, what trends are you watching most closely across AI infrastructure and inference over the next 12–24 months?

The buildout happening inside existing infrastructure keeps accelerating. Most enterprises, telcos, and governments aren’t waiting for new data centers.

They’re deploying inference into facilities they already have, and I expect that to become the bigger story even though it gets less attention than hyperscaler headlines.

Additionally, memory remains the physical constraint. HBM supply hasn’t caught up with demand, and as models keep moving toward trillion-parameter mixture-of-experts architectures, that pressure increases rather than eases.

Companies with secured, strategic supply relationships will have a real advantage over the next two years, not just a cost one.

Chiplets are how we get there. We’ve already mass-produced a highly advanced 4-chiplet package – a level of integration Nvidia has struggled to reach.

Reporting this year indicated Nvidia had built and demonstrated a four-chiplet Rubin Ultra design, then canceled it in favor of a dual-die architecture over manufacturability concerns: a four-die single package pushes roughly 7.5-8x past reticle limits on yield and cost. That’s the foundation.

The next layer we’re building on is performance optimization of HMB3E (3D-stacked memory) in close collaboration with memory and compute co-designed together from the start, not bolted on after the fact.

Also on my radar is the fact that as more companies in this space go public, capital will get valued against production fundamentals rather than funding rounds.

And the efficiency point matters: as inference gets cheaper per token, demand doesn’t shrink, it expands, because new use cases become viable at lower cost. That’s been true of every computing platform in history, and I don’t expect AI inference to be the exception.

• Nvidia's Olympus core architecture prioritizes single-thread IPC over frequency, using a 10-wide decode front end, deep out-of-order mid-core, and a graph prefetcher tuned for agentic AI's branch-heavy, pointer-heavy workloads
• Vera trades chiplet-style core density for a monolithic 88-core die and single-NUMA-per-socket design, a deliberate bet on agentic AI workloads that Nvidia's own engineers admit comes at the expense of legacy workload performance
• Self-reported SPEC CPU 2026 results by Nvidia paint a per-core advantage figure of anywhere between 70% and 80% versus AMD's EPYC 9755 server CPU

Nvidia's Vera CPU has a lot to prove, representing the company's first real attempt at the AI server CPU market, where traditional vendors Intel and AMD, along with third-party Arm-based providers, are all gunning for a piece of an increasingly lucrative data center pie.

To that end, Nvidia has published its most detailed technical account yet of the chip, promising substantial performance gains over the competition. Vera is the company's first server processor built around a fully in-house core design, a departure from the stock Arm cores that powered its Grace predecessor.

As Vera heads toward general availability in the second half of 2026, Nvidia is making its case on a specific front: not raw core count, but sustained per-core performance under load, the metric it argues matters most for agentic AI.

What's actually under the hood of Nvidia's Vera CPU?

At the core of every Vera CPU is Olympus, Nvidia's first custom server core, built to the Armv9.2 instruction set but designed in-house rather than derived from Arm's stock Neoverse designs, as its predecessor, Grace, was.

Nvidia's second-generation data center CPU is a completely reworked design built around a single goal: to lead in agentic workload performance.

Rather than following Intel and AMD down the chiplet route, Nvidia packs all 88 cores (176 threads) onto a single monolithic die, with a dual-socket configuration delivering 176 cores and 352 threads in one system.

Nvidia has detailed the core design extensively, and several publications have since dug into the microarchitecture. The front end runs a 10-wide decode engine paired with a neural branch predictor that can resolve up to two taken branches per cycle, designed to handle the large instruction footprints and irregular control flow of interpreters, compilers, and agent runtimes.

The mid core combines a wide rename-and-allocation engine with a large reorder buffer and dependency-breaking techniques including memory renaming and value prediction.

The execution engine schedules dynamically across integer, vector, floating-point, cryptographic, load, and store resources, while the cache subsystem adds a graph prefetcher targeting the pointer-chasing access patterns that defeat conventional streaming prefetchers.

That design has not gone unanswered. AMD has countered with estimated figures for its 256-core Zen 6 "Venice" part, claiming a 3.3x rack-level advantage over Vera, though those numbers are extrapolated rather than measured.

AMD responding first isn't surprising. Within x86, it continues taking ground from Intel, reaching 33.2% of x86 server CPU shipments in Q1 2026 per Mercury Research, up from 27.2% a year prior.

With Arm reporting that its architecture now accounts for roughly 50% of CPU compute among top hyperscalers, competition is building from several directions at once. Vera may well set a new bar for agentic AI.

• A recent report suggests Nvidia has informed its partners of GDDR6 and GDDR7 memory kit price increases
• This means retailers may increase the prices for RTX GPUs using GDDR6 and GDDR7 memory
• The potential price hike and one user's dual-GPU setup both highlight the importance of hardware preservation

The RAM crisis continues to impact PC manufacturers, with rising memory prices and shortages — ultimately leading to increased retail prices for consumers — and the trend may be set to continue with Nvidia.

According to a report on Benchlife, Nvidia is rumored to be planning an increase for prices of its GDDR6 and GDDR7 memory kits for RTX GPUs, and has informed AIB (Add-In Board) partners such as Asus, MSI, and Gigabyte, who make their own versions of Nvidia's GPUs. Unsurprisingly, the price increase for these memory kits is cited as a result of the ongoing RAM crisis.

While RTX GPU price hikes aren't guaranteed for consumers, because this will directly impact AIB partners who manufacture the GPUs, eventual retailer price increases are unfortunately likely. It's a trend we've seen with recent PC hardware.

Notably, Valve's Steam Machine was initially supposed to cost less than its eventual $1,049 / £879 / AU$1,609 price tag, which could now also be at risk of a price increase, based on hints from a Steam Machine engineer (as reported by Bloomberg). New handheld gaming PCs are also now often launching for more than $1,000, and the price of RAM and storage certainly plays a part.

Consumer struggles

The RAM crisis is a sad state of affairs for consumers and underlines the importance of PC hardware preservation. GPUs aren't cheap by any means, especially with recent suggestions that AMD would also be increasing prices of memory kits for its own AIB partners.

We've also recently seen the return of the RTX 3060, due to Nvidia's attempt to help consumers with more options amid a rough PC hardware market. However, if these price hike reports are legitimate, the RTX 3060 will likely be subject to higher prices at retailers.

That's exactly why GPU setups like one Reddit user's RTX 3090 and RTX 3050 dual-GPU rig matter now more than ever. In this case, the RTX 3050 is responsible for allowing frame generation via Lossless Scaling to function.

A game then utilizes the RTX 3090's power for high-quality visuals and performance, with the two GPUs teaming up. The user states it resulted in 144fps at a 4K resolution over the initial 71fps without frame generation.

Frankly, a dual-GPU setup isn't exactly common, nor is it actually recommended for gaming, since modern games are only designed for single-GPU rendering (even though the frame generation use case is noteworthy).

Users would also need a beefier power supply, depending on the GPUs used, and likely a new motherboard with another compatible PCIe lane.

However, what I feel is more important here is that's it's a great example of why hardware preservation is so important, serving as backup in case GPUs melt or end up faulty, or for experiments like the one I've outlined above. With no end of the RAM crisis in sight, repurposing older hardware might be one way through this.

• Nvidia again showed off DLSS 5 at its Siggraph 2026 keynote
• Team Green made it clear that developers will have a level of control, with a choice of three different AI models
• There will be two sliders to play with, and masks to keep DLSS 5 from interfering with game assets where it's not wanted – but many gamers think devs should have much more control than this

Nvidia has given us another showing of DLSS 5, the incoming tech for its GeForce GPUs which received a very stormy reception when first revealed at GTC 2026 – and unsurprisingly, changes have been made in the past few months.

Tom's Hardware reports that Nvidia revealed more about how DLSS 5 will be implemented at Siggraph 2026 over in Los Angeles, where the tech was the foremost topic in Team Green's keynote.

Nvidia was keen to point out how it has changed the neural rendering model in some key ways since DLSS 5 was first announced. Of course, it's obvious that a lot of this is about combating the idea that the tech is 'AI slop' which makes games look worse rather than better, and characters (particularly faces) look bad and have that undesirable 'uncanny valley' effect. And indeed the criticism that DLSS 5 is messing with the art direction and overall feel of a game's graphics.

On the latter point, back at the original reveal, Nvidia stressed that it would give game developers control over the end result with DLSS 5, and now we're seeing how this will work. Team Green has brought in three different AI models which offer varying levels of detail (and impact on performance), so devs can choose between three different filters, essentially.

This isn't a game-wide choice, though, as the developer can apply any of those models to any different scene, mixing and matching as needed. There are also two sliders to adjust the 'structure' and 'tone' intensity of the effects.

On top of that, the developer can mask off any game asset where they don't want DLSS 5 to be applied. That's good news as it means that, for example, character's heads (and faces) could be marked as out-of-bounds, and therefore not subject to Nvidia's tech. The developer could just get DLSS 5 to enhance background objects and details, and maybe only in a relatively minor way (by toning down those intensity sliders).

To clarify, DLSS 5 is essentially an AI filter that sits atop the existing DLSS suite of technology. Underneath that, you still have upscaling (and frame generation), and Nvidia applies what's essentially a photorealism filter on top of that in the form of this new neural rendering model.

If you want to grab some popcorn, Nvidia's full Siggraph presentation is available to watch on YouTube, check it out below. All the DLSS 5 stuff is at the start (well, past the usual preamble).

Analysis: a step forward, but the doubters remain

Has Nvidia managed to placate the DLSS 5 haters, then? No, in a word. Did you really think it would?

As one Redditor put it: "I had so much hope for DLSS in the early days, what a dumpster fire it's turned into."

Another observed: "So just confirming what anyone with a basic understanding of AI already knew. Glorified Instagram filter."

To be fair, in general the reaction has been somewhat more positive compared to the original reveal, and clearly it's a good thing that game developers can at least keep 'yassified' faces out of the mix with characters. Clearly, there is a level of control here, but despite Nvidia treading more carefully in this respect, there's still a feeling that it isn't doing enough.

A choice of three AI models and a pair of intensity sliders is relatively limited, really, and a far cry from anything like a full level of control over how DLSS 5 imposes itself on a game. Although admittedly there's still some way to go before DLSS 5 comes out – it'll supposedly arrive later this year, in the fall (so theoretically any time from September through to November) – and Nvidia could put in additional controls before the launch.

Aside from disappointment around the level of control Nvidia is giving devs, the other beef being raised is how much of a performance hit this AI filter might be burdened with. When Nvidia first demoed DLSS 5, it was running on a pair of RTX 5090 graphics cards, and in this more recent outing, there's no mention of the hardware involved.

It's fairly easy to guess that the GPU demands haven't been meaningfully reduced yet, otherwise Nvidia would likely be shouting about that – and this is being read as a worry by some. I get where they're coming from, although Nvidia has clarified that the tech will run on a single graphics card (it'll have to, of course), adding at Siggraph that DLSS 5 is "VRAM efficient" (a nicely vague assertion).

I'd expect it probably won't run on anything less than an RTX 5080 to begin with, or indeed an RTX 5090. And the other fear is that the initial incarnation could be quite rough in terms of artifacts and the like, and that's a danger with the first take on any tech. Just look at the original DLSS (version 1), which suffered from blurriness and glitches, before they were polished out and it became a seriously impressive upscaler. Perhaps the same will be true of DLSS 5, but there's a long road ahead yet, and notably, a lot more doubts and question marks here.

• Nvidia's RTX 5000 Super models are being readied, and board makers have the chips, a new rumor reckons
• However, these graphics cards are 'on hold' due to the high price of VRAM
• The Super refreshes pack a lot of VRAM, and so that cost could have an unfortunate impact on MSRPs – and none of this sounds particularly comforting for would-be buyers

Nvidia's RTX 5000 Super refreshes are apparently being readied, but they're on hold for now – and the reason why makes me even more nervous about the pricing of these GPUs when (well, if) they finally emerge.

Of course, these RTX Super models are still rumors – albeit so persistent at this point, there must be at least some validity to them – and the latest assertion comes from VideoCardz.

The tech site has apparently spoken to a source at one of Nvidia's board-making partners who claims that their company has already received 'graphics cards', which presumably refers to GPU chips (which is what Nvidia supplies, along with VRAM, to partners that make the boards).

At any rate, while this board maker supposedly has at least some of the necessary chips, it has been told by Nvidia that the RTX 5000 Super refreshes are now 'on hold'.

Why? Apparently, the delay is down to the price of the video RAM, specifically the new 3GB GDDR7 modules that are required (as per previous rumors) for the refreshed graphics cards.

Compared to the 2GB GDDR7 modules currently in use with Nvidia RTX 5000 models, the 3GB memory chips are around three times more expensive right now. Ouch.

Analysis: Super stumble?

This presents an obvious stumbling block because, due to these Super variants being packed with VRAM, the total cost of materials gets expensive quite quickly.

Remember that the grapevine believes that the RTX 5080 Super and RTX 5070 Ti Super will bristle with 24GB of VRAM, and even the RTX 5070 Super will have a weighty 18GB on board. Gamers are going to love the latter as an upgrade to the lean 12GB of the vanilla version – but not if it pushes the price of the GPU through the roof.

Going by what VideoCardz tells us about pricing in US dollars as the market currently stands, in the case of the RTX 5070 Super, the greater quantity of RAM, and more expensive modules, would mean 18GB costs in the order of $360 (maybe a bit more), versus $120 for the current RTX 5070's 12GB loadout, whacking an extra $240 on the price (or the regional equivalent elsewhere).

These graphics cards were already expected to be pricey, but that kind of additional premium lumped on top, this could make them real wallet-worriers.

The good news for those hoping for new GPUs from Nvidia is that at least these RTX 5000 Super models do seem to be happening. It's just that we may have to wait some time for them yet – although another recent rumor did suggest they were due this year. The latter speculation also theorized that an RTX 5060 Super 12GB model was incoming, but that didn't make a lot of sense, and I note that it isn't mentioned in this latest rumor. As ever, don't go lightly with the seasoning on all this.

• Palit has launched a new RTX 3060 model with 12GB of VRAM
• This continues a recent theme of old GPUs being resurrected
• These GPUs may pack 12GB of video RAM at a relatively affordable price, but they don't make sense compared to current-gen pricing

We might not have seen any new graphics cards so far from Nvidia this year — and no RTX 5000 Super refreshes are in sight (although there are rumors again) — but we're continuing to witness the resurrection of some old GeForce GPUs, with Palit officially revealing a new RTX 3060.

That popular workhorse of a gaming GPU has already made something of a comeback, returning to the shelves of retailers like Newegg as an alternative budget graphics card in a RAM-starved climate where prices of many of these current-gen boards are being hiked up (alongside other PC components, of course).

As flagged by VideoCardz, Palit has trumpeted the launch of its new GeForce RTX 3060 Infinity 2 OC graphics card as "the return of a classic", which is certainly one way of putting it.

Although to be fair, even today, the RTX 3060 remains a very popular GPU, ranking second on the Steam hardware survey for June 2026 (behind only the RTX 4060 laptop GPU in a rather bizarre twist I won't get into here).

Palit observes: "Combining a massive 12GB GDDR6 memory capacity with a clean, all-black dual-fan shroud, the Infinity 2 delivers honest, dependable performance without the premium price tag."

Analysis: let's hope for a Palit-able price tag, but so far, RTX 3060 pricing has been out of whack

The key selling point for these RTX 3060 models is that they're loaded with 12GB of video RAM (VRAM), as Palit emphasizes with this new Infinity 2 OC model. Where this is side-stepping the RAM crisis (to some extent) is that these past-gen Nvidia cards use GDDR6 VRAM, a different memory supply to tap than the GDDR7 used in RTX 5000 graphics cards (except the RTX 5050, which goes with GDDR6, but that's the outlier here).

This means Nvidia can get its partners to produce graphics cards that have 12GB of GDDR6 VRAM (a loadout a good few gamers put a priority on securing), but sit at the cheaper end of the market. Due to cost issues, these days it's a lot less tenable to sell lower-end GPUs which stack up the GDDR7 VRAM (like the RTX 5060 Ti with 16GB).

It's sad that the RAM crisis has forced us to this point, but that's the reality of the GPU world at the moment. What's also problematic for me here is that the RTX 3060 might have 12GB of VRAM at a relatively affordable price point, but looking at current pricing in the US as an example, it's not meaningfully cheaper than the RTX 5060 with 8GB.

Yes, you're getting a lean RAM configuration with the latter current-gen graphics card, but it's still a much faster GPU — one that benefits from DLSS 4, too — and only just over 5% more expensive (at the time of writing). At this kind of price differential, the RTX 3060 12GB doesn't make a lot of sense.

If these RTX 3060 models were a lot cheaper, then there might be more of an argument. To be fair to Palit, we don't have a price tag on this new offering yet, but pricing needs to be a lot more compelling than what I'm seeing right now (especially in the UK, where the RTX 5060 8GB is actually cheaper than the 3060 at some retailers, although there isn't much stock of the latter).

• A Reddit user's Zotac GeForce RTX 5090 GPU reportedly exploded during gameplay
• The power connector wasn't damaged, but a visible burn mark was present on the PCB gold finger
• It's yet another point of concern for RTX 5090 users

Nvidia's RTX 5090 has been the subject of controversy ever since its launch, with several well-publicised cases of melting power connectors causing malfunctions — and this time, it's arguably the worst case yet.

As reported by Wccftech, a Reddit user's Zotac RTX 5090 GPU has exploded, 'five minutes' into playing the tutorial for Assassin's Creed Black Flag Resynced. Interestingly, the 16-pin power connector (often the case of the RTX 5090's woes) wasn't damaged. Instead, the user claims they heard a loud pop and crackle, followed by a cloud of smoke.

The 16-pin power connector has been blamed for several RTX 5090s melting, either due to high temperatures or connectors that aren't properly seated. It also shouldn't be a surprise that these melting complications are effectively exclusive to Nvidia's flagship GPU, since it draws a significant amount of power (575W maximum).

Instead, the PCB gold finger (the part that goes into the motherboard's PCIe slot) on the user's Zotac RTX 5090 has a visible burn mark. The cause isn't exactly clear, but speculation suggests that a crack in the circuit board may have caused the GPU to short-circuit.

Perhaps the most concerning aspect of this case is the suggestion from fellow Reddit users that the supposed crack and short circuit may be the result of GPU sag — and if that's the case, it should be a major concern for high-powered GPU users.

Plenty of modern GPUs, notably from third-party Nvidia partners, are quite chunky, which leaves little clearance to fit in smaller cases and, most importantly, not enough space to use GPU sag brackets.

Fortunately, the RTX 5080, RTX 4080, and RTX 4080 Super (which is the GPU I use) aren't cards that draw nearly as much power as the RTX 5090, so there isn't too much concern in terms of power connectors melting. However, if GPU sag can lead to a short circuit, it raises concern for any GPU that's heavier than most, especially the high-powered RTX 5090, as it's an additional issue to be wary of alongside melting connectors.

A melting or exploding GPU in this current PC hardware market is possibly the worst-case scenario, particularly for RTX 5090 users (who are seemingly most at risk), as prices are completely out of the affordable range due to the ongoing RAM crisis.

Frankly, it makes me want to steer clear of high-wattage GPUs completely (or at least undervolt them). TechRadar has contacted Nvidia for a response.

IDC projects that AI infrastructure costs at Global 1000 companies will run 30% higher than current budgets by 2027. That gap shows a mismatch between how AI workloads behave in production and how enterprise IT has historically planned for capacity.

The pattern repeats across industries. A pilot project validates an AI model on a controlled dataset, and budgets are created around those economics. When the system moves into production, the bill often outpaces what anyone originally modeled.

The natural instinct is to blame the size of the model or the cost of using tokens, but that’s not where the money goes. The cost lives in the data layer, driven by how often the system reads, how many services it touches, and how continuously those operations run.

What pilots don’t show you

A pilot runs against a narrow dataset, with a handful of concurrent users, on a request-response cadence familiar to anyone who has shipped a web application. Production looks nothing like that.

Consider a generative AI customer support agent in production. A single user prompt can trigger simultaneous lookups across session activity, CRM records, inventory systems, retrieved manuals, and other sources before the model produces a response. All of this happens under sub-100ms latency budgets, with the slowest lookup gating the rest. The operational problem becomes tail latency across many small parallel lookups.

Now layer agentic workflows on top. A user request decomposes into a plan, then into a series of steps that each issue their own lookups, write intermediate state, and read it back. What starts as one inference expands into tens or hundreds of data accesses, with the system holding session and memory state across the entire arc. The cost profile that emerges is nothing like what the pilot priced.

Where the 30% comes from

The overrun comes from a series of defensive choices made under uncertainty. When teams can’t see how data flows through a single request, they over-provision to absorb spikes. When they can’t predict cache behavior under shifting context, they duplicate data across systems to reduce dependency risk.

When one downstream service slows down, they layer another service on top to insulate against it. Each choice is locally rational. The aggregate is a system that costs 30% more than the workload requires, and that’s before anyone adds a new use case.

The underlying problems are usually the same. Fan-out per request goes unmeasured end-to-end. Context gets fragmented across feature stores, session stores, user profile systems, vector indexes, and third-party APIs. KV cache and prefix reuse get left on the table because the inference layer can’t hold or share state across calls.

Replication and tiering decisions get made per system rather than per access pattern. None of these show up in a pilot. All of them show up in the production bill.

What the AI data tier has to deliver

AI in production is a continuous, distributed system whose hot path is context assembly — many small reads per request under tight latency budgets — combined with writes that must keep multiple representations of the same entity consistent.

These systems need two things at the same time: predictable low-latency reads under high concurrency and consistent writes across the data path. The infrastructure underneath has to be sized and shaped accordingly.

A few architectural decisions end up driving most of the outcome:

Match the data tier to the access pattern Session state, agent memory, feature lookups, retrieved context, and KV cache reuse all have different read patterns, freshness requirements, and durability needs.

Treating them as different data tiers — or laying them on whatever database happens to be in the stack — is the most common source of overrun. The session store and the system of record have different access pattern demands from the same data tier.

Engineer for fan-out and predictable tail latency Throughput is the wrong primary metric for an AI data tier. The right one is the predictability of many small reads triggered by one request. A batch of parallel lookups is only as fast as its slowest member, and a single slow lookup stalls the entire context-assembly step.

Storage systems optimized for write throughput pay a read amplification penalty under this access pattern. Systems that keep the primary index in memory and resolve point lookups in a single I/O behave differently at the tail.

Treat write consistency as a correctness requirement When updates across user profiles, embeddings, feature vectors, and session state aren’t synchronized, downstream context assembly reads a mix of versions and the model produces confident output grounded in contradictory data.

These are hallucinations that have nothing to do with sampling or model probability, and they don’t yield to better prompts or bigger models.

Treat inference-time data reuse as infrastructure. KV cache reuse, prefix sharing, and agent memory persistence are first-class infrastructure concerns. Teams that figure this out early run the same workloads at lower GPU utilization than teams that haven’t. This is the largest leverage point that doesn’t appear in most AI cost models.

Where to start

The most useful first step is to trace a single production request end-to-end — counting lookups, logging sources, and measuring tail latencies. That exercise reveals more than any architectural review. Once teams can see how data moves through one interaction, they can categorize data accesses by tier and verify that each is running on infrastructure suited to its pattern.

From there, the next question is what’s being recomputed that could be reused, particularly across inference calls and agentic steps. Fan-out per interaction should become a metric teams watch as closely as p99 latency — because at scale, it drives cost just as directly.

AI cost in production is a design discipline. Teams that address it early have far more control over performance and spend than teams that wait until the bill forces the issue. In many cases, the 30% gap is the cost of learning these lessons too late.

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The PC gaming landscape has changed dramatically in the last handful of years. We've seen the slow and steady move away from native (purely rasterized) performance and onto the crutch of AI-powered upscaling technology. Whether Nvidia DLSS, Intel XeSS, or FSR 4, Multi-Frame Generation, or "fake frames", have become a core part of the experience.

It doesn't really matter how powerful the best graphics cards are anymore, as AI-powered upscaling has shifted the playable performance expectations across the board. We see this as standard in the system requirements for today's demanding PC games; it's a huge asterisk that developers use to claim otherwise unheard of FPS in intensive software.

Are the likes of Nvidia DLSS, Intel XeSS, and AMD FSR just a failsafe to make up for poor software optimization? That's part of the story, sure, but it's far more nuanced than that. As computing components become more expensive, and AI muscles its way into the territory in a more aggressive manner, the two, which used to go hand in hand, have now become inherently parasitic in a wanton race to the bottom if things are not course-corrected. Here's what AI-powered upscaling means for the future of PC gaming.

Nvidia DLSS 5 is the first symptom of a wider issue

I've been a champion of DLSS for many years, primarily for how it can boost weaker graphics cards to give users playable framerates. It's an ever-evolving AI-powered tech that's continuing to improve and deepen. Some of its best features include Ray Reconstruction, which makes Path Tracing more viable, Frame Generation/MFG, and DLAA for smoother anti-aliasing. When used as an assist to your hardware, it can be the difference between smooth and stuttering, but DLSS 5 is where things simply went too far.

Instead of being a supporting tool, as with DLSS 4.5's Dynamic MFG, the only thing people can take away from DLSS 5 is how AI is actively impacting image quality, and not for good reasons. Described as a "breakthrough in visual fidelity for games", and said to bridge the "cinematic gap", this upcoming AI model uses an algorithm to re-color and overlay motion vectors.

DLSS 5's showcase of results is troubling to say the least. At best, it slightly improves the lighting in EA FC, and at worst, it completely overwrites the distinct visual art style of PC games like Starfield, Resident Evil Requiem, and Hogwarts Legacy. Sure, the lighting is a little better, but it comes at the cost of a flat and artificial-looking brightness of the entire scene, making everything (ironically) look far more lifeless and void of personality.

Nvidia tends to be the frontrunner that AMD and Intel later catch up to. With the DLSS 5 release date still unconfirmed, but claimed to be coming in the autumn of 2026, we (likely) won't see the full ramifications of this on the wider gaming industry until next year, but when this glorified AI-filter drops, it's likely to become an ingrained option in many flagship titles. There's a reason why Team Green started by showing off some of the largest games from the most well-known publishers and developers in the business; if you enforce it at the top, the rest will follow for fear of being left behind.

The frightening reality of how expensive new graphics cards could be

It's no exaggeration to say that 2026 is one of the worst times on record to build a custom gaming PC. DDR4 and DDR5 RAM prices have skyrocketed due to the global supply of memory modules being drained en masse to build data centers, and the less said about what's happened to flash memory found in the best SSDs, the better. While these individual components doubling in price overnight is already troubling, there are even worse consequences for graphics cards, made all the more infuriating by the fact that it's a self-destructive cycle with (seemingly) no end in sight.

Graphics cards rely on VRAM to have enough bandwidth to perform properly. For Nvidia's current-generation RTX 50 series, that's the superfast, denser GDDR7, whereas AMD and Intel are still using the slower, older GDDR6 standard. As memory modules are becoming scarcer, it massively drives up the price for core components, such as the Samsung, Micron, and SK hynix memory modules needed to build the video cards in the first place.

Put simply, graphics cards will become more expensive because manufacturers are too busy building data centers with the components, meaning the end consumer ends up paying considerably more. We've already seen countless examples of this, such as the Steam Machine's overpriced nature, the Steam Deck's price increase, and even how it's made the PS5 and Xbox Series X more expensive six years in than at launch.

We've seen prices of graphics cards increase massively since the semiconductor shortage, which plagued the RTX 30 series launch. You paid more, you got less, and now the manufacturers know they can overcharge you. In a computing landscape where components can shoot up anywhere from 20 to 50% overnight, it puts the reinforced focus on DLSS, XeSS, and FSR as a necessity rather than an optional helping hand.

The future of AI-powered upscaling is a necessity

While we're heading towards more expensive graphics cards that rely on AI upscaling tech just to keep up, we can also look at what the future of DLSS, XeSS, and FSR will need to do to keep up. We've already established that DLSS 5 made a contentious call with its AI art filter, but what about real-world, practical uses in 2027 and beyond?

Intel XeSS 3 launched with Multi-Frame Generation, which was rolled out to both its Alchemist and Battlemage graphics cards, even bringing true MFG to handhelds like the MSI Claw 8 EX AI+ and Acer Predator Atlas 8. To look positively at AI-upscaling tech in this regard, its major benefit will be to make handheld gaming PCs, such as a Steam Deck or Lenovo Legion Go S successor, more competitive.

The most recent update to AMD's AI-powered upscaling tech, FSR 4 Redstone, still trails behind DLSS 4.5, which means Team Red will need to strike back with FSR 5 to have a chance at dethroning Nvidia while it is down. AMD's image quality is far better than it used to be, even though its frame pacing leaves a lot to be desired. Based on the track record, from what I've seen from Intel and AMD, XeSS and FSR look to continue to iterate on the core fundamental technologies, whereas Nvidia is looking to do its own thing in counter to what was expected.

The future of AI upscaling tech becomes two-fold. Ideally, enabling any of these settings should be as normal and natural as turning on TAA (temporal anti-aliasing) and forgetting about it. It's easy to forget that there was a time when such a setting was contentious, and now it's almost universally used across the board. It's a similar story to when Nvidia's PhysX SDK was pushed so heavily, as it's now a default setting that's enabled within game engines as standard.

So, AI-powered upscaling is best when it's not noticeable. If you're playing a game and you're noticing smooth performance and a high average FPS, then it's doing its job properly. Problems only really arise when that tech tries to overtake the core experience rather than in support of it, and chiefly as a symptom of a wider problem that we're still experiencing. AI-trained algorithms need data servers; we're making more of those, which means taking away the resources to build graphics cards, meaning you'll pay more for them when the Nvidia RTX 60 series, Intel Celestial, and AMD RDNA 5 eventually roll out.

Will native performance ever be relevant again?

I mentioned above about the asterisk of estimated performance when a game's benchmarks and recommended system requirements go live. Oftentimes, these developer/publisher-approved tables promise 30-60 FPS as standard, and try to discreetly hide that DLSS, FSR, and XeSS are needed to hit that cap. It's something that Valve is just as guilty of with its somewhat questionable claims of 4K60, which was walked back when using FSR.

When the biggest and most well-known entity in PC gaming makes a move like this, the gaming world takes notice, particularly with who Valve was targeting in the first place. If FSR is essential for playable framerates, then it becomes non-negotiable; a forced standard, an excuse for developers to rush out unoptimized games, which have plagued countless PC ports over the last five years.

We find ourselves at a crossroads then. AI-powered upscaling does just as much harm as it does good; it is simultaneously the answer to (and cause of) a fair amount of the problems we're currently experiencing as PC gamers, as the benefits and cons constantly battle out for pole position. It looks as though AMD and Intel are on the right track, even if Nvidia is pacing its own trail, one that (hopefully) isn't followed by its competition. If we're already expected to pay four figures for a "mid-range" GPU, let us hope that it can perform decently enough.

Asus ProArt RTX 5090: Two-minute review

The Asus ProArt GeForce RTX 5090 32GB GDDR7 OC Edition is a specialised flagship GPU built for a niche audience and isn’t going to appeal to those looking for a premium upgrade for a gaming build. It’s aimed at creators, AI users and compact workstation builders who want full RTX 5090 performance without the bulk of larger 3.5 or 4 slot coolers.

The ProArt uses the normal RTX 5090 spec: 21,760 CUDA cores, 32GB of 28Gbps GDDR7 on a 512-bit bus and PCIe 5.0 support. The card has a 2482MHz default clock and a 2512MHz OC mode, and a 1000W power supply is recommended.

The 32GB of VRAM helps when rendering, running larger AI models or heavily modded 4K games would exceed the memory available on lower-tier cards. The ports — HDMI 2.1b, DisplayPort 2.1b and USB-C — give excellent monitor setup flexibility.

The 304 x 140 x 50mm, 2.5-slot SFF-ready design is a key selling point, as most flagship RTX 5090 cards are much chunkier, while the slimmer ProArt leaves more room for nearby expansion cards, side-panel clearance and power-cable routing.

Installation is straightforward: a 1-to-4 adapter is included and the 16-pin socket on the card is angled back for easy cable routing, plus the included adjustable GPU support can be used to help prevent sag.

Build quality is top-notch, with no obvious flex, and the GPU avoids gamer-style RGB lighting in favour of a subdued creator/workstation look with brown wood-patterned laminate trim and subtle ProArt gold markings.

Cooling is provided by two large Axial-tech fans, a vapour chamber, liquid metal on the GPU and a double-vented backplate. In OC mode, the GPU peaked at 76°C and memory at 86°C, with a 36dB peak sound level, which is reasonable for a slimmer RTX 5090 cooler. Unfortunately there was noticeable coil whine under heavy loads, and at times it was louder than the fans.

Standard mode was about 5% cooler and 6% quieter, but most importantly, barely slower, while Quiet mode was about 7% cooler and 6% quieter than OC mode, but gave around 2% less performance.

Overall performance is excellent, as expected from an overclocked RTX 5090. OC mode was about 3% faster than a stock RTX 5090, around 45% to 50% ahead of the RTX 5080 in compute loads, and up to 60% in gaming. The improvement is most obvious at 4K, in ray tracing and compute-heavy workloads.

The coil whine is frustrating given this is a card designed for creators, though it’s not so bad that you’d hear it outside a well-designed closed case.

Otherwise, the ProArt RTX 5090 OC Edition does a difficult job well. Yes, it’s expensive and specialised, but its slimmer design, quiet fans, 32GB of GDDR7 and top-end performance make it a capable choice for creators and high-end builders who can use what it offers.

That said, unless you can find it at a compelling price, it faces stiff competition from slim RTX 5090 cards that use AIO water cooling.

Asus ProArt RTX 5090: Price & availability

• How much is it? It retails for about $4,099 / £3,799 / AU$6,499
• When can you get it? The Asus ProArt RTX 5090 is available now
• Where is it available? You can buy it in the US, UK, and Australia

The Asus ProArt RTX 5090 is available now, retailing for about $4,099 / £3,799 / AU$6,499 depending on the region and retailer. That makes it around the typical price for a premium RTX 5090, though it sits above many cheaper air-cooled cards with the same spec.

While the ProArt would always be an expensive card, AI data centre demand has really pushed up prices, so it’s a tough sell unless you really need the features.

The premium price mostly gets you the compact ProArt design rather than a big performance uplift. You still get the full RTX 5090 spec, a mild factory overclock, 32GB of GDDR7, USB-C display output, a slimmer 2.5-slot cooler and a three-year warranty.

The main competition is from other RTX 5090 cards, and if you don’t need the SFF-ready form factor, cheaper air-cooled models will get you very similar core performance for less, while larger premium or all-in-one (AIO) water-cooled RTX 5090 cards may offer lower temperatures, less noise or more overclocking headroom for similar money.

If the compact creator-focused GPU is perfect for your next build, the ProArt may be a compelling buy. If you just want the cheapest route to RTX 5090 performance, wait for the price to come down a little, or look at alternatives.

• Value: 3.5 / 5

Asus ProArt RTX 5090: Specs

• Full RTX 5090 spec and 32GB of GDDR7
• Slimmer SFF-ready 2.5-slot design

• Specs: 4 / 5

Asus ProArt RTX 5090: Design

• Slimmer than most flagship RTX 5090 cards
• SFF-ready

The ProArt design is the main reason to choose this particular RTX 5090 over a more conventional option. Asus has kept the card to 304 x 140 x 50mm (12.0 x 5.5 x 2.0in) and a 2.5-slot thickness, which is much easier to work with than the very large 3.5-slot and 4-slot RTX 5090 designs.

It’s also SFF-ready, which means it fits Nvidia’s size guidelines for small-form-factor enthusiast builds rather than just being smaller than average. That doesn’t make it a tiny GPU, but it does mean the ProArt is built to work in more compact cases that support SFF-ready graphics cards, as well as larger systems where you want more room for other expansion cards or easier airflow.

Visually, it avoids the usual gaming-card look. The brown wood-patterned laminate trim and small ProArt gold details give it a more subdued workstation feel, and there’s no RGB lighting to worry about.

Cooling is handled by two large Axial-tech fans, a vapor chamber, liquid metal on the GPU and a double-vented backplate. That’s a decent cooling setup for a thinner RTX 5090, though the slimmer cooler does mean it has less heatsink volume than some of the much larger cards.

The card includes a dual BIOS switch for Performance and Quiet modes, a solid backplate and an adjustable GPU holder in the box. The 16-pin power connector is angled back toward the end of the card, which helps cable routing, but you’ll still need to be careful with bend clearance, especially in smaller cases.

Overall, the design feels practical, premium and nicely understated, but it only really makes sense if you specifically need a thinner RTX 5090.

• Design: 4.5 / 5

Asus ProArt RTX 5090: Performance

• About 3% faster than a stock RTX 5090
• Excellent cooling and low fan noise
• Very noticeable coil whine

The Asus ProArt RTX 5090 is at its best when you look beyond gaming and treat it as a compact high-end creator card. In Geekbench 6 Compute and Blender, it performs at the level expected for a tuned RTX 5090, with small but useful gains over the stock RTX 5090.

The gap is much bigger against older or lower-tier cards. Across the compute tests, the ProArt was about 50% ahead of the RTX 5080 on average, and around 40% ahead of the RTX 4090. The RTX 4090 is still a very capable GPU, but the ProArt’s combination of 32GB of faster GDDR7 and Blackwell architecture gives it a clear advantage in compute workloads and makes it a worthwhile upgrade.

While not the focus, gaming is still excellent, as you’d expect from an RTX 5090. Cyberpunk 2077 reached 98fps at 4K using Ultra Ray Tracing and balanced scaling, while Black Myth: Wukong hits 88fps at 4K with ray tracing, Cinematic settings and 50% upscaling. So while not the point of the ProArt, it’s a very capable gaming card.

Thermals are quite good for a slim RTX 5090. The ProArt peaked at 75°C, which is much warmer than larger 5090s with aftermarket cooling, which typically run about 10°C cooler. The VRAM reached 86°C, which is again higher than most 5090s but not in any way problematic.

Fan noise was also very low, with the ProArt peaking at just 36dB in my testing. That’s about the same as 5090s with big coolers.

All in all, Asus has done a good job tuning the ProArt to keep performance high and fan noise under control, even if that means letting temperatures sit a little higher than some larger cards. That trade-off is perfectly fine for a 2.5-slot RTX 5090, and the GPU and VRAM temperatures stay well within reasonable limits.

The catch is coil whine, which is an electrical buzz caused by components vibrating under heavy power load, rather than the fans themselves. In fact, most of the time it's louder than the fans themselves.

Some coil whine isn’t unusual on very high-power graphics cards, but it’s still disappointing on a premium creator-focused model where low noise is a key selling point.

Cyberpunk 2077: Ultra Ray Tracing, balanced scaling
Black Myth: Wukong: Ray Tracing, Cinematic, 50% upscaling

• Performance: 4 / 5

Should you buy the Asus ProArt RTX 5090?

Buy the Asus ProArt RTX 5090 if...

Don't buy it if...

How I tested Asus ProArt RTX 5090

• I spent about a week testing the Asus ProArt RTX 5090
• I ran synthetic, compute and gaming benchmarks
• I compared it against the stock RTX 5090, 4090 and the Asus ROG Astral RTX 5080 OC

I spent about a week with the Asus ProArt RTX 5090, testing it in OC mode and comparing it against other high-end cards. The main comparisons were stock RTX 5090 and RTX 4090 performance and the Asus ROG Astral RTX 5080 OC.

I used 3DMark for synthetic performance, Geekbench 6 Compute and Blender for creator workloads, and games including Cyberpunk 2077 and Black Myth: Wukong for real-world gaming results.

• Originally reviewed June 2026

• YouTuber bitluni has unveiled a RISC-V DIY Graphics Processing Unit
• Builder has a library of insane DIY projects on his YouTube channel
• RISC-V microcontrollers were sourced from AliExpress

YouTuber bitluni has unveiled his latest project, which might put any concerns you have over GPU pricing into context. The German DIYer, who has built up a strong following with his selection of intriguing and off-the-wall projects, has assembled a GPU using a large collection of affordable RISC-V microcontrollers (MCUs).

While the results are impressive for a DIY build, the GPU is unfortunately quite modest, and would probably have been abandoned long before now had a PCB design firm not got in touch to explore a partnership over the build.

Demonstrating the project on his channel, bitluni – real name Matthias Balwierz – expressed how difficult the build was, and how it almost drove him mad.

8000+ RISC-V MCUs output at 320x200 resolution

In his latest video, bitluni recalls a previous attempt at building a GPU in late 2025 which lead to PCB designers Altium getting involved, explaining, “The clusters I made before were already challenging my sanity. I thought I was done with the topic, but the budget and these tools would allow for a cluster of a different magnitude, and the magnitude I had in mind was just insane.”

Pushing things to the next level is a hallmark of bitluni’s videos, and the finished build has an incredible 8,192 RISC-V CH570 microcontrollers, each running at 100 MHz, with 12KB of SRAM. These are mounted on blades, which went through several revisions as the PCB company declared them “too complicated.”

Each CH570 has an array of LEDs mounted, and these correspond to each MCU, and the equivalent QVGA pixel. With 8,192 MCUs, the resulting 320x200 might seem modest, but bitluni already has plans for a more power version with 32,000 MCUs.

Graphics or hashing?

The spike in GPU prices before the AI boom was thanks to industrial-scale cryptocurrency hashing, which graphics cards a particularly suited to. It is this relationship between hashing and graphics that gives bitluni’s project some hope beyond displaying images and video.

Identifying the serial port as the bottleneck to the project’s success as a working (if unwieldy) GPU, bitluni has appealed for help from his community of viewers to find a workaround, but that didn’t bring the project to a close.

Changing tack, he used the microcontrollers for hashing and found it outperformed his PC’s 8 core CPU, with a low 4 watt power draw, so perhaps there is a use for this approach after all.

• A Reddit user's RTX 5090 has partially melted a PCIe riser cable
• Some of the riser cable's material is stuck on the RTX 5090's backplate
• It's not as serious a concern as connector melting issues, but it's worth being cautious about riser cable and GPU spacing henceforth

Building a new gaming PC in 2026 and beyond is a tough ask for any gamer amid the RAM crisis, and that's why this case of caution around hardware components is vital.

As reported by TweakTown, a Reddit user has reported a partially melted Lian Li PCIe riser cable due to heat from direct contact with the Asus TUF GeForce RTX 5090 backplate. The material chipped away from the riser cable is evident on the GPU's backplate, but fortunately it isn't a substantial amount.

Riser cables are an ideal solution for any PC builder aiming for a small case that can fit larger GPUs by placing them in a vertical position. In this case, the user specifically notes that the riser cable was stuck against the GPU's backplate during a routine PC cleanup, suggesting that the GPU's heat melted the riser cable's insulation.

It shouldn't come as a huge surprise that the RTX 5090 is involved in a case of components melting (even if it's very minimal). In case you've missed them, there have been several cases of GPU power cables melting with RTX 5090 GPUs, which stems from the high power draw (575 W).

It's important to note, though, that this can happen with other GPUs across AMD's Radeon and Nvidia's RTX cards, especially those that can get quite toasty under heavy load. However, Team Green's flagship is likely more prone to melting complications, made worse when using cheap riser cables or GPU power connectors.

Fellow Redditors argue that this isn't a melting issue, as it appears the riser cable has been chipped away instead. Regardless of the exact cause, it's best to use this as an example to proceed with caution when mounting a new GPU.

If using a riser cable is required due to space limitations, users must provide enough clearance for both the GPU and the riser cable to avoid direct contact. It's a great thing that this user caught on to the matter before any real issue arose.

We've reached out to Nvidia to see if it has any comment on this particular case, but haven't received a response yet — but I'm sure the same warning of caution will be shared.

We've seen rare but far worse cases of GPUs melting, and given how expensive it would be to buy new PC hardware, I'd go so far as to suggest users be overly cautious.

• AMD is reportedly preparing a 10% price hike on graphics memory kits for Radeon GPUs
• This means retailers could increase prices of Radeon GPUs from the likes of Sapphire or Asus
• This case is yet another example of just how hard the RAM crisis is hitting the PC market

Gaming is becoming an expensive hobby for both console and PC gamers, and it's all because of the ongoing RAM crisis. Unfortunately, another manufacturer's GPU components may be the next victim of price hikes.

As reported by VideoCardz, AMD is planning a 10% price increase on graphics memory kits for its Radeon GPUs this July, according to a new post on a Board Channels forum. This means that AMD's AIB partners, such as Sapphire and Asus, will pay more for components, which might result in higher retail prices for Radeon GPUs.

The Board Channels post states that these price hikes are a result of 'tight global supply of graphics memory chips,' including the noticeable trend of 'continuously rising market prices'.

Of course, there's no confirmation from AMD on the matter just yet, but if true, it doesn't help AMD's position in the GPU market up against its fierce rival, Nvidia. Frankly, both are in a tough spot due to higher memory costs across the board, which is well established for effectively every PC manufacturer at this stage in 2026.

While most of Team Green's RTX GPUs are fairly expensive for consumers, the advantage of better upscaling technologies and raw performance when using ray tracing in games makes them more appealing, in comparison to newer Radeon GPUs.

While we await confirmation from AMD, consumers should start buying new hardware now. Both AMD and Nvidia GPUs are still expensive across several retailers, and it's a tough spot for any gamer to be in when searching for an upgrade.

However, if you don't own a gaming PC at all, it's worth keeping an eye out for several deals — and fortunately, Best Buy's 4th of July sale is already underway, and this iBuyPower Slate Intel Arc B570 gaming PC is only $899.99 on sale.

It also comes with 16GB of DDR5 RAM (which would be expensive to buy separately), and most importantly, it's using a GPU that's perfectly capable of great gaming performance at 1080p and 1440p resolutions.

When acknowledging the messy state of the PC hardware market and the prospect of more price hikes, it's hard to ignore a deal such as this, especially when Intel's XeSS upscaling has improved over the last few years — so if it were me, I wouldn't hesitate to purchase.

• Nvidia's RTX 3060 has returned to retailers, amid the ongoing RAM crisis
• MSI, Gigabyte, and Asus models reportedly started at $339.99, but have seemingly increased
• It's a sign that the current state of the PC hardware market is in complete disarray in terms of shortages and pricing

The PC and console hardware market is in the worst place its arguably ever been, with the RAM crisis leading to high prices on components utilizing memory. That's why the return of a popular GPU is one that doesn't come as a huge surprise.

As reported by Tom's Hardware, the Nvidia GeForce RTX 3060 graphics card has returned to retailers, notably on Newegg, with numerous Asus, MSI, and Gigabyte models available.

While Tom's Hardware highlights that prices for these returning GPU models were seen at $339.99 (which is $10 above the MSRP when launched), listings range from at least $416. It's already a big ask at $339 for an old graphics card that doesn't have the same advantages of the current-gen RTX 5060, so the increase won't do it much favors.

It's worth noting that the RTX 3060 has been the most popular GPU on PC, according to Steam's Hardware & Software Survey results, consistently ranking at number one each month for several years. The same trend is evident in May's results, and likely the same for June.

The 12GB GPU is still very much capable of providing solid performance in games at 1080p or 1440p, especially when using DLSS upscaling, and that explains why it's still so popular among Steam users.

However, it's not exactly being received well among consumers. My colleague and PC enthusiast, Matthew Hanson, views the return of the RTX 3060 "like a s**t Frankenstein", and frankly, it's a reasonable observation and one that highlights just how messy the PC hardware market is.

Users on Reddit haven't been shy about highlighting why the GPU's return doesn't make much sense, with one stating: "Even Nvidia enthusiasts would be hard-pressed to get this for $339.99. At that point, just get an RTX 5060.

"The only real benefit to this card is the VRAM, so we all know who this is really for - The AI crowd."

It's not that the RTX 3060 is a bad graphics card, because that's far from the case. However, at the current price the models from MSI and others are available at, it makes little to no sense, since the RTX 5060 is within the same price region, and it has the advantage of Multi-Frame Generation over the RTX 3060.

Regardless, one more option on the market doesn't hurt, especially since it doesn't seem as though its unstable condition will change anytime soon.

These days, if you're looking at tech news, it won't be long before you come across a mention of the RAM crisis. The dwindling supply of memory chips is proving increasingly problematic, and at this point, it seems that the AI industry hasn't just eaten the consumer's lunch, but also their dinner, pudding, and the little mints that usually arrive with the bill afterwards. And did it tip? Not likely.

I have got a tip for you, though – yeah, I know, smooth segue – or rather five tips as we face the inexorably on-rolling memory shortage, which is crushing all wallets in its path. In this article, I've picked out five pieces of computing hardware that I believe you should buy now, or in the very near future, before the PC component crisis — because it extends beyond mere RAM, of course — renders this hardware a good deal pricier.

Nothing is guaranteed in life, not even my expert predictions (ahem), so consider them with that in mind. But I'm making these recommendations — from MacBooks to SSDs — based on what's happened most recently in terms of said crisis, for good reasons that I'll expand upon when I come to each individual pick.

So here are my five devices or components which I believe you should move sooner rather than later with if they're on your shopping list. Bear in mind that with Amazon Prime Day just around the corner (and discounts on the go already), it could also be worth keeping an eye out for bargain deals at that retail giant.

1. Apple MacBooks — especially the MacBook Neo

Earlier this week, Tim Cook came forth with some information that everyone who's mulling buying a new Mac needs to know about. The Apple CEO told us that its products are going to get substantially more expensive before long, liberally sprinkling around words like "unavoidable" without going into any specifics.

Price hikes, and likely sizable ones, are coming to Apple's major products, then, and that includes MacBooks (as well as iPhones and probably iPads too, and more besides no doubt). What I'm particularly worried about though, as I discussed at the time, is what this could mean for the MacBook Neo.

Long story short: Apple ditched the entry-level Mac mini last month, the product which cost an enticing $599 (£599 / AU$999), making the next-tier-up model, which has twice the storage (512GB instead of 256GB), the cheapest option at $699 (£699 / AU$1,099). Might the same thing happen to the MacBook Neo? I believe there's a chance that Apple could mirror this move, because given the need to address MacBook pricing, when it comes to the Neo, applying a hike this early in the device's lifespan doesn't look good. Especially not when it's built entirely on its value appeal as a budget model.

So, a way of rejigging things without a price hike would be to just ditch the current entry-level Neo, just as happened with the Mac mini, leaving folks to pay a hundred notes more for the baseline model (while getting more storage, granted). Then this could be framed as a configuration adjustment rather than a cost adjustment.

With that in mind, the MacBook Neo at its current entry-level price might be a steal, and that's especially the case if you can get a bargain on the laptop. Indeed, there's already a good early Prime Day deal in the UK (at the time of writing) — and there may be others too.

Of course, there could be deals on other MacBook models, and just as with the Neo, it's likely a smart move to grab a bargain on a MacBook Air or MacBook Pro, or any Mac you might be considering buying this year. All these PCs may be hit with what'll seemingly be substantial price hikes, and so I think buying now is very much the thing to do.

A MacBook Air M5 at $950 currently (the discounted price in the US on Amazon, again as I write this) might look pretty good compared to the Neo at $700 a little way down the road, potentially.

2. SSDs

My second port of call given recent rumblings with the component crisis is to grab a relatively affordable SSD if you can, though granted, this comes with caveats — it may not be easy to find one.

Still, I think it's worth trying — again, Prime Day might help here — and to pick an example, Samsung's 990 Pro SSD has been at reasonably palatable pricing (for the current climate) of late. Okay, so a current deal on the 1TB model costs about the same as the MSRP of the 2TB flavor before the RAM crisis hit, but still, a bit over $200 in the US is not bad in the overall picture.

And the thing to bear in mind here is that earlier this week, an exec in the SSD supply chain (a VP at Silicon Motion, which makes drive controllers) gave us an alarming soundbite that ran: "The retail SSD market has almost disappeared." With SSDs all being funneled towards data centers and OEMs (PC manufacturers), there's precious little left heading to the shelves where the average consumer can pick them up.

Unlike RAM, where price inflation may not have much further room to maneuver — given that consumers will just stop buying at some point — there is still some space for SSDs to rise. What the Silicon Motion VP said all sounds rather ominous, and points to a picture where the decent deals — relatively speaking — that we can get right now on SSDs may evaporate before too long.

3. External hard drives

This comes right off the heels of the above in the world of storage, as there's evidence pointing to a rise in hard drive prices. While as noted, SSD prices have shot up — especially for larger capacity or high-end models — hard drives hadn't really been affected, at least not until a few months ago.

Based on the findings of 3D Center, which monitors the German retail market, hard drives are becoming increasingly expensive. Last month, they were 34% more expensive than a year ago, and as of June 2026, that ante was upped distinctly to 58% (effectively a price increase of close to 20% in the span of a month).

So, if you need an external hard disk for your backups or NAS — or to provide extra capacity for your Mac with a small SSD — you might want to consider buying it now before price increases shift up another gear, which looks like what's going to happen.

4. Windows 11 laptops

MacBooks aren't all you need to worry about when it comes to price hikes, as naturally the broad upward pricing pressures on components affect all notebooks.

I previously wrote about why it's a good idea to buy a Windows 11 laptop now, following Nvidia's CEO, Jensen Huang, predicting that the RAM crisis might spin onwards until the end of this decade.

The crux of the matter is that affordable notebooks are still very much a thing, especially during sales, but with the additional gloom cast on pricing woes in recent times — particularly by Huang, who is well placed to forecast — I feel there's a distinct danger that it could be harder to buy a cheap laptop before long.

I doubt you'll regret buying a Windows 11 laptop at today's prices, put it this way, particularly with a discount applied.

5. Graphics cards

Finally, if you're pondering a graphics card upgrade at some point going forward, this is another component that I reckon it'd be smart to grab soon.

While price increases for Nvidia GPUs have been more confined to higher-end models — products with more video memory (VRAM), which is where the pricing misery mainly stems from — I think that they'll apply more to mainstream cards as 2026 rolls on.

Why? For starters, I don't think it's a great sign that Nvidia is resurrecting half-decade-old GPUs from the generation before last to help bolster supply levels of its boards. (These RTX 3000 models use GDDR6 VRAM, which is a useful alternative memory vein to tap away from the GDDR7 that's in current-gen graphics cards). We've seen nasty hikes applied to the RTX 5060 Ti 16GB already, and the RTX 5070 price is creeping up too, based on the latest data from our sister site Tom's Hardware.

The RTX 5070 and RTX 5060 8GB can still be had at relatively palatable prices, though, so they may be a solid buy with a bit of a Prime Day discount in the offing perhaps. (Even if they aren't hugely well-liked GPUs, in all fairness, for one reason or another — the scant VRAM allocation being the main bone of contention for many gamers).

Where I'd be more inclined to look, though, is at the AMD RX 9070 XT. That GPU is in a similar position to the RTX 5070 in the US with a relatively mild (roughly) 10% price hike right now, the difference being that it packs 16GB of VRAM and considerably better performance (at least away from ray tracing).

We've seen some decent deals on the 9070 XT recently, and if you can grab one, I think that could be a great move as a GPU upgrade that'll last you. Particularly given that AMD's CEO Lisa Su has forecast pricing misery to come later this year, with Team Red serving up some relatively strong signals for the likelihood of incoming Radeon graphics card price hikes.

• The first HDMI 2.2 products are likely to arrive in 2027
• Massively increased bitrates with 4K 480Hz support, or 10K resolution
• Great for PC gaming, but won't make much difference to TV viewers

HDMI 2.2 is coming, and it could be a very big deal for gamers. That's because it delivers double the bandwidth of HDMI 2.1: up to 96Gbps.

That means HDMI 2.2 is capable of delivering uncompressed 4K at up to 240Hz (current HDMI can only do this using Display Stream Compression, aka DSC) or uncompressed 4K at 480Hz with 4:2:0 chroma subsampling, and it can also deliver uncompressed RGB 8K at 60Hz. And using DSC, it's capable of delivering 1440p at rates exceeding 1,000Hz.

There is an important qualifier, though: devices can be HDMI 2.2 certified without delivering the very fastest data speeds, so you'll need to study the spec sheets of any potential purchases.

As FlatpanelsHD reports, the HDMI organization expects the first HDMI 2.2 devices to go on sale in 2027; testing and certification are already taking place.

According to Rob Tobias, CEO and president of the HDMI Licensing Administrator, "We're hearing chip manufacturers will start to sample their FRL2 [Fixed Rate Link, the signalling technology used by HDMI] chips this year. And so we should start to see some 96 or up to 96 gigabit HDMI 2.2 products next year."

Will HDMI 2.2 be a big deal?

The specifications are impressive, though it'll be available in three flavors, all of which are faster than HDMI 2.1: 64Gbps, 80Gbps and 96Gbps.

The performance for gaming is particularly impressive, but we already have DisplayPort 2.1 at up to 80Gbps in many of the best gaming monitors so there isn't a hugely pressing need to upgrade. And HDMI requires licensing fees that are likely to be higher than for DisplayPort.

Chances are if you're into high-end gaming hardware you've already gone down the DisplayPort route, especially if you have a multi-monitor setup, but HDMI 2.2 should mean more choice among premium displays.

HDMI has more benefits in living rooms, thanks to features such as ARC (Audio Return Channel), CEC (Consumer Electronics Control) and ALLM (Auto Low-Latency Mode), so it's conceivable that the PlayStation 6 could embrace the higher bitrates of HDMI 2.2 — although games only rarely achieve the 4K 120Hz that's already achievable over HDMI 2.1, and we're not sure that's going to really change with the PS6.

But some 4K 240Hz games might be possible, and the best gaming TVs might offer the refresh rate to support it, so there may be some benefit for the most hardcore. For most living room uses, though, HDMI 2.1 is probably overkill — it's really best for PC connectivity when it comes to the pure data rate.

However, there's another benefit to HDMI 2.2: LIP, which stands for Latency Indication Protocol. LIP improves sound syncing on home theater setups, such as soundbars or AV receivers — this can be a pretty common problem with even the best soundbars, so we'll be keeping an eye on how well this works.

For most of us, I think HDMI 2.2 is generally something to keep an eye on rather than something to demand as soon as it debuts. HDMI 2.2 has been made with 10K resolution in mind, but the vast majority of commercial and streaming video tops out at 4K.

It's something to think about when buying new AV hardware in the future (there's good chance we'll need to wait for 2028's CES for widespread adoption in TVs), but initially at least, it'll be more about marketing than real-world benefits for most of us.

And bear in mind that HDMI adoption is really driven by the companies that provide the connection hardware.

Even today, not all high-end TVs have four HDMI 2.1 ports, because the most powerful processing chips used by many manufacturers don't support this. Some lower-tier TVs from the same makers do, because those chips have more up-to-date HDMI control elements — it's a bit of a mess. I suspect HDMI 2.2 will have the same kind of slightly chaotic support.

On PCs, we'll be relying on GPUs to support the tech, and new models aren't expected until late 2027 or after that — so there's a good chance of seeing HDMI 2.2 on them. But like HDMI 2.1, it'll probably launch on the highest-end models first.

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• PUBG's Ally Duo mode is now in testing on Steam
• Nvidia's ACE tech is powering the AI teammates in this mode
• You can play with an AI teammate against other human + AI duos until the end of June

PUBG Battlegrounds now offers the option to play with an AI teammate — powered by Nvidia — in a new mode which is available to try in beta for the next two weeks.

As VideoCardz noticed, the Ally Duo Mode is now available through PUBG Arcade, and represents Krafton and Nvidia teaming up to use Team Green's ACE technology — as aired back at the start of the year — to create an AI teammate called Ella.

PUBG Ally was in testing early in 2026 and has now reached the point where it's ready for public consumption — at least as a beta, where the mode will be playable through to the end of June on Steam.

This is an opportunity to "collect invaluable real-world player feedback, to guide the future of AI agents in games," Nvidia tells us.

How does Ella work exactly? The best way to find out is to watch the demo in the YouTube video below.

As you can see, the idea is that you have an AI teammate you can talk to, and more to the point, give orders, or ask for help, using natural spoken language (or typed text if you prefer), with responses from the AI designed to be suitably 'human' in feeling and tone.

At least that's the idea here, but watching the brief demo leaves me with a lot of doubts about what "represents a new generation of AI game characters designed for deeper immersion" according to Nvidia.

Analysis: reaping what was sown a long time ago

It should be no surprise that Nvidia is dressing this up in a lot of fancy talk. Last year when it introduced the concept, Team Green talked about revolutionary Co-Playable Characters or CPCs, as opposed to boring old NPCs. Of course, as my colleague on TechRadar Christian Guyton noted at the time, these are just glorified bots – and we've had bots for ages (PUBG has, too).

In fact, I was deathmatching bots some 30 years ago in Quake, when the Reaper Bot mod arrived. (The Reaper was a horrifically accurate CPU-controlled creation — positively lethal if it got hold of the lightning gun – but overall it had the strategic skills and gaming savvy of a house brick, and was easily trounced by a reasonably good player, but hey, these were very early days here.)

So, this isn't a revolutionary idea, or the next step on from NPCs, or whatever accolades relating to gaming greatness that Nvidia might want to heap on PUBG Ally. However, there is more to Ella than this, in fairness to Team Green. One half of Ella is the bot intelligence to actually play the shooter well enough (hopefully), but the other side is the AI models — the Nvidia ACE trimmings.

These are small language models (SLMs requiring an Nvidia GPU with at least 8GB of video RAM) driving the AI companion's "realistic" decision-making processes, and facilitating communication via speech models. Ella is "equipped with the ability to understand and respond to game situations in a human-like manner" over and above your typical game bot, but I'm not convinced from the demo.

Ella feels painfully artificial — not human — and borderline sycophantic in the game footage shared by Nvidia. OK, so this is still early testing, but I'm not getting any real 'revolutionary' vibes about the gaming skills or chat on show here.

Maybe we'll get selectable personalities eventually — and even true-to-life gaming types. For realism there definitely needs to be an occasional AI teammate who throws a massive hissy fit about how rubbish you are before abruptly quitting, surely?

The general reaction to the emergence of PUBG Ally has been as you might expect: some gamers are curious, while others are mystified or even scathing, and there are more in the latter camp. Some are convinced this will be highly amusing: "I look forward to the comedy that this feature will produce." While others on the same Reddit thread are already trash-talking the AI's ability to play.

I'm not surprised at the feedback thus far given the way Ella has been realized by Nvidia, and the AI being overly chatty hasn't gone down well either. Players engaged in a competitive game don't want flowery chat putting them off their flow, and maybe obscuring important sound effects that are clues as to where the enemy might be and so forth.

The whole thing leaves me rather cold at this stage, frankly, but among more casual gamers — or those who don't have friends to play with at the time, and don't want to hang around in lobbies, or be exposed to toxicity in pick-ups — Nvidia's AI teammate may yet find a place.

As long as the gamer in question owns a decently beefy Nvidia graphics card, that is, and another concern of mine is how much of the GPU's resources are these AI models demanding? Presumably not a lot —they are 'small' by nature — but gamers are notoriously unhappy about anything running interference with their FPS, no matter how slight.

• Rumor has it that Intel's CPU with integrated Nvidia RTX GPU is planned to debut early in 2028
• We could see an announcement at CES 2028
• This will be a powerful mobile chip that could be a huge step forward for gaming laptops and handheld devices

Intel processors with integrated Nvidia RTX graphics are coming, we know that, but we now have a rumor of a rough timeframe, namely early 2028.

VideoCardz reports that according to YouTube tech reporter Erdi Ozuag on X, Intel's silicon roadmap currently has its processors featuring built-in Nvidia GPUs as launching in the first quarter of 2028.

In theory, then – assuming this is true, and the planned timeline doesn't slip – we could be looking at a CES 2028 launch for these chips which are codenamed Serpent Lake.

Also regarding the timeline, it aligns with the expectations for the release of Nvidia's next-gen of GeForce RTX GPUs, which is currently forecast to debut in 2028.

Ozuag adds that the prospect of Intel producing Apple silicon continues to advance, and the final quarter of 2027 is apparently the target for Team Blue to be making chips for MacBooks or iPhones (probably lower-end models, the rumor mill has previously suggested). This (theoretically) represents Apple diversifying somewhat away from TSMC for chip production duties.

Analysis: a lot of potential – but some questions too

An Intel processor with Nvidia RTX chiplet on-board to provide graphics muscle should be a huge move for the PC world. Granted, we know very little about the chip as it stands, but packing next-gen RTX graphics is likely to mean this could usher in SoCs which are revolutionary for the performance of thin-and-light gaming laptops. Indeed, these Intel processors should prove excellent for elevating gaming handhelds too.

If this rumor is true, it means these Intel chips are potentially only a year and a half away. Or at least that's when we could first see the silicon announced, although the notebooks packing the chips themselves may take a fair bit longer to arrive on shelves.

Of course, a lot of how this timeline plays out may be heavily dependent on what happens with the current RAM and component crisis, and its impact on the world of laptops in general. (Which could get substantially nastier as time rolls on – as I've discussed elsewhere recently, I think now could be a smart time to buy a laptop based on the indicators we're hearing from various key players in the PC sphere – and indeed phones for that matter).

Other questions remain, such as: where does this leave Intel's own Arc graphics plans? And how will this partnership play out given that Nvidia is now spearheading a fresh thrust to make Arm devices more relevant in the realm of Windows 11 laptops with its RTX Spark chip – a prospect that'll surely worry Intel (despite the brave face in public)?

There are a few unknowns as to how all this will unfold, let's put it that way, but this Intel and Nvidia collaboration still remains an exciting prospect.

• AMD's RX 9070 XT GPU is finally in the Steam hardware survey
• As of May 2026, it's suddenly the most popular AMD GPU
• It has a 1.33% market share, but Nvidia is still way ahead of AMD overall

While Nvidia's current focus certainly isn't on its gaming graphics cards, it still has the desktop GPU market firmly in its grasp — although things might be looking up for rival AMD.

As reported by TweakTown, the AMD Radeon RX 9070 XT is suddenly the most popular AMD GPU, according to Valve's latest Steam Hardware Survey. As of May 2026, the 9070 XT has leapt to 1.33% of Steam's market share, placing it above the likes of Nvidia's GeForce RTX 4070 Ti (whereas previously the AMD card failed to register at all in the survey).

As always, it's worth noting that Valve's Steam surveys are only a limited perspective on the whole market. Not everyone runs Steam, of course, and the survey is limited to a relatively small slice of gamers on the platform. That said, Valve's survey results are an interesting glimpse into the state of hardware and software popularity.

The RX 9070 XT's popularity shouldn't come as a huge surprise, considering its raw performance capabilities, with the additional benefit of FSR 4 (FSR Redstone) providing solid image quality and performance when upscaling.

What's more surprising is why this GPU wasn't present in Steam's GPU rankings at all before now. TweakTown theorizes that the sudden appearance of the 9070 XT could be down to recent updates to the survey regarding how GPUs are classified, which is one possibility.

Still a one-sided battle

Since the GPU market is still in disarray due to the RAM crisis and its impact on video RAM, both the Radeon RX 9070 XT and 9060 XT (which is also now present among Valve's May survey results) are arguably the most value-packed GPUs available. They're not as cheap as Nvidia's low-end RTX 5000 series GPUs, but these Radeon graphics cards are still affordable and provide much better performance.

Still, it's hard to see Nvidia's GeForce GPUs falling out of favor anytime soon, and they remain clearly dominant in the desktop arena. Team Green's DLSS upscaling technology remains superior to AMD and Intel's equivalents, and it really impresses on the image quality and performance fronts.

Nvidia's RTX 3060 is still leading as the most popular GPU, as it has for years in the Steam survey results, but it's good to see Team Red's latest GPU line-up putting up a fight finally — even if the competition is still very one-sided.

• The Nvidia RTX 3060 and 3050 are apparently making a comeback
• A graphics card maker in Asia has new models planned as budget alternatives, and this practice could spread to other regions
• Meanwhile, we're hearing that SK Hynix plans to accelerate development of its memory chip production capacity and triple it by 2034

The RAM crisis appears to have another side-effect, namely bringing back old GPUs, but there's some more positive news for the future from one of the big memory chip makers.

VideoCardz noticed the resurrection of a couple of Nvidia graphics cards that aren't even from the past generation, but the one before that: the RTX 3050 and 3060, which are GPUs from 2020 that have been brought back by Manli, a graphics card maker that mainly supplies Asia.

The Manli RTX 3060 which has returned from the dead is a 12GB model, whereas the RTX 3050 has 6GB of VRAM and a 70W power envelope, meaning that it can run off the PCIe slot (with no need to be connected to the PSU).

Elsewhere, the future of RAM is looking rosier, with news from SK Hynix, one of the major three players in making memory chips, that it's ensuring that manufacturing capacity is being ramped up in a big way going into the next decade.

Chey Tae-won, the chairman of SK Group, said in an interview with Nikkei Asia (via ComputerBase, as spotted by TweakTown): "Since we're proceeding with the plan to expand as much as possible, our calculations show that our wafer capacity will double within five years. But honestly, once all these facilities are built, it won't just double, it will triple by around 2034."

So that means by 2031, SK Hynix will have doubled its manufacturing capacity, and it'll only be a few years more until that's trebled, moving notably faster down the line. That said, this is a best-case scenario we're told, and those expectations could always slide.

Analysis: a notable acceleration

This represents SK Hynix putting its foot down on the RAM production pedal in a big way. We're now looking at eight years for a trebling of manufacturing capacity for DRAM and NAND flash memory chips, whereas previously the timeline for this was 20 years. Yes – two whole decades.

This is a measure of how much RAM demand SK Hynix is expecting going forward – and that the current memory-hungry environment isn't going away. As we've been hearing recently, if anything, the AI boom is set to continue and gobble up even more RAM in the foreseeable future.

As for the Nvidia GPUs which are being brought back to life, this may be just in Asia for now, but it indicates that this could happen elsewhere, too. There have been previous rumors about the RTX 3060 12GB making a comeback, so here we're seeing some hard evidence that this is actually happening.

Are you wondering why it's happening? Well, both of these GPUs are back to serve as budget-friendly options in a time where current-gen GPU availability is becoming more problematic, and prices are rising. (Indeed, at the higher-end for Nvidia GPUs, price inflation has been getting quite nasty this year, and the fear is that it'll only worsen).

The RTX 3060 was a popular offering for a long time, and the 12GB variant proved tempting for gamers who didn't want to be shortchanged by newer Nvidia budget graphics cards that only served up 8GB of VRAM. While you may question the wisdom of an affordable GPU that packs on the video RAM in this memory-starved climate, remember that this Nvidia card uses GDDR6 VRAM, not GDDR7 as seen in current-gen Blackwell GPUs, so it's a different kettle of fish in that respect. It theoretically allows for some extra breathing room to produce some more wallet-friendly graphics cards without tapping into precious GDDR7 resources.

Of course, we don't know how wide a scope this apparent revitalization of old Ampere GPUs might have, and it could be quite a limited affair. Time will tell.

• Nvidia's CEO has said the RAM crisis will last for "quite a few years"
• The comment came ahead of the announcement of a multiyear partnership between Nvidia and memory chip maker SK Hynix
• It's one of the bleaker predictions we've heard, and it might be wise to take it on board if you're thinking of buying a laptop, PC, or components in the near future

If you were hoping the worst of the RAM crisis might soon be over — and we've heard some hints to that effect in recent times — you can think again, according to Nvidia's CEO.

Reuters reports that Jensen Huang just predicted that the memory shortage is a crisis that'll last for "quite a few years", and this doubles down on some of the more pessimistic forecasts about the RAM crisis.

Given that this comes from a heavy-hitting source – Huang was speaking ahead of the announcement of a collaboration between Nvidia and SK Hynix in a "multiyear technology partnership for next-generation memory" — it's a distinct worry for me.

As I noted at the outset, we've recently heard some more positive inklings, suggesting that the RAM crisis could be peaking and a recovery might arrive sooner than expected. That includes the ex-chief of Samsung's semiconductor business forecasting that the RAM shortage could possibly ease in just a year's time, or certainly by early 2028, partly due to the efforts of Chinese memory chip makers.

Previously, though, we've also heard that more RAM price hikes are on the cards due to disruption to shipping in the Strait of Hormuz, as well as predictions that the memory crisis could be prolonged until the end of the decade.

Interestingly, that latter claim of RAM pricing misery continuing through to 2030 came from the chairman of the parent company of SK Hynix, which Nvidia just signed its deal with, and Huang appears to have adopted the same line of thinking. For me, the declaration of "quite a few years" means at least three, and hints at more, effectively backing that previous 2030 worst-case prediction.

What does this mean for the average consumer? The words from the Nvidia CEO carry some considerable weight, I think, and this is a rather ominous development in my opinion, although it's not quite as simple as "if you think prices are bad now, just wait — so buy, buy, buy, while the going is still relatively good".

Of course, calling the going 'relatively good' given the price of RAM sticks themselves is more than a stretch, but the truth is that there are certain categories of hardware you should probably consider pulling the trigger on now, to avoid what might be even more painful price hikes by the time next year rolls around.

Here's what I think we all need to consider given the current picture and predictions relating to the RAM crisis, in terms of different types of hardware. I should caveat all this by underlining that it is just my opinion, and I may, of course, turn out to be wrong.

Laptops: don't hang around for too long here

What immediately sprang to mind when I read Huang's comment was that this additional gloom nugget is another sign that the time to buy a laptop is now. Or rather, perhaps as part of an Amazon Prime Day deal (later this month) or come Black Friday, with these big tech sales in 2026 likely to represent your best chance to get a new notebook at a relatively affordable price.

The trouble is that if the RAM supply situation really is set to be a very rocky road through to the end of the decade, even the big laptop manufacturers are going to run out of all their inventory of memory bought at (relatively) cheaper prices – and the same is true of storage (SSD price hikes have also been seriously nasty).

If all this plays out as Huang expects, continued pressure on just those two component categories are going to hike laptop prices substantially over a few years. And that's not considering other pressures such as with discrete GPUs (that use VRAM) on gaming laptops, or price increases on other notebook parts, notably CPUs which are starting to feel the heat a bit in the broader component crisis.

If you're thinking you'll need a new notebook in the not-too-distant future, and you can score a good deal with a laptop that's on sale this year, I can't see you losing out. It seems unlikely that you'll find something cheaper next year, put it that way — and if there's one piece of hardware I'd advise buying with some level of urgency, it's a laptop.

Do bear in mind, however, that more Windows 11 laptop makers are now dropping models back down to 8GB of RAM as a cost-cutting measure to reduce those sticker prices, and there are caveats for future-proofing here, which I've discussed at length elsewhere.

Desktop PCs: ditto, sort of…

I think everything I've written above about laptops is also true of desktop PCs to an extent, as the same pressures around the same components exist broadly, certainly for prebuilt computers. Matters are worse in so much as a desktop will usually have a discrete GPU (much more often than a laptop will), and the price hikes therein add some extra misery into the mix, particularly at the higher-end (although the same is true of gaming notebooks).

So, again look to the likes of Amazon or Black Friday sales, and if you need a new desktop, a discounted prebuilt might turn out to be a pretty smart move this year.

However, a DIY PC build is a bit different and more painful these days, as you are sourcing individual components all with varying price hikes, and some of those increases are huge – you're lacking any of the cost cushioning that you might get from a prebuilt (where the PC maker gets cheaper prices for bulk buying components, of course). In this case, I'd be far more cautious, as it's not exactly clear how things will play out with component pricing more widely as I'll come on to next.

RAM and SSDs: the real pain points

The RAM crisis kicked off all this pricing pain when it began back in September 2025, followed by SSDs (which also use memory chips, of course), and at this point, price tags on both are massively hiked.

Having to buy system RAM is as bad as it gets when it comes to the extra damage to your wallet, although the exact extent of that can depend on the type of memory you're looking at. Prices of DDR4 and DDR5 RAM have trebled or even quadrupled in price, though, which is ridiculous, frankly.

In short, I'm loathe to recommend buying RAM at this kind of premium to anyone. That said, Huang's comment here hints that perhaps memory does still have further to go in terms of hikes, and as Tom's Hardware recently reported, a Lexar executive just predicted a further doubling of RAM prices by the end of 2026.

So, in spite of some recent evidence of memory price stabilization or even slight decreases, it seems the worst isn't over yet. At the same time, there's a ceiling as to what consumers will pay in terms of price hikes before they collectively fold their arms and refuse to buy. Overall, it's unclear how this will play out, and while the Lexar exec's prediction feels alarmist to me, it's not a great overall indicator of where we're headed – particularly now in light of what Huang's chipped in with.

I simply can't recommend buying RAM at the price it's at, and the same is true for SSDs in many cases. Indeed, I have to wonder how much worse the prices on these components can get before it becomes farcical, frankly. But for now, I'd err on the side of playing a waiting game to see how these fresher predictions begin to pan out.

GPUs and CPUs: a more positive picture, but not entirely

As for computing components outside of RAM and SSDs, the main concerns are the other key staples of any PC – the processor and graphics card.

If you're in the market for a desktop GPU in the near future, I think it's probably a good move to buy (again, perhaps with a bargain in the next big sale – though I think we're less likely to see any sizeable discounts on graphics cards as opposed to, say, laptops). That's because pricing is relatively palatable currently for mainstream products, meaning mid-range and lower-tier GPUs – we've seen some solid deals on AMD's RX 9070 XT GPUs recently for example.

For high-end GPUs – meaning Nvidia models – sadly price hikes have been a lot heftier (what with more copious amounts of video memory being involved for one thing). It's quite possible these graphics cards could see further cost increases, though, into next year, but the RTX 5070 Ti and upwards are already seriously over and above the MSRP to the point where I balk at recommending them.

CPUs are a different matter, with price inflation being much more restrained here, and with the possibility of processors getting more expensive down the line, I think it's a safe bet to buy one now if you need an upgrade. There's even a new budget Ryzen 7700X3D set to launch next month, and we could see further additions along these lines at the lower-end from AMD apparently, so I think the picture for CPUs looks stable enough for the foreseeable.

The enterprise world is currently witnessing a stark divergence between AI ambition and real-world execution. While the narrative of the last eighteen months has been dominated by the transformative potential of Large Language Models and Generative AI, the operational reality for many businesses is a "Proof of Concept graveyard".

Organizations are making significant investments but are frequently failing to realize the expected return on investment.

To understand why these projects are stalling, we must look past the software and the user interface. The bottleneck is not a lack of imagination; it is a profound foundational fragmentation occurring at the infrastructure level.

As we move from the era of experimentation into the era of implementation, the industry is discovering that the "plumbing" of AI is far more complex than the applications themselves.

The false start and the public cloud paradox

For many organizations, the initial move to the public cloud provided a logical and low-barrier entry point for AI experimentation. The ability to quickly spin up instances of compute power allowed for rapid prototyping without the need for immediate capital investment.

However, as these workloads transition from small-scale testing to full-scale production, the limitations of this model have become a significant financial and operational burden.

Public cloud infrastructure is inherently flexible in the short term, but it often introduces unpredictable costs that impact long-term financial planning. When AI workloads are run at scale, the "metered" approach to billing can lead to budget "shocks" that cause boards to pause or even cancel projects.

Furthermore, the concept of data gravity is becoming a primary concern. This is the idea that as datasets grow, they become increasingly difficult and expensive to move.

Massive proprietary datasets are effectively being locked into hyperscaler ecosystems. When you factor in egress fees and the latency penalties associated with moving data back and forth from the cloud, real-time processing becomes nearly impossible.

This creates a physical separation between the data and the compute power, leading to architectural bottlenecks that stifle performance and prevent the business from seeing real results.

The three pillars of fragmentation

The current gap between investment and ROI is fueled by three specific areas of fragmentation that most enterprises are currently unequipped to handle internally.

1. Fragmented data and the sovereignty crisis Organizations today struggle to unify data that is siloed across different regions, departments, and regulatory jurisdictions. As residency and sovereignty requirements tighten globally, the ability to train and deploy models where the data actually resides is becoming a prerequisite for success.

We are seeing a major shift towards the requirement for Sovereign AI. This is the need for organizations to maintain total control over their data, their intellectual property, and the physical location of their models.

Without this level of control, an enterprise risks not only regulatory non-compliance but also the potential exposure of sensitive intellectual property. If the infrastructure does not support sovereignty, the project is often doomed before it leaves the pilot phase.

2. The specialized skills gap AI requires a highly specialized intersection of data science and systems architecture. It is no longer enough to have a generalist IT team managing these environments. Successful AI deployment requires expertise in high-throughput storage, low-latency networking, and power heavy GPU distribution.

Many enterprises find themselves with the right software tools but without the deep technical knowledge required to optimize the entire stack. This leads to what we call "accidental architectures" which are inefficient and fragile.

These systems are ultimately unable to support the throughput required for production-level AI, leading to performance degradation that kills the business case for the technology.

3. Infrastructure complexity and the rack-level challenge Building a production-ready AI environment is no longer just about buying individual hardware components. It is about validating a complex ecosystem at the rack level.

From high-density power management to liquid cooling integration and multi-node GPU clustering, the physical requirements of AI are immense. When these components are sourced and managed in a fragmented way, the risk of architectural failure increases.

Many projects fail during the critical transition from a controlled sandbox environment to a mission-critical production environment because the underlying infrastructure simply cannot scale at the same pace as the data.

The economic barrier of procurement

A significant and often overlooked factor in the Proof of Concept graveyard is the economic mismatch between how AI is built and how it is paid for. Traditionally, enterprise infrastructure required massive upfront Capital Expenditure.

In the fast-moving AI landscape, committing millions to hardware that may be superseded in two years is a risk many Chief Financial Officers are unwilling to take.

Conversely, the Operational Expenditure model of the cloud, which seemed attractive for experimentation, becomes prohibitively expensive when used for constant, high-intensity workloads.

The industry needs a middle ground. It requires the economic predictability and physical control of on-prem infrastructure, combined with the cash-flow flexibility traditionally associated with cloud consumption.

Until the channel can offer staged deployment approaches and financing models that align infrastructure costs with actual workload adoption, the ROI gap will remain wide.

Moving beyond the sales pitch

To bridge this gap and rescue projects from the graveyard, the industry approach to AI delivery must evolve. The traditional model of fragmented hardware procurement where a business buys a server here and a storage array there is insufficient for the demands of modern AI.

We are moving towards a requirement for unified ecosystems. This means a shift away from flashy sales pitches and towards rigorous technical validation. The industry needs environments where organizations can test their specific workloads on validated stacks before a single pound of capital is committed.

This requires a new level of collaboration between hardware vendors, specialized AI consultancies, and infrastructure integrators. The goal must be to de-risk the process by proving the outcome before the investment is finalized.

The path forward: AI as a utility

The transition from AI as a trend to AI as a utility requires a fundamental rethink of the technology stack. Sovereignty, performance, and economic predictability must be the three metrics by which success is measured.

For AI to truly deliver on its promise, organizations must be empowered to run their models where their data, policies, and priorities dictate, rather than where a hyperscaler decided to build a data center.

By addressing these foundational infrastructure challenges and moving towards a more collaborative, validated ecosystem, we can finally move past the era of experimental failures. Only then can we build a sustainable, ROI-driven AI future that delivers genuine value to the enterprise.

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• Nvidia's rumored RTX 5000 Super refreshes are seemingly back on track, and could be out this year
• As well as the previously rumored variants, we could also see an RTX 5060 Super with 12GB
• This goes against existing rumors insisting that Nvidia isn't producing any new GeForce GPUs at all this year

Nvidia's long-rumored RTX 5000 Super refreshes might just be back on track, according to the latest from the GPU grapevine — and there could be an interesting twist here.

Namely, we could see a lower-tier GeForce graphics card get the Super treatment, as there's, in theory, an RTX 5060 12GB model inbound that could carry this naming.

As VideoCardz flagged, according to MegaSizeGPU, a regular leaker on X, this new refresh and the other previously rumored RTX 5000 models are "back on track" and may be released in 2026.

To recap, those earlier rumors pointed to an RTX 5080 Super and RTX 5070 Ti Super with 24GB of VRAM, and an RTX 5070 Super with 18GB. So, you can possibly add to that an RTX 5060 Super with 12GB. Nvidia has produced xx60-class graphics cards in Super variations before, of course (indeed, I have an RTX 2060 Super myself).

Analysis: is 'Kicker' back in play?

This is an about-face from the previous rumor mill chatter, which had floated the theory that Nvidia wasn't going to release anything in the way of new Blackwell GeForce GPUs this year. Of course, that could still be true as MegaSizeGPU isn't certain about the 2026 release timeframe – but the leaker seems fairly confident that these refreshes (supposedly codenamed 'Kicker') are back on the table for Nvidia.

Still, all of this should be taken with a good deal of seasoning, especially as this is a lone rumor for now. When other leakers back up the idea, we can start to become a bit more ready to believe that Nvidia may indeed have these Super GPUs in the works again. For now, though, I remain highly skeptical.

The reason why Nvidia seemingly shelved the RTX 5000 Super refreshes (and remember, these GPUs have only ever been rumors) is due to the cost of RAM, which is particularly applicable to these models as they pack so much video memory.

So, what changed? Nothing, according to MegaSizeGPU, it's just that Nvidia "can always get what it wants!" and therefore this won't be an issue as such. Team Green will still provide its card-making partners with GPU chips and VRAM bundles, we're told (meaning they won't make those board manufacturers source their own video RAM).

It still doesn't make a huge amount of sense that Nvidia might want to go ahead here, because with the higher-end VRAM configurations, those RTX 5000 Super refreshes are going to end up very costly, there's no two ways about that.

Okay, so maybe Nvidia simply won't make many of them – perhaps Team Green just wants to keep the GeForce range ticking over so gamers can't accuse the company of abandoning them this year (for greener AI pastures). In that case, though, stock will end up thin on the ground, and the GPUs will be potentially even pricier as a result.

We shall see, but if heavyweight RTX Super refreshes are incoming, I expect them to be wallet-damagingly priced. An RTX 5060 Super with 12GB, on the other hand, would be very welcome in helping address one of the major stumbling blocks with this particular lower-end model. Again, though, would Nvidia want what would effectively be a lower-end RAM drain that's (relatively) not very profitable? Again, time will tell...

In February 2026, Moltbook, a social network built for AI agents, learned a familiar lesson in a new way: you can’t scale AI without building the right infrastructure underneath it.

Despite rapid traction and heavy funding, the company rushed into production with parts of the stack generated by AI agents, without fully validating the infrastructure.

The cracks showed quickly. Researchers found fundamental gaps in how data was stored, accessed, and protected, allowing unauthorized posting and exposing sensitive user information.

Vibe coding, no doubt, has applications in today's software world. However, Moltbook had received millions in venture capital money and has since been acquired by Meta. This wasn’t an experiment; it was a production system handling real user data.

And that’s the point: once AI moves from prototype to production, shortcuts in architecture across compliance, data privacy, and auditability become real business risks.

The lesson here is about more than security, it’s about how AI is raising the cost of getting data infrastructure wrong.

The growing challenge in AI infrastructure

AI agents don’t just use data, they’re constantly creating it, moving it, and acting on it at a scale most systems weren’t built for. And now that foundation models are widely available, the model itself isn’t what sets companies apart anymore.

The real advantage comes from the infrastructure behind it. For IT leaders, that shifts the risk from theoretical to operational: broken pipelines, gaps in compliance, and security vulnerabilities as data moves through the system.

The challenge is architectural. AI workloads don’t stay in one place, they move across on-prem environments, the cloud, and the edge, with machines authenticating and interacting with each other nonstop in the background.

And with AI projects going from experiment to production so quickly, teams are being forced to rethink how data actually flows: how fast it moves, how reliable it is, how it’s governed, and where it should live.

That’s why many are starting to move back toward on-prem and hybrid models, designed around where data naturally sits, rather than what’s most convenient in the cloud.

It’s at scale where things start to break down. Caches go stale, data formats don’t line up, and pipelines that worked in testing begin to strain, dragging down performance and creating inconsistent user experiences. Behind the scenes, access controls built for human behavior don’t hold up in a machine-driven world.

Governance rarely fails all at once; it slowly slips as teams focus on improving models instead of maintaining the data layer. And over time, that leads to something much riskier: a data foundation that’s increasingly messy, exposed, and out of compliance.

Why storage and governance must be foundational

AI initiatives at enterprise scale don't fail because of models, they fail because the data supporting them isn't ready. Training pipelines stall when data storage can't sustain the throughput GPU clusters demand.

Inference degrades when feature data is scattered across siloed environments with no consistent access layer. Governance breaks down when there is no single authoritative record of what data exists, who accessed it, and under what policy.

The consequences of treating storage as an afterthought are structural, and this is a pattern seen repeatedly across large financial institutions from my personal experience.

When the data under AI workloads lives in fragmented silos spanning on-premises systems and cloud environments, every AI practitioner becomes an integration engineer first. Teams spend cycles moving data instead of working with it. Compliance teams chase lineage across systems that were never designed to provide it.

Risk surfaces multiply precisely where visibility is lowest, in the data path between raw objects and the models consuming them, creating operational and regulatory exposure.

For regulated industries such as financial services and healthcare, making storage the governance layer, not a separate concern bolted on afterward, is the only solution.

With policy enforcement, encryption, and IAM controls embedded in the data path across both object storage and industry standard table formats, AI practitioners gain self-service access without sacrificing auditability or control.

Structured and unstructured data are governed under a unified platform, which means compliance teams have consistent lineage and access records whether the workload is model training against raw objects or analytics running against Apache Iceberg tables.

The data under every AI initiative becomes a controlled, observable, high-performance foundation.

What leaders must prioritize now

As organizations move from experimentation to production, the data layer becomes the factor that determines whether AI actually scales. It’s a subtle shift, but an important one: success is no longer defined by the sophistication of the model, but by whether the infrastructure around it can support real-world demands.

That means:

• Treat storage as a strategic decision, not a backend concern. Data integrity, governance, and performance are architectural requirements, not supplementary support. High-performance, S3-native object storage that can keep pace with GPU clusters and AI pipelines is now table stakes.
• Design for AI agents as primary data consumers. Autonomous systems depend on fine-grained access control and full auditability for machine-to-machine interactions.
• Stay cloud-flexible without becoming cloud-dependent. The ability to run consistently across on-premises, hybrid, and multi-cloud environments without egress penalties or lock-in is what gives organizations real control.
• Eliminate the silos AI exposes. Bringing governed, SQL-accessible structure to the same platform as raw object data closes the gaps where visibility and control tend to break down.

Autonomous, production-scale AI is within reach, but it’s only as strong as the foundation beneath it. Deploying AI tools without enterprise-grade data infrastructure isn’t bold, it’s a liability. The organizations that treat the data layer as foundational will be the ones that scale AI safely, reliably, and for the long term.

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• Nvidia just announced its Q1 fiscal 2027 results
• This came with a change in the way GPU sales are reported
• They won't be detailed separately any more, but buried in another category — Edge Computing — and there are reasons to be nervous

Nvidia is flying with the revelation of its latest financial results, hitting a record-breaking quarter. But hidden among the trumpeting of success there was a move which I find somewhat disturbing regarding Team Green's gaming GPUs.

Tom's Hardware noticed that aside from the record revenue in Q1 fiscal 2027 – which hit a staggering $81 billion – Nvidia is making a change to the way the company reports its financials going forward.

From this quarter and in the future, Nvidia won't separately report sales of client graphics cards, meaning consumer (GeForce) and professional (RTX Pro and others) GPUs. Instead, sales of those graphics solutions will be absorbed into another bigger category: Edge Computing.

So, Nvidia will have just two main categories with its financial reports: Data Center (which encompasses cloud, AI and supercomputing) and Edge Computing (which is PCs, workstations, consoles, as well as robotics, automotive and telecoms). We won't get a breakdown of sales of graphics solutions at all.

Analysis: shifting priorities

All this sounds mightily dull, of course, so why does it matter to gamers? And why might it worry them, more to the point? Well, what it means is that it'll no longer be possible to see how the GeForce and RTX side of Nvidia's business is performing.

In short, it's effectively draping a cloak of obscurity (I found one in Baldur's Gate 3, I think) over Nvidia's graphics revenue so nobody can easily see how this side of the business is doing.

Of course, this is a reflection of a few things: most certainly that Nvidia has become an AI juggernaut. And also that all investors really care about is AI now, and Team Green doesn't feel that graphics are important enough to report directly. The various RTX graphics cards that Nvidia sells — whether GeForce, or non-consumer RTX models — can just be filed away quietly, in the background.

The worry for me is that this is also a way of keeping graphics sales out of the limelight if Nvidia is going to deprioritize its GeForce lineup going forward. With no visibility in the financial reporting, there's going to be no easy way to spot gaming GPUs dwindling.

That may seem like a leap to a conclusion, but it isn't just this latest move with the financial reports to consider — there's also Nvidia's wider attitude towards GeForce in recent times. With the RAM crisis, we've seen GPU price hikes and concerns over production and stock. On top of that, there have been rumors of GeForce models that were supposed to be released: RTX 5000 Super refreshes, which were heavily rumored, and supposedly designed and readied, but have now been shelved.

The chatter from the grapevine is that we won't see any new GPUs from Nvidia this year at all — not one — and that's very rare (in fact it hasn't happened in three decades). This is because Nvidia needs all the chips it can get — and perhaps more to the point, all the video RAM — for AI graphics cards, which are far more profitable than consumer models.

And let's not forget that Nvidia's keynote at CES 2026 — a show that's about consumer electronics — didn't mention anything to do with GeForce GPUs. (Not the hardware, anyway, although we did hear about DLSS 4.5, but that's not quite the same — the only hardware touched on was gaming monitors).

There's an increasing feeling among gamers that priorities are shifting more radically towards the AI side of the market for Nvidia, and away from gaming GPUs, and I can't blame people for thinking this way. This latest move to bury graphics sales in Nvidia's financial reports feels like another step on this path of marginalizing the GeForce family, and yes, I agree, we can't be jumping to conclusions, but it's all adding up and feeling rather ominous to me.

• NVIDIA releass new GPU driver updates for Windows and Linux
• Fixes 14 vulnerabilities across GeForce, RTX, Quadro, Tesla, NVS, vGPU, and Cloud Gaming software
• The most severe flaw, CVE‑2026‑24187, is a use‑after‑free bug rated 8.8/10 that enables code execution, privilege escalation, data theft, or system crashes

Nvidia has issued a new patch for its GPU display drivers, fixing 14 vulnerabilities in both Windows and Linux.

The majority of the flaws are labeled as “high-severity”, which is why Nvidia urged its users to apply the fixes without delay.

In a newly released security advisory, Nvidia said it fixed bugs in GeForce, RTX, Quadro, Tesla, and NVS product lines, as well as its vGPU and Cloud Gaming software.

How to patch up

The biggest vulnerability to be fixed is a “use-after-free” memory flaw, now tracked as CVE-2026-24187. With a severity of 8.8/10 (high), this bug allows threat actors to execute arbitrary code, steal data, escalate privileges, and even crash entire systems.

Several other high-severity vulnerabilities allow malicious actors to do similar things, from code execution to privilege escalation, and from data tampering to information disclosure.

One affects both Windows and Linux systems through improper access to GPU resources in the kernel layer, while another targets Windows specifically through a timing flaw that could be exploited to manipulate system operations.

Among the fixed flaws are two in Nvidia’s Unified Virtual Memory subsystem on Linux, which could lead to denial-of-service attacks without the need for elevated permissions. Nvidia's vGPU software, used in virtualized and cloud environments, also received patches for two vulnerabilities in the virtual GPU manager component.

Users can now download the updated drivers either through the Nvidia Driver Downloads page, or through the Nvidia Licensing Portal, depending on which products they’re running. Windows users should look for the driver version 569.49 and newer, while those running Linux, version 590.48.01.

Besides making sure the software is patched, users should also always keep their Windows Defender (or other antivirus programs) up and running at all times.

The company credited several external security researchers for responsibly reporting the flaws, including researchers from Seoul National University and Binarly Research Team.

• This brand new perception chip supports real-time awareness at huge efficiency
• Always-on tracking could also improve smartphone camera performance
• Object recognition, positional tracking and scene understanding also supported

Swiss semiconductor startup Mosaic has successfully raised $3.8 million in funding to build super-efficient so-called perception chips, which would be installed across smart glasses and other wearables to bring more powerful processing into smaller form factors.

Company co-founder and CEO Alfio Di Mauro argues that “spatial intelligence shouldn’t require an application-class processor and a GPU,” hence the inception of Mosaic SoC, which aims to deliver “real-time perception at a fraction of the energy.”

Mosaic says its “next-generation perception chips” will address some of the current bottlenecks wearable makers face today, including battery size, heat output, device thickness and engineering complexity.

Are perception chips the answer to powerful yet slim wearables?

While cameras and sensors continue to improve, manufacturers are under increasing pressure to add more compute and processing power inside devices while maintaining (or even decreasing) form factor.

The perception chip is said to be able to provide real-time environmental awareness, object recognition, positional tracking and scene understanding while consuming only a fraction of the power a conventional smartphone-class compute stack would consume, making it ideal for future AR glasses and headsets.

“The Mosaic SoC chips are designed to be small enough and efficient enough to make smart glasses indistinguishable from regular glasses, while still delivering full spatial awareness,” the company declared in a press release.

Early smart glasses like Ray-Ban Meta and Oakley Meta devices currently lean heavily on smartphone pairing to get the most out of the technology they provide because the physical constraints of size and weight prevent them from being able to install the relevant hardware. On the flip side, Apple’s Vision Pro often draws criticism for its sheer weight (750-800g), which makes it uncomfortable to wear for longer periods.

However use cases extend far beyond just glasses – in smartphones, perception chips could power always-on tracking and classification to deliver continuous awareness with minimal impacts on battery life.

Extra power and big goals

Where Mosaic’s perception chip differs from others is that, where others rely on single- or dual-code ARM-based designs, this one uses a proprietary design and a multi-core architecture of eight or more cores, which maximizes performance per watt to back up Mosaic’s bold efficiency claims.

Mosaic also claims to remove original design manufacturer (ODM) complexity, shipping its chips with a full application layer developed and maintained by the startup.

While the company has already achieved “meaningful revenue” through NRE contracts with ODM partners, future plans revolve around far more than being a chip provider. This startup has big visions to become a platform supplier, where applications are developed specifically for its silicon.

“The next billion smart devices will see and understand the world around them,” Antonia Albert, an investor with leading Swiss pre-seed fund Founderful, commented. “Mosaic SoC's product is the chip that makes that possible at scale.”

• AMD has announced its Q1 results, with booming revenue driven by AI
• There's bad news for the gaming division, though, due to 'higher memory and component costs, ' AMD's CEO Lisa Su observed
• AMD's CFO has forecast 'gaming revenue to decline by more than 20%' in the second half of 2026 compared to the first half of the year

AMD just revealed its latest financial results, with good news for investors in the form of a major surge in revenue, but bad news for consumers, with more RAM-related worries looming on the horizon.

Tom's Hardware reports that AMD's Q1 2026 fiscal results witnessed a new record for data center revenue, as the AI boom drove further growth, but CEO Lisa Su warned of PC component price spikes going forward.

Su predicted that demand is going to wane with its client and gaming businesses – essentially the consumer side of AMD's hardware – in the second half of 2026 due to "higher memory and component costs".

So, yes, that means AMD's chief executive believes that after next month, as we head into Q3, RAM and other components are only going to get pricier.

With gaming, the damage done by price hikes could be quite considerable, as AMD's Chief Financial Officer, Jean Hu, observed: "We expect second half [of 2026] demand in gaming to be impacted by higher component and memory cost. We now expect second half gaming revenue to decline by more than 20% compared to the first half."

Analysis: Radeon price hikes – or indeed pricier consoles?

In other words, compared to the first half of the year (of which less than two months now remain – with time flying by, as ever), the second half of 2026 is going to be considerably more sluggish for AMD's gaming revenue. The expectation isn't just a 20% drop, but a more than 20% fall, so that could be a quarter less money raked in, or maybe more, up towards 30%, even.

This would seem to indicate that AMD's Radeon graphics cards are going to be in shorter supply in Q3 and Q4, and that there may be further price hikes on RX 9000 models. Clearly, AMD is expecting things to slow down with these graphics cards as 2026 rolls on, but its gaming revenue isn't just about Radeon, of course – Team Red also makes the semi-custom GPUs for the PlayStation and Xbox consoles.

Sales of those consoles are softening naturally, mind you, given that they're in the later stages of their expected lifespan now, so we're reaching saturation levels for would-be buyers. What could also be factored in here is the price hikes for console hardware, causing further unwillingness to buy – or possibly, AMD is anticipating further PS5 or Xbox price rises later this year, compounding the misery.

That's just guesswork, but clearly the outlook isn't great for the second half of 2026, and this is the latest in a quickfire round of pessimistic RAM crisis predictions, two of which have come from memory chipmakers themselves. Micron has warned of growing AI demand and more pressure on RAM supply, while Samsung has observed that 'significant shortages' of memory will continue to plague us through 2027 as a best-case scenario.

There isn't much faith out there in Reddit-land that RAM pricing will recover anytime soon, or indeed that prices will ever reach the levels we saw last year, before the memory hikes started coming thick and fast.

As one Redditor put it in reaction to this news from AMD: "My prediction is that over 2027 prices will drop from an insane 400% [price increase] to a nice and summerly 200%. But they'll never fall lower than that ever again. The RAM economy will permanently change and data centers will be a nuisance for the rest of recorded time."

• A PC modder just built a room-sized gaming PC, fit with an air conditioner and a sauna
• The gaming PC has hardware components larger than a human
• The RAM sticks are props that are estimated to be 18,000GB of RAM each

Gaming PC builds come in all different shapes and sizes, either targeting improved airflow and temperatures or prioritizing desktop space — but this one takes the cake.

As reported by VideoCardz, a modder known as Soda Baka on Bilibili, has built a gaming PC the size of a room, with gigantic fans, a large GPU, CPU, and an AIO cooler. These are accompanied by an air conditioning unit to help control room temperatures and a literal 12,000W sauna with added water to help simulate the heat of a regular-sized gaming PC.

It's worth noting that the RAM sticks in this build are only props, and that's no surprise, since the estimated capacity based on their scale is 18,000GB of RAM each. That would already cost a fortune on its own, but we're in the middle of a RAM crisis, so I couldn't even imagine how costly each RAM stick would be.

Baka's project is easily one of the most ludicrous PC building projects out there, and has almost no real-world use case, as I struggle to imagine anybody has the resources readily available — or better yet, any reason to build a system like this, but it's great for aesthetics.

It's quite an amusing solution to summer heat (minus the sauna), with a significant amount of room for a gaming PC's operation and the addition of an air conditioning unit.

However, in a more practical and reasonable use case to help keep temperatures cool, it's best to simply invest in an AIO water cooler for the CPU, and look toward undervolting the GPU where possible. I've done exactly that with my main gaming PC build, and temperatures for both my RTX 4080 Super and AMD Ryzen 7 5700X3D — and both max out at 65 degrees Celsius.

That's easily achievable, without building a room-sized gaming PC, but again, it is a perfect themed environment for any gamer.

Offering speed, flexibility, and the ability to scale without heavy upfront investment, the public cloud has for years been the model of efficiency. But as AI becomes embedded across every function of organizations, what once seemed like convenience now looks a lot more like a permanent cost burden.

That’s why many businesses are shifting from a cloud-first mindset toward a more balanced, hybrid approach, one that sees AI workloads brought back on-premise

Cloud used to be a major cost saver, but in 2026, the economics are changing quickly. Ingress and egress fees, combined with the premium charged for GPU compute cycles, have ballooned as more AI models run.

When 10% of top-line revenue goes to a cloud provider just to keep the lights on, organizations feel like they’re not simply renting infrastructure but paying a recurring tax on their own growth.

This is the state of play with the always-on nature of today’s AI models.

Frequent, high-volume tasks are driving cost increases. Enterprises are now using large language models (LLMs) to summarize internal meetings, scan customer support tickets, and run continuous retrieval-augmented generation (RAG) pipelines.

Individually, these API calls seem inexpensive. But at scale, they are a massive recurring expense. AI agents bring more complexity. These systems function more like digital employees, planning tasks, verifying outputs and retrying workflows.

From renting to owning

With public cloud pricing models, the more a team relies on AI, the more an organization pays. In other words, there’s a tax on realizing AI’s full potential.

On-prem infrastructure turns that upside-down. A one-time investment in high-performance hardware converts unpredictable monthly expenses into fixed, depreciable assets. Companies own the computing capability outright rather than paying exorbitant rent.

The cost of local hardware is often recouped quickly when compared to ongoing API usage or GPU rental fees, particularly for predictable, always-on workloads.

But cost is just part of the equation. Performance is the other.

In the cloud, workloads typically run on shared infrastructure. Organizations often operate on a “slice” of a server alongside other tenants, introducing latency, resource contention, and performance variability.

By contrast, local AI runs on dedicated hardware. There is no network lag, no shared queues, and no “noisy neighbor” interference. For end users, that translates into immediate responsiveness.

The governance imperative

Data sovereignty is another driver of the on-prem trend.

In a public cloud environment, sensitive data resides on third-party infrastructure, creating challenges for compliance, auditing, and intellectual property protection.

On-prem AI changes that dynamic. Prompts, proprietary training data, and outputs remain within the organization’s physical and logical boundaries. Compliance with frameworks like GDPR or HIPAA becomes more straightforward because data residency is guaranteed by design.

This also addresses growing concerns around “prompt leaks.” When employees input sensitive information into external AI systems, there is a risk of unintended persistence or exposure. Localized AI environments create a controlled, secure environment for experimentation and deployment.

Smaller, more efficient models are making this possible. Businesses do not need hyperscale infrastructure for every use case.

That’s why we are beginning to see the “rightsizing” of AI. Capable assistants can now run on systems with 64GB or 128GB of high-speed memory. What once required a large, expensive server can now be done with a compact, cost-effective workstation.

Hybrid model

This transition to on-prem AI does not mean abandoning the cloud.

For most forward-looking businesses, the right solution is a hybrid model. Cloud can be used more strategically, reserved for large-scale training jobs and burst workloads that require massive, synchronized GPU resources.

At the same time, local infrastructure handles agentic AI programs, internal copilots, and sensitive data analysis.

As a strategic hub rather than a peripheral, companies can build environments that are faster, more secure, and more cost-efficient than a cloud-only approach.

They can attain full control over their data, eliminate hidden costs such as egress fees, and offer their teams a better experience.

In the future, we will see one person directing a team of agents, and in an enterprise, hundreds or even thousands of agents may continuously plan, call tools, share context, verify results, and retry tasks — all of which drive token usage sharply higher. This is a fundamental shift in how AI is used.

Collectively, these trends point to the emergence of a “private AI” model.

The shift from cloud-first to hybrid and on-prem AI is being driven by a convergence of forces: economics, governance, and performance. In 2026, the question is no longer whether to use the cloud, but how to use it strategically while keeping control over the workflows that matter most.

We've featured the best cloud computing provider.

This article was produced as part of TechRadar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit

• Fakes are now reaching a very high standard
• An Asus ROG Strix RTX 4090 proved to be indistinguishable from a genuine Nvidia GPU on first inspection by a repair expert
• There's a worrying level of sophistication here, and we've also seen this in recent times with fake SSDs

A fake Asus ROG Strix RTX 4090 is an example of just how authentic scam hardware can seem, and it's a worrying reminder given that this kind of fraud is very much on the rise of late.

VideoCardz reports that Northwest Repair (a Kentucky-based repair shop in the US) highlighted the incident with a YouTube video (which you can see below).

As Northwest Repair (NR) points out, the color of the board didn't look at all faked, and overall, it appeared to be a factory-made, genuine Nvidia graphics card. Typical clues like missing thermal compound around the corners of the GPU chip — which would indicate the chip had been removed (and then switched out) — were not present.

On first inspection, the only discernible clue was the thermal compound being somewhat darker than normal (or more "baked"), which NR picked up on, but noted that this could be due to the RTX 4090 being used for some serious overclocking, perhaps — it's not necessarily a telltale sign of a fake.

Even the laser-engraving of the model number on the chip looked authentic, and the same was true for the video memory.

In short, this was a carefully crafted fake GPU which, as NR makes clear, is made to a standard that the repair expert hasn't witnessed before. "This is the best scam I've ever seen," he observed, noting, "We've reached a point where the scam has gotten so good that even the trained eye cannot detect it."

Only examining the board under a microscope, in fine detail, revealed the small clues (related to soldering mostly) that gave away that this was a fake.

Analysis: a reminder that we need to be ever more vigilant

The advice NR gives is clear: don't buy an expensive flagship GPU second-hand, as these days, there's too much of a risk that someone could be trying to con you out of a lot of money. The only exception is if you're buying the graphics card from a friend you can trust.

Fakes also cast doubt on the practice of buying 'open box' returns, because the buyer who returned that hardware could have switched it out for a fake — one that stands up to inspection (but not to testing, though the retailer won't necessarily check that).

The sad reality of inflated prices for all PC components is that we're seeing a rise in hardware scams targeting this market. That includes graphics card scams, and incidents where a third-party marketplace seller doesn't even bother to include a fake GPU, but just a weighty 'dummy' item instead (a packet of laundry detergent, for example, or more commonly a rock or lump of metal).

What you should be careful of most right now, though, is scams relating to RAM and SSDs, where prices have spiked to ridiculous levels, tempting fraudsters to try their hand more with these components. As well as this highly sophisticated fake of an RTX 4090, we've seen some very convincing counterfeit SSDs of late — here's what you need to know in terms of watching out for them.

The level of scamming that's been seen this year has even prompted action from RAM manufacturers — Corsair has introduced new packaging in an attempt to make life more difficult for criminals who are trying to rip off consumers.

• Reuters has leaked details of SpaceX's plans ahead of its IPO
• They include "manufacturing our own GPUs"
• Whether that means chips in the current line of Tesla AI processors, or a new category of GPU or AI accelerator is unclear

SpaceX is apparently set to make its own GPUs, based on insights gleaned from its S-1 form ahead of the company's IPO, although these won't be products you'll be installing in your PC.

Tom's Hardware picked up the report by Reuters, with the news site claiming to have seen the S-1 form, which is filed by companies going public. SpaceX is expected to be worth in excess of $1 trillion – and probably closer to $2 trillion – when its shares are available on the stock market (next month, in theory).

Part of the form explains major spending plans to develop AI, which includes a mention of "manufacturing our own GPUs" in the listed 'substantial' capital expenditures.

As noted, these wouldn't be a rival for the likes of Nvidia's GeForce GPUs, but rather graphics boards built for AI tasks, commonly known as AI accelerators.

Nvidia makes those solutions, as well, of course – as does AMD – but SpaceX would seemingly rather have its own homegrown AI accelerators, because as noted in the S-1 filing, it does not have "long-term contracts ​with many of our direct chip ​suppliers". And clearly, that's going to be problematic given the current RAM crisis and its knock-on effects.

What isn't clear is whether SpaceX's mention of 'GPUs' in the filing could more broadly refer to Tesla's AI processors, which are currently on the AI4 generation, with the next-gen AI5 set to provide a big leap in performance (of a factor of 40x, it's claimed). It's possible that the company may be referring to this series of AI chips, rather than an entirely separate line of GPUs for AI tasks.

When it comes to making these chips, presumably the 'Terafab' project – a huge chip manufacturing center in Texas which involves SpaceX, xAI and Tesla – is going to be involved, even though GPUs haven't been mentioned directly in connection with this facility.

Analysis: AI5 or something else entirely, it won't be in your PC

At least to me, this does read more like a play for a new product, as opposed to a mention of existing AI chips and calling them 'GPUs' informally. That said, looking at the bigger picture, as Tom's points out, Elon Musk has referred to Tesla's AI5 chips as GPUs, and even noted that it "basically is a GPU" – and so you can see where the confusion comes in. All this could simply boil down to a liberal use of the term GPU, and mean precisely nothing.

At any rate, for the average consumer, the point to bear in mind is that whatever SpaceX is up to on the GPU front, if anything, it won't be relevant to the everyday computer user. This is all about silicon designed to drive AI performance specifically, and whether it's technically an AI chip in the existing line, or something different in terms of a new GPU accelerator designed for AI tasks, is all semantics – it won't be a graphics card as such (as in one designed to process 3D graphics).

Any confusion aside, the overall aim for Musk is clear enough, and that's to ensure AI chip supply by producing them himself, given the mentioned lack of 'long-term contracts' with chip makers.

How comforting this leak will be to would-be investors in SpaceX remains to be seen, but Musk appears to have some grand plans here, and not for the first time.

• Most AI GPUs run at shockingly low utilization across production systems
• Companies are paying for twenty times more GPU capacity than needed
• Overprovisioning is rising sharply instead of improving year after year

Companies across the tech industry are racing to buy massive amounts of AI infrastructure, but most of it does barely any useful work at all.

A report from Cast AI, based on tens of thousands of Kubernetes clusters across AWS, Azure, and GCP, found that average GPU utilization sits at just 5%.

Many teams deploy sophisticated AI tools to manage their applications, yet those same tools are not used to optimize the underlying infrastructure.

The numbers are getting worse, not better

Organizations pay for roughly 20x more GPU capacity than their workloads actually use at any given moment.

The numbers come from direct measurements of production clusters and millions of compute resources before any optimization was applied.

"This is the third year we've published this report. The numbers are worse," said Laurent Gil, co-founder and President of Cast AI. "CPU utilization fell to 8%, down from 10%. Memory dropped from 23% to 20%."

The report also measured something called overprovisioning, which is the gap between what workloads actually need and what teams allocate to them.

CPU overprovisioning rose from 40% to 69% year over year, while memory overprovisioning now stands at 79%.

This means organizations reserve nearly twice as many CPU resources and four times as much memory as their workloads actually consume.

In short, organizations pay for infrastructure that their workloads do not even request, and the trend is accelerating instead of improving.

The situation gets even more expensive when comparing CPU and GPU costs directly. A CPU core sitting idle costs only cents per hour, but a GPU sitting idle costs dollars per hour.

For the first time since EC2 launched in 2006, GPU prices are rising instead of falling.

In January 2026, AWS raised H200 Capacity Block prices by 15%, citing supply and demand, which broke a two-decade precedent.

"At 5% utilization, the math doesn't work," the report states. The hoarding instinct makes sense because lead times are long, yet that same hoarding feeds the scarcity loop that drives prices even higher.

Not every cluster performs this badly, and one organization hit 49% utilization on H200s and 30% on H100s, well above the 5% average.

The difference comes down to automation rather than luck or better hardware. The tools to fix this already exist, including automated rightsizing, GPU sharing or time slicing, and Spot management.

However, most teams never get there because overprovisioning feels safer than running out of capacity, but that safety comes at a steep price.

The teams that closed the gap stopped treating resource efficiency as a manual, one-time task and started treating it as an automated, continuous process.

But Cast AI data reveals that most companies seem willing to keep paying large fees rather than change their habits.

I’ll be honest: despite many tech companies trying to convince me otherwise, artificial intelligence has left me cold (if not downright outraged when Google’s AI overviews steal my content) — but there’s one AI feature that I use almost every day: Nvidia’s DLSS, which uses artificial intelligence to upscale video game graphics, reducing the stress on compatible graphics cards, and essentially allowing for more impressive graphical effects and results without the hardware holding them back.

When it comes to flagship GPUs like the RTX 5090, it means achieving the kind of results (such as 8K resolutions at 60fps) that would have once been impossible, but it also means more affordable mid-range and budget GPUs can offer the kind of performance that used to demand more expensive GPUs.

Since its inception in 2019, Deep Learning Super Sampling (DLSS) has continued to improve, with its latest version, DLSS 4.5, focusing primarily on frame rate improvements that aim to match the frames per second (fps) your GPU can achieve with the native refresh rate of your monitor.

Having a monitor with a refresh rate of 120hz, for example, and a frame rate of 120fps, will give you a fast and responsive gaming experience with (hopefully) no instances of screen tearing or stuttering, two big visual problems that can occur when the frame rate and refresh rates don’t match exactly.

DLSS 4.5 attempts to rectify that by bringing up to six times frame generation, as well as a new Dynamic Multi Frame Generation feature.

The pros and cons of Multi Frame Generation

Frame Generation, which was introduced in 2022 as part of DLSS 3.0, was — before DLSS 5’s announcement — arguably the most controversial part of DLSS. It uses AI to generate additional frames between rendered frames, helping to improve or stabilize frame rates. Essentially, if a GPU was hitting 20fps, Frame Generation could boost this to 40fps by inserting a generated frame after each rendered one.

To be honest, I wasn’t too impressed when Frame Generation first debuted. While the generated frames were supposed to be indistinguishable from the rendered frames (as they were generating frames based on the rendered frames, and you’re likely seeing lots of frames every second), I found that it made the overall look of certain games (I tried it with Cyberpunk 2077) feel rather soft and blurry.

The introduction of Multi Frame Generation (MFG) with DLSS 4.0 allowed for up to three frames to be generated per rendered frame, potentially increasing the frame rate by up to four times, while minimizing the impact on the GPU (as generating a frame is less intensive than rendering a new one). I was initially worried about this due to my experience with the first generation of Frame Generation, as generating more frames per second would make any issues more noticeable.

However, I was pleasantly surprised by the improvements Nvidia had made to Multi Frame Generation, as I found the impact on graphical fidelity to be much less noticeable (if at all), while frame rates drastically increased. Of course, this does depend on the game, level of Multi Frame Generation support, and your own subjective experience, but what I liked most of all about Multi Frame Generation was that it allowed you to determine how many frames (if any) were generated compared to rendered.

So, while I got my RTX 5090 to hit ridiculous 200+ fps in Cyberpunk 2077 at 4K with path tracing on, because my TV has a refresh rate of 120Hz, I could tweak Multi Frame Generation, lowering the number of generated frames until I hit a rock-solid 120fps.

Combined with improvements to upscaling quality, DLSS was fast becoming an impressive bit of tech, especially for people who couldn’t afford flagship GPUs.

However, controversy was brewing amongst PC gamers, mainly focused on the idea of ‘fake frames’, with some naysayers dismissing the generated frames and arguing that relying on DLSS and MFG could lead to game developers not bothering to optimize their games for PC.

I didn’t really buy that argument — for me, personally, I was finding it increasingly difficult to differentiate between rendered and generated frames, though there were still times when visual glitches and artifacts gave the game away.

Also, because DLSS is exclusive to Nvidia GPUs (and the most recent RTX models), game developers would still need to optimize their games for Intel and AMD hardware, not to mention consoles.

In fact, DLSS’ success saw AMD and Intel (plus others) come up with their own alternatives (AMD FidelityFX Super Resolution (FSR) and Intel XeSS) — and in my view, any technology that inspires or forces competitors to innovate and up their games is broadly positive.

Courting controversy

Aside from some, in my mind, easily dismissed complaints in some quarters about ‘fake frames’, over the past few years, DLSS has quietly been impressing me, and has become an AI tool that this AI sceptic uses almost daily.

I’m not the only one, either. According to numbers Nvidia shared early last year, 80% of PC gamers who have an Nvidia RTX graphics card (which, let’s face it, is a large majority of PC gamers due to Nvidia’s market lead) play with DLSS turned on, and as the technology develops, and an increasing number of games support it, I assume that number has only grown.

Still, the launch of DLSS 4.5 wasn’t without its risks, especially as one of the key selling points is that MFG can now generate five additional frames per rendered frame (which Nvidia terms 6X). If you had concerns about ‘fake frames’, or found the generated frames particularly noticeable and distracting, then you might not be overjoyed by the thought that with 6X MFG, the vast majority of frames you’re seeing will be AI-generated, rather than rendered by your GPU.

However, DLSS 4.5 also introduces Dynamic Multi Frame Generation (DMFG), which automatically changes the number of frames generated to ensure games reliably hit your target frame rate. So, by setting the target at 120fps, for example, DMFG will generate more frames when a scene in a game gets particularly intense, then it will lower the number of generated frames when things calm down, which should give you a solid frame rate without sacrificing image quality too much. It’s kind of what I’ve been doing manually, but with DMFG, it can change the settings on the fly as you play, and it means you don’t have to mess around with settings if you’re one of those weird PC gamers who prefer actually playing games, rather than tweaking.

With extremely high refresh rate gaming monitors, capable of 240Hz, 360Hz and beyond, becoming more common, 6X MFG also aims to take the burden of producing similarly high frame rates off the GPU, and combined with Nvidia’s Reflex tech, which lowers latency, DLSS 4.5 shouldn’t just allow compatible GPUs to punch well above their weight when it comes to performance, but should make games feel faster and more responsive, and allow gamers to make the most out of high refresh rate monitors without having to spend a fortune on a high-end GPU.

Testing out DLSS 4.5

For the past few weeks, I’ve been having a play with DLSS 4.5 to see if it can deliver on its lofty promises. To do this, I installed a PNY RTX 5070 graphics card in one of my PCs. The RTX 5070, with 12GB of GDDR7, is a mid-range (though at the upper end of the market) GPU that’s more affordable than the more powerful RTX 5080 and 5090 GPUs, but with hardware that will struggle with gaming at 4K and super-high frame rates, and that means it’s going to be one of the GPUs that most benefits from DLSS 4.5.

To help with my tests, MSI kindly sent me over its MPG 322UR QD-OLED X24 gaming monitor, which is a 31.5-inch 4K OLED (which uses MSI’s fourth generation QD-OLED technology) that’s capable of refresh rates of up to 240Hz. This is a screen that offers exceptional image quality alongside super-fast refresh rates.

Ordinarily, the relatively modest RTX 5070 graphics card would struggle to get anywhere near 200fps at 4K resolution in modern, graphically intensive, games, so pairing it up with the MSI MPG 322UR QD-OLED X24 could be considered a bit of a waste of money.

Not so with DLSS 4.5. At native resolution, with DLSS and MFG turned off, Cyberpunk 2077 at ‘High’ textures hit around 51fps on average. This is the kind of performance I’d expect from the 5070 — not mind-blowing, but with a bit of tweaking and lowering some settings, you could get a playable 60fps at 4K. Turning on extremely intensive Path Tracing lighting saw the average frame rate plummet to below 4fps.

Enabling both DLSS upscaling and 6X frame generation (which, for the moment, needs to be done via an override in the Nvidia app, rather than within the game’s settings) saw the frame rate jump to a very impressive 140fps on average. Not only is this taking advantage of the MSI monitor’s high refresh rate, but it also means RTX 5070 owners can experience Cyberpunk 2077, an already great-looking game, with immersive and realistic path-traced lighting — something they’d have missed out on in the past.

Turning off path tracing saw frame rates hit 270fps — well above the 240Hz of the MSI MPG 322UR QD-OLED X24. All of a sudden, the RTX 5070 becomes a great GPU to pair with the MPG 322UR, and while I did notice some minor visual issues (mainly around reflections sometimes looking pixellated, and some ghosting around moving objects — especially when driving), on the whole it looked, and felt, amazing to play, and the OLED monitor really made the neon-lit metropolis of Night City, where the game is set, look incredible. The visual issues weren’t too distracting, and I’m aware that at 6X frame generation, 4K resolution, and high graphical settings, I really was pushing the RTX 5070 to its limits.

Setting Dynamic Multi Frame Gen to match the 240Hz maximum refresh rate of the monitor made for more consistent frame rates, while also slightly reducing ghosting, as fewer frames are generated.

I also set Dynamic Multi Frame Gen to target 120fps, as my OLED TV (which I usually play on) offers, like a lot of TVs, 120Hz refresh rates at 4K. This gave me headroom to increase DLSS from ‘Performance’ to ‘Quality’, which means less upscaling is involved, while also turning on path tracing. This again improved image quality, and I was getting around 130fps on average.

It wasn’t perfect, as there were still some glitches (especially with some of the user interface (UI) elements, which DLSS 4.5 is supposed to address), but the fact that the RTX 5070 was managing 4K at 120fps with ray tracing on was very impressive.

I also played Avowed, which looked fantastic, and was hitting around 255fps on average, and Hogwarts Legacy. That last game was less impressive, with very noticeable and distracting visual artifacts and ghosting around characters. I fiddled around with the settings in both the game itself and via the Nvidia app, but I couldn’t seem to fix it, though this seems to be an issue with the game, rather than DLSS 4.5.

With the high refresh rates, I didn’t experience noticeable latency (a pause between issuing a command, such as firing a gun, when gaming, and when that command is shown on the display), though people who play competitive esports, where latency has to be kept to an absolute minimum, might find it more noticeable. However, in those cases, you’d be better off lowering the resolution to 1080p and turning down graphical effects so you can play the game natively, rather than introducing DLSS features, which, no matter how well optimized, will increase latency slightly.

Don’t let DLSS 5 overshadow DLSS 4.5’s good work

Overall, I was impressed with how DLSS 4.5 boosted my RTX 5070’s performance enough to take advantage of a 240Hz 4K screen. While there are some visual issues when really pushing MFG, the benefits far outweigh these, in my opinion, if you’re using a mid-range or budget GPU, and it continues to make high-end effects and features like path tracing more accessible for people who can’t afford high-end GPUs.

Bearing in mind my disappointing first impression of the original DLSS, I’m also reasonably confident that DLSS 4.5 will continue to be improved both by Nvidia and game developers as the technology matures and they become more familiar with it.

As I’m lucky enough to also own an RTX 5090, and mainly play on a TV that is capable of 4K and 120Hz, I’m going to stick with playing games natively where possible (so no upscaling or frame generation is used), but as games continue to become more graphically demanding, I could definitely see Dynamic Multi Frame Gen helping out, kicking in only when needed.

What does concern me, however, is that DLSS 4.5’s solid release seems to have been eclipsed already by its successor. This is due to Nvidia showing off a preview of DLSS 5… which didn’t get a positive response, to put it lightly.

Rather than concentrating on improving frame rates, image quality, and general performance, DLSS 5 instead looks to focus purely on aesthetics. The issue is, while the likes of DLSS 4.5 would upscale or generate images based on existing rendered frames, while preserving the original artistic intent, DLSS 5 appears to fundamentally change certain aspects of the image, including lighting and, most controversially, character designs, with many critics unflatteringly comparing DLSS 5 to cheap AI filters you find in mobile apps. The backlash was vocal enough to prompt Nvidia CEO Jensen Huang into responding, though his initial reaction, calling naysayers “completely wrong”, didn’t help matters.

While I have reservations myself (the DLSS 5 examples shown did feel like too many liberties were taken with the artists’ original intent, and it could lead to gamers having very different experiences depending on whether they have a GPU capable of DLSS 5 or not), DLSS 5’s biggest crime will be if the furor surrounding it makes people dismiss DLSS 4.5, and the positive steps it’s taken to make advanced graphical effects achievable to more people (though as long as you have an Nvidia GPU, of course).

I’ll reserve my judgement until DLSS 5 officially comes out — until then, I hope Nvidia continues down the DLSS 4.5 route, rather than turning future DLSS releases into just another generative AI slop tool I’d rather avoid using.

• Asus has announced the ROG Equalizer 12V-2x6 power cable
• The cable aims to solve melting issues with high-powered GPUs like the RTX 5090
• It's compatible with all PCIe 5.0 power supplies from other manufacturers

It's no secret that high-powered GPUs, notably Nvidia's RTX 5090, are still suffering from reports of connector melting issues, even with upgraded cables that have improved power connectors — but fortunately, Asus has seemingly introduced a solution.

As reported by TweakTown, Asus has announced the ROG Equalizer, a 12V-2x6 power cable for GPUs utilizing up to 600W, one that's designed to further help protect them from melting issues.

Of course, as a rule, RTX 5090s using 12VHPWR and 12V-2x6 cables aren't experiencing melting issues, but there have been enough cases to cause concern for consumers. Both cables are effectively the same, and the only major difference for the revamped 12V-2x6 design is that it has shorter sensing pins and longer power pins, which help to ensure the cable is properly seated. It's a poor connection which theoretically causes the melting problem.

That's why Asus has opted for a 12V-2x6 power connector, but with a tweaked design that has been specifically refined for a bigger load capacity within each wire, now at 17 amps (it's usually 9.2 amps with regular 12V-2x6 cables). These are also 'premium etched cables' that are flexible to help with cable management.

Combined with the GPU Tweak III software and its Power Detector+ feature, the ROG Equalizer should effectively keep hardware safe from melting, or that's the theory. In an extreme test case from Asus, the company observed that even if the connector wasn't seated in the GPU correctly, the ROG Equalizer would still maintain a temperature of 73.4C.

Meanwhile, a standard 12V-2x6 cable would reach 146C in the same scenario, which is a very high temperature that would likely result in the connector melting.

It's a big claim from Asus, and if this can be reproduced in other independent tests once the ROG Equalizer is available, it may just be the perfect tool to eliminate any melting issues with RTX 5090 GPUs.

The ROG Equalizer is compatible with power supplies from "all leading manufacturers", which is a massive bonus for all PC owners who have ATX 3.1 (PCIe 5.0) power supplies. It will also come bundled with ROG Thor III and ROG Strix Platinum series power supplies, but there's currently no release date for those.

Despite the marketing pitch here, Asus has not done enough to convince some gamers, and there are those who suggest that graphics cards linked to connector problems are simply best avoided.

These connector melting issues have been problematic for years, ever since the RTX 4090's launch in 2022. If the ROG Equalizer can finally put an end to all this, it should make high-powered GPUs like the RTX 5090 more approachable — well, aside from the obvious stumbling block of the pricing, anyway.

• Someone bought an MSI RTX 5090 Lightning Z, which is a $5,000 GPU
• They then practiced their soldering skills on the board, and broke the graphics card
• Why? It's surely because they wanted to implement a hardware mod that allows for an extreme BIOS to be run on the GPU

MSI's RTX 5090 Lightning Z is a rare limited-edition graphics card that costs $5,000 (or thereabouts) in the US, so what better way to practice your soldering than to test out your skills with the iron on said board?

Reckless? Well, that doesn't even begin to cover it really, but VideoCardz spotted this improbable-sounding tale of GPU foolishness, which was posted on YouTube by NorthridgeFix, a California-based hardware repair shop.

The broken card was sent into the repair outlet with an issue description which revealed the owner wanted to "learn how to solder tiny 0402 resistors" and that they were "practicing" on their pricey GPU.

"He tried to 'learn soldering' on an extremely rare MSI 5090 Lightning Z GPU of which only 1,300 units were ever made worldwide," NorthridgeFix observes in its YouTube blurb, adding: "I'm speechless."

Me too.

Predictably enough, the soldering lesson went badly wrong and ended up ripping one of the resistor pads, rendering the RTX 5090 non-functional, and requiring a delicate repair.

The repair isn't actually fully completed in the video clip, as it still needed further testing, but it seems like NorthridgeFix was successful in rescuing the expensive graphics card.

Analysis: solder to cry on

Why was the RTX 5090 owner even thinking about playing around with soldering resistors in this way with such a pricey piece of hardware? It's presumably because they were trying to get the MSI XOC ('extreme performance') BIOS to work so they could engage in some extreme overclocking.

That BIOS was seemingly limited in distribution and only given to pro overclockers, and MSI RTX 5090 Lightning Z graphics cards bought at retail can't be flashed with it. However, the BIOS was leaked, and what's happened is that some folks have found a way around this restriction, with adding an extra resistor seemingly allowing the XOC BIOS to be used. And apparently this is what the owner of the GPU was trying to do.

There is a reason, of course, why MSI hasn't freely released the XOC BIOS: namely, because if it did, there'd likely be a fair few destroyed RTX 5090 GPUs (and warranty claims). Remember, this extreme take on the BIOS is designed to allow for 2500W of power, a truly ridiculous amount (intended for the likes of liquid nitrogen-driven world record overclocking attempts).

At any rate, extreme BIOS aside, and to state the obvious: if you're a newcomer to soldering, don't start your journey on any functional graphics card, let alone a cutting-edge flagship model – and a limited-edition effort at that. The mind boggles with this case, frankly.

• Crimson Desert is now playable on Intel Arc GPUs, seemingly after Intel's latest driver update
• The game previously wouldn't launch on any Intel GPU, due to Pearl Abyss omitting compatibility
• Visual glitches are evident, but an official future update from Pearl Abyss or Intel should fix them

Pearl Abyss' Crimson Desert has provided a joyful experience to a variety of gamers on PS5 and Xbox Series X consoles, including those with gaming PCs powered by an Nvidia or AMD GPU. The same couldn't be said for Intel GPU users at launch, but the tide has suddenly turned.

As reported by Wccftech, Crimson Desert is now playable on Intel Arc GPUs, thanks to the latest GPU driver, noted by several users on Reddit. They're now able to boot into the game, which hasn't been possible since launch, with an error message previously stating 'the graphics device is currently not supported'.

This all comes after Pearl Abyss urged Intel Arc GPU users to request a refund for the game via its FAQ page, leaving both users and Intel itself bewildered by the decision to omit compatibility.

Well, thanks to backlash from Arc GPU users and Intel stating its "huge disappointment" at the lack of support, despite reaching out over the game's seven-year development period, Pearl Abyss recently promised future updates to provide compatibility; however, the latter is not exactly what we're seeing at this stage.

While Crimson Desert is playable on Intel Arc GPUs, it's still far from being fully supported, as numerous screenshots (available above) show multiple visual glitches. Some of them look like they've been pulled from a book of nightmares, with the main character, Kliff, and other NPCs missing parts of their faces or bodies.

However, this should be expected, as this isn't an update from Pearl Abyss on the game's end, and Intel's driver patch notes don't reference Crimson Desert. Once updates arrive from both Pearl Abyss and Intel, performance and visual glitches should no longer be a concern.

Fortunately, the game being in a playable state is a step in the right direction, even if it wasn't intentional on Intel's end, and hopefully, we'll be able to move past the matter of full compatibility for Arc GPUs sooner rather than later.

• Asus RX 9070 XT GPUs have been hit with a sudden price hike in the US
• Framework has warned that while memory costs have stabilized in some areas, this is likely just a temporary reprieve
• The laptop maker predicts that "We'll continue to see volatility and cost increases through the rest of 2026."

There's been a fresh round of price rises for PC components, and that includes price hikes for certain Radeon GPUs, as well as more RAM and storage-related unpleasantness.

We'll start with the graphics cards. VideoCardz noticed that Asus RX 9070 XT models have become more expensive at the firm's US store.

For example, the Asus Prime 9070 XT OC was previously $799.99 and has now been jacked up to $939.99, which represents a hefty increase of 17.5%. The newer white model of this graphics card has gone up by 7% and will now set you back $959.99.

The Asus TUF overclocked take on the 9070 XT is up by 16.5%, meaning the GPU now tips the scales at $989.99, very nearly a grand.

There's been no official announcement from Asus regarding any rising prices, and these hikes have been applied overnight, worryingly.

On top of that, modular laptop and PC manufacturer Framework has announced more RAM and SSD price increases (hat tip to Tom's Hardware), albeit these are not as painful as we've previously witnessed.

The Framework Laptop has seen a price increase for the prebuilt model with 64GB of DDR5 RAM, although there is some more positive news with lower CPU pricing (this is down to its original level for some configurations).

With the Framework Desktop PC, the company notes that it has absorbed RAM cost increases save for the 128GB model, which has had a price hike.

On the storage front, it says new SSDs the company is buying have "substantially higher costs" and to begin with, this means the 4TB version of the WD SN850X is more expensive – and other drives are likely to be soon.

Framework adds: "Our advice here is still that if you need high capacity storage, you should configure a system with it now while we still have inventory at the older, lower cost basis."

Analysis: a temporary reprieve?

Despite the fact that some cost increases have been absorbed, and the latest round of hikes is relatively limited, Framework cautions against getting comfortable with any notion that things might be calming down with regard to the memory crisis. The manufacturer warns: "We have seen costs stabilize in some areas, but all indications are that this is a temporary reprieve and that we'll continue to see volatility and cost increases through the rest of 2026."

There have been glimmers of hope regarding a RAM reprieve elsewhere of late, too — albeit mainly based on the cost of memory getting so high that consumer demand is seriously flagging — but those hints have been weighed down with similar caveats.

The Asus GPU price hikes are more alarming here, though, as they represent some swiftly applied increases, pushing the Radeon 9070 XT graphics card up over the $900 mark for a basic model. That said, this isn't representative of the overall market for AMD's 9070 XT in the US, as you can still get this GPU (at the entry level) for not much more than $700 (at least for some models).

However, is this an early signal that we might see other board makers also increase their 9070 XT asking prices? Possibly — that is very much the worry here, and we shouldn't forget that a few months ago, back at the start of the year in the US, you could still get these AMD GPUs for around the $600 mark, which certainly isn't the case now.

Data centers are entering the most aggressive expansion cycle in their history. Artificial intelligence, high-performance computing, and GPU-accelerated workloads are transforming data centers from traditional IT facilities into power-intensive “AI factories.”

This shift is structural, not incremental and forces a fundamental rethinking of power delivery inside the data center.

This transition hinges on a simple question: Can traditional AC power architectures scale to meet the density, efficiency, and economics required in the AI era? According to industry research, the answer is no. High-Voltage Direct Current (HVDC), specifically 800VDC distribution is emerging as a practical and economical alternative.

The scale of the AI buildout

Global data center capacity will expand from sub-100 GW today to as much as 300 GW by 2030. Approximately 70% of that capacity will support AI workloads, making high-density infrastructure the dominant growth segment.

This expansion requires 200 GW of new capacity over the next five years, equivalent to roughly 2,000 new large data center campuses worldwide. The defining characteristic of these new facilities is power density.

Traditional enterprise racks operating at 5–10 kW are giving way to 30-60 kW GPU clusters, 80-150 kW AI training racks, with industry roadmaps targeting loads as high as 500 kW per rack.

At these densities, electrical distribution emerges as a primary constraint on cost, efficiency, reliability, and scalability.

Traditional AC architecture is reaching its limits

Most data centers today rely on a multi-stage AC power chain that introduces losses, equipment cost and operational complexity at every step. A typical conversion sequence starts when utility power enters the facility and gets stepped down by a transformer.

The UPS system converts the current from AC to DC and back to AC again, then passes it through a power distribution unit. At that point, it interfaces with the server’s power supply for another AC-to-DC conversion before a final DC conversion at the board level.

Each of these handoffs leaks efficiency. While manageable at moderate densities, these inefficiencies become economically unforgiving at AI-scale. In high-density environments, the complexity of AC distribution becomes the limiting factor for operators—not compute power, not cooling, not even real estate.

Higher currents demand larger copper conductors, adding material costs and compounding heat buildup throughout the system.

Facilities operating legacy equipment may also have a variety of voltages running simultaneously, each with its own set of breakers, fuses and relays that prevent a fault in one part of the system from cascading into a wider outage.

The sheer complexity shrinks the margin of error for operating high-density AI data centers and places a real upper limit on the ability to scale, both physically and financially.

Perhaps most importantly, every watt lost in conversion becomes heat to be removed, driving the need for additional cooling infrastructure and raising operating costs.

Working with physics, not against It

Given the challenges of traditional AC distribution, the industry is increasingly looking for a different approach that simplifies the power chain. Servers at the end of the chain are running on DC. With an 800VDC architecture, there’s an opportunity to align facility power distribution with the native requirements of modern servers.

Rather than stepping utility AC through multiple voltage environments, an 800VDC architecture uses a central rectifier to convert incoming utility power once, distributing it as a stable DC current directly to rack-level converters. Changing the power distribution architecture eliminates conversion losses.

Technical research indicates this can improve end-to-end electrical efficiency by 8-12%. By replacing multiple AC voltage levels with a single high-voltage DC bus, facilities can eliminate much of the switchgear and transformer infrastructure that drives distribution complexity and the risk of failure.

This simplicity also makes it easier to integrate with battery systems and solar generation.

Ultimately, the physics are straightforward. Higher voltage means lower current for the same power. Data centers looking to scale beyond 100 kW racks can’t do it with an architecture that fights physics at each step.

Leaving $10 billion on the table

Efficiency gains extend beyond technical discussions and translate directly into costs that industry can’t afford to ignore. On both sides of the Atlantic, governments are grappling with skyrocketing electricity demand and rising utility prices, with some U.S. states now proposing laws requiring data centers to pay a higher price for electricity.

A High-Voltage Direct Current (HVDC) distribution system that delivers 8-12% improvement in energy efficiency over traditional AC distribution, translates directly into millions in savings. A continuously operating 100 MW IT load can save $8.5 million (£6.4 million) per year at a conservative energy cost ($0.12/kWh).

With estimated data center growth of 200GW by 2030, those savings easily reach $10 billion (£7.52 billion) annually.

The cost savings also apply to new builds. Between simplified installation, less equipment (PDUs, transformers, distribution panels, copper conductors) and reduced cooling capacity throughout the system, a 100 MW campus can save up to $80 million (£69 million) capital costs.

Power architecture as a competitive advantage

The scale of the AI buildout is unprecedented, and industry forecasts are continuously increasing. Currently, they stand at $6–7 trillion (£4.5 to 5.2 trillion) in total global data center investment by 2030, with the majority directed toward AI infrastructure.

At these investment levels, even modest efficiency improvements translate into billions in savings.

The shift toward higher-density computing is not temporary. Roadmaps from major hardware vendors indicate continued increases in rack power over the next decade. Facilities designed around legacy electrical assumptions risk becoming constrained or obsolete.

In this context, power architecture pivots from an engineering selection to a strategic decision that affects capital efficiency, operating cost, deployment speed, and long-term scalability.

The transition to AI-centric infrastructure is redefining the economics and engineering of data centers. As capacity expands toward hundreds of gigawatts globally and rack densities climb well beyond 100 kW, traditional AC distribution reaches its practical limits.

Compute capacity alone won’t determine the winners in the AI era, but rather, compute delivery with the greatest efficiency, speed, scaling potential, and economic discipline.

Power architecture plays a strategic role. Simplifying the power chain, improving efficiency, reducing capital requirements and enabling scalable high-density deployments are the foundation for the next generation of AI and GPU data centers.

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The boom in AI tools is frequently framed as a race to secure GPUs before someone else does. The contestant’s position is typically measured in hyperscaler CapEx, chip partnerships, and the number of Nvidia units deployed.

Yet when two thirds of organizations struggle to centralize their data, and less than a quarter (23%) report robust GPU capacity, the race changes course. It no longer resembles a sprint for sparse silicon, but looks more like a rush-hour gridlock of poorly connected clusters.

This bottleneck comes at a time of growing anxiety over digital sovereignty. European ministers now describe digital infrastructure as a ‘matter of national survival’, warning that reliance on closed, foreign-controlled systems creates strategic vulnerabilities.

With US cloud providers commanding roughly 85% of the European market – and sovereign cloud spending forecast to more than triple by $23 billion by 2027 – the pressure to secure control is well and truly on.

GPU scarcity is real. But much of today’s constraints are the result of fragmented Kubernetes environments, duplicated clusters, and IT infrastructure that was simply never designed for secure, efficient scaling at the scale required by AI. For many organizations, Kubernetes has become the default system for running modern applications.

It acts as an orchestration platform, managing containers, allocating compute resources, and keeping everything ticking along reliably across servers. These are grouped into clusters: pools of computing power that teams use to run workloads.

The problem is that Kubernetes was designed in a pre-AI era, when workloads were significantly lighter. It is far less optimized for sharing scarce, high-value GPU resources, and it’s for this reason that the cracks are starting to show.

The hidden (and growing) cost of cluster sprawl

In the rush to deploy AI safely, many organizations have defaulted to isolation by duplication. That means spinning up entirely separate clusters and environments for each team or workload, rather than securely sharing the same underlying infrastructure.

In practice, should one team needs autonomy, they get their own cluster. Should another team’s workload be sensitive, it is separated physically rather than logically. If governance is complex, the easiest answer is to ringfence infrastructure instead of rethinking how it’s shared. The result is cluster sprawl on a huge scale.

While each cluster may feel like a sensible risk-reduction decision, the reality is that it becomes an economic liability. GPU-backed environments are expensive to stand up, but even more expensive to leave underutilized. Left fragmented between teams, visibility of clusters drops while idle capacity increases.

This is so much more than an operational headache. Structural inefficiencies such as these erode return on investment. GPUs should be seen as capital assets, not experimental hardware tucked away in research and development budgets. Leaving them stranded behind siloes is the equivalent to building a power plant and running it at half capacity simply because the grid cannot distribute electricity efficiently.

Kubernetes may have transformed how infrastructure is built and operated, but it was never designed with multi-tenant GPU sharing in mind. In simple terms, this allows multiple teams or workloads to share the same underlying infrastructure safely, all with clear boundaries, isolation and governance without duplicating the hardware itself.

Without true multi-tenancy, shared environments creak under AI workloads. It leaves a choice between compromising on isolation or retreating to dedicated clusters. Neither scales, and the model is untenable. It’s for this reason that the narrative of GPU shortage is obscuring a much deeper flaw.

Sovereignty heightens the tenancy debate

Moving workloads either on-prem or into sovereign environments does not magically solve inefficiency. In fact, it can amplify it. When organizations bring AI infrastructure closer to home to meet regulatory, security or geopolitical concerns, they assume full responsibility for its utilization. This ownership, without orchestration, cannot be considered sovereignty, however. It is simply overhead.

As AI workloads continue to accelerate, the race won’t be towards hardware acquisition but secure deployment and efficient monetization. That requires a control layer purpose-built for the economics of AI, such as software that sits above the raw infrastructure and governs how compute is allocated, shared and isolated.

This enables isolation without duplication, governance without friction, and autonomy without sprawl. True multi-tenancy will emerge as the missing layer of AI compute, preserving both compliance and performance while unlocking higher utilization.

With sovereignty so high on the agenda, multi-tenancy will reposition resilience as a built-in process. Rather than respond to capacity constraints by spending evermore eye-watering sums, organizations can optimize what they already own. New workloads shouldn’t justify new clusters when it can be a shared asset that’s governed intelligently.

In any race, raw speed is only part of the equation. Control, coordination and the ability to navigate complexity often determine whether momentum is sustained. AI infrastructure is no different. Until it is designed for secure, shared scale, no amount of raw speed will deliver its true potential.

Read our list of the best IT management tools.

• A Redditor reports that their RTX 5070 (which they were gifted) died
• PNY replaced the faulty graphics card with an RTX 5070 Ti
• These kinds of upgrades can happen with returns if you get very lucky, and others on Reddit have similar tales to share

If your graphics card goes kaput, that's obviously regarded as bad news — as even if it's still within warranty coverage, you must go through the hassle of returning it for a replacement — but such a failure could turn out to be a good thing.

The recent experience of a Redditor (as highlighted by VideoCardz) who had a PNY GPU go wrong makes this clear. They reportedly received an unexpected upgrade after their RTX 5070 went to silicon heaven.

The Reddit post states: "5070 died completely, PNY sent me a 5070 Ti to replace it. RMA process was fast, too. Basically, a free $400 upgrade."

The Redditor further explains: "To make things even better, it wasn't even my GPU originally. It was in my brother's work computer. But since he had already replaced it, he gave it to me and said if I wanted to deal with the potential hassle of the RMA [return merchandise authorization], I could keep it. So he basically gifted me a $500 GPU, which turned into a $1000 GPU, all for an hour's work and $30 shipping."

As another poster succinctly puts it: "Unlimited GPU upgrade glitch unlocked."

Obviously, we must add some seasoning, but as other posters indicate, this can happen, and there are other reported incidents of replacement GPUs happily turning into upgrades.

Such as Gigabyte replacing an RTX 4070 Ti with a 4070 Ti Super, or Sapphire switching out a dead RX 6650 XT for an RX 6700 (albeit that took a month and a half, according to the poster).

Analysis: winning the RMA lottery

It's a breath of fresh air to hear a positive tale around PC components these days, as with the price of RAM, storage, GPUs (and now CPUs) all increasing — by wild amounts in some cases — we're hearing a lot more about sometimes sophisticated scams trying to con people out of the large sums of money asking prices have now turned into.

Don't believe that a graphics card manufacturer would replace a broken GPU with a higher-tier model? Well, I get where your skepticism is coming from, but this can happen, and the mentioned Sapphire example above is a clue to why — mainly because the return took so long to action. In these cases, the problem is likely finding stock of the GPU in question.

It might be the case that PNY didn't have RTX 5070 inventory in the warehouse at the time, and wanted to get the return completed in good time (there was a quick turnaround on this RMA, as the Redditor noted) — so the firm sent an RTX 5070 Ti instead. Obviously, this is a good way to keep the customer happy, and it serves as a positive piece of word-of-mouth when aired on social media, as is the case here.

So, if you have to return a GPU in the future, you might just get lucky yourself. However, note that these instances of apparent on-the-spot upgrades as part of a return aren't the rule, but very much the exception. They certainly happen, though.

The one potential wrinkle in this scenario — as one poster pointed out — is that if you're already at the limit of the wattage that your power supply is capable of coping with in your current PC, a more power-hungry GPU won't work in your system. Although you could, of course, just upgrade the PSU in this case (or just sell the GPU, and buy the lesser model again, pocketing the profit).

Gigabyte Radeon RX 9070 XT Gaming OC Ice: Two-minute review

If you’re after an AMD Radeon RX 9070 XT with a mild factory overclock, a clean white finish and a useful set of extra features, the Gigabyte Radeon RX 9070 XT Gaming OC Ice 16GB is well worth considering. At $1,099, it sits at the premium end of the price spectrum, but it does offer enough to still make it compelling compared to more affordable options.

If you want a slightly cheaper option, the non-Ice version (the Gigabyte Radeon RX 9070 XT Gaming OC 16GB) has the same spec and performance as the Ice variant we tested.

The Gigabyte card comes with a factory overclock to give a little extra performance from the get-go, with a game clock of 2520 MHz and a boost clock reaching up to 3060 MHz under peak loads. That’s a 120 MHz uplift to game clock and a 110 MHz increase to boost clock compared to AMD’s stock RX 9070 XT.

In real-world terms, that translated to roughly 5% better frame rates than a standard RX 9070 XT, and keeps it competitive against other factory overclocked cards. This means the RX 9070 XT is perfect for high-end 1440p gaming but also very capable at 4K in many games.

So it will come as no surprise that (at the time of writing this review), the RX 9070 XT is the number one pick in our best graphics card buyer’s guide.

To keep temps under control, Gigabyte has used its Windforce cooling setup, which has three ‘Hawk’ fans that spin in alternating directions to reduce turbulence and improve airflow.

Underneath the cooler, there’s a large vapor chamber, chunky composite copper heat pipes and the Gigabyte ‘Screen Cooling’ extended heatsink design with rear cutouts for airflow.

Gigabyte also uses server-grade thermally conductive gel on components like the VRAM and MOSFETs.

Like most GPUs these days, the Gigabyte card includes a dual BIOS switch, which lets you swap between Performance and Silent modes depending on whether you want the best cooling or a little less noise.

As expected, the cooling setup easily proved itself in testing with temperatures slightly lower than many of its competitors — in performance mode, the card idled at 33°C and peaked at 59 °C, with fan noise staying under 32dB. Switching to Silent mode dropped noise to under 30dB and resulted in peak temperatures rising slightly to 65°C.

Visually, the Gaming OC Ice goes for a clean white and silver look that is meant to be shown off in a windowed build, and includes subtle customizable RGB lighting with a sliding cover.

While it’s a large triple-fan card, the 288 x 132 x 56 mm dimensions are pretty reasonable compared to some of the chunkier RX 9070 XT models. That said, smaller mid-tower builds should double-check clearances before purchase. The card does have a reinforced metal backplate to add rigidity, but there’s no support bracket in the box.

Connectivity is very good, with two HDMI 2.1b ports and two DisplayPort 2.1a outputs — more than enough to support both high FPS gaming and a multi-monitor productivity setup. Power comes from three standard 8-pin PCIe connectors, and Gigabyte recommends an 850W PSU.

One handy feature — there are little power indicator lights that will let you know if there is an issue with one of the PCIe connections. Gigabyte also backs the card with a standard but appreciated three-year warranty.

Gigabyte Radeon RX 9070 XT Gaming OC Ice: Price & availability

• How much is it? It retails for about $750 / £700 / AU$1,099
• When can you get it? The Gigabyte Radeon RX 9070 XT Gaming OC Ice is available now
• Where is it available? You can buy it in the US, UK, and Australia

The Gigabyte Radeon RX 9070 XT Gaming OC Ice is available now, retailing for about $750 / £700 / AU$1,099 depending on the region and retailer. That puts it comfortably above entry-level RX 9070 XT cards, but that premium does at least get you a mild factory overclock, a larger cooler, a clean white finish, plus a few useful extras like dual BIOS support and power indicator lights.

At this price, the Gigabyte card lands well below the roughly $999 / £800 / AU$1,399 that RTX 5070 Ti cards tend to sell for, but slightly above the cheaper end for an RTX 5070. That gives it a solid position for gaming performance if you are happy with AMD over Nvidia.

While I'll dig into performance below, given current street pricing the Gigabyte Radeon RX 9070 XT Gaming OC Ice still makes a pretty good case for itself if you want a more premium RX 9070 XT. It's not the cheapest RX 9070 XT available, but compared to the RTX 5070 Ti in particular, it’s quite competitive for what you're getting.

The non-Ice version (the Gigabyte Radeon RX 9070 XT Gaming OC 16GB) has the same spec and performance as the Ice variant we tested and is often up to 10% cheaper.

Of course, prices have been shifting rapidly of late, up and down, so make sure you do your own comparison before shelling out.

• Value: 4.5 / 5

Gigabyte Radeon RX 9070 XT Gaming OC Ice: Specs

• Useful factory overclock and board-level extras
• Not a huge boost over the stock 9070 XT

• Specs: 4 / 5

Gigabyte Radeon RX 9070 XT Gaming OC Ice: Design

• Clean white finish with useful extra touches
• Large, but still fairly sensible by RX 9070 XT standards

Since there isn't a reference RX 9070 XT design from AMD, what matters here is how well Gigabyte's own board and cooler come together, and on that front the Gigabyte Radeon RX 9070 XT Gaming OC Ice makes a pretty good first impression.

The headline feature is obviously the white and silver finish, which gives the card a cleaner, more premium look than the usual black slab. It is clearly aimed at windowed builds, but Gigabyte has kept things fairly restrained, with just a little customizable RGB lighting rather than going overboard.

Cooling duties are handled by Gigabyte's Windforce setup, with three Hawk fans, a large vapor chamber, composite copper heat pipes and a sizable heatsink with rear cutouts to improve airflow. It is still a triple-fan card and still a fairly chunky one, but at 288 x 132 x 56 mm (11.34 x 5.20 x 2.20 in) it doesn't feel absurd by RX 9070 XT standards.

There are a few little extras too — like a dual BIOS switch for Performance and Silent modes, a reinforced metal backplate and power indicator lights that can help spot PCIe connection issues.

The only real downside is that there is no support bracket in the box, so smaller cases and heavier builds will need a quick clearance check before you buy. There is an attachment point though, so you can add your own support if needed.

Like most cards in this class, it still takes up a fair bit of space and needs three 8-pin PCIe power connectors, so cable management isn’t especially fun. Still, if you want an RX 9070 XT that looks the part without getting too flashy, Gigabyte has done a bang-up job here.

• Design: 4 / 5

Gigabyte Radeon RX 9070 XT Gaming OC Ice: Performance

• About 5% faster than a reference RX 9070 XT in my testing
• Very close to the RTX 5070 Ti in gaming, and comfortably ahead of the RTX 5070
• Cool-running for a premium factory-overclocked card, peaking at just 59°C

Simply put, the Gigabyte Radeon RX 9070 XT Gaming OC Ice does exactly what you want a mild factory-overclocked card to do — take the already very capable RX 9070 XT and squeeze a little more out of it.

In testing the factory overclock resulted in about a 5% uplift over my reference RX 9070 XT results averaged from cards running at stock speeds. Keep in mind that my RTX 5070 Ti comparison results are from when the card was first released and a non-OC version. I also had quite a few stability issues with the 5070 Ti early on. So with updated drivers and a comparable factory overclock, the 5070 Ti will be ahead in most gaming scenarios — especially when ray tracing is involved where the difference can be significant. If you are a fan of DLSS, then team green can often give much higher frame rates in certain games.

In synthetic benchmarks, the Gigabyte card turns in an excellent 3DMark Steel Nomad score of 6,624, which is very slightly ahead of both the reference RX 9070 XT and even the RTX 5070 Ti. In more ray-tracing-focused tests like Speed Way and Port Royal, Nvidia still has a slight edge, but the gap is nowhere near as dramatic as it once was, as AMD's ray tracing hardware is markedly improved over previous generations.

Gaming performance is what we all care about, though, and here the Gigabyte card excels. Across my 1440p gaming results, it was about 5.9% faster than the reference RX 9070 XT, about on par with the RTX 5070 Ti on average, and roughly 50% faster than the RTX 5070.

At 4K, it stayed about 3.6% ahead of the reference card, finished effectively level with the RTX 5070 Ti overall, and remained about 50% faster than the RTX 5070.

That makes this a very capable high-end 1440p card and a genuinely solid 4K option in a lot of modern games, particularly if you're happy to lean on upscaling where needed. Cyberpunk 2077 and Black Myth: Wukong both ran well at demanding settings, while Shadow of the Tomb Raider showed just how much fps headroom there is in lighter games.

Outside gaming, the picture is a bit more straightforward. Blender and Geekbench 6 Compute scores were a little higher than the reference RX 9070 XT results, but still behind the RTX 5070 Ti.

Thermals are an area where the Gigabyte card stands out, though, with the card peaking at 59°C and idling at 33°C in my testing, which is an excellent result and shows that the Gigabyte triple-fan setup handles the RX 9070 XT well.

Cyberpunk 2077: Ultra Ray Tracing, balanced scaling

Black Myth: Wukong: Ray Tracing: Cinematic, 50% upscaling

• Performance: 4.5 / 5

Should you buy the Gigabyte Radeon RX 9070 XT Gaming OC Ice?

Buy the Gigabyte Radeon RX 9070 XT Gaming OC Ice if...

Don't buy it if...

How I tested the Gigabyte Radeon RX 9070 XT Gaming OC Ice

• I spent about a week testing the Gigabyte Radeon RX 9070 XT Gaming OC Ice
• I used my complete GPU testing suite to analyze the card's performance
• I compared it against competing graphics cards using a mix of synthetic and real-world gaming benchmarks

I spent about a week with the Gigabyte Radeon RX 9070 XT Gaming OC Ice, testing it and comparing its performance against competing graphics cards.

I used industry-standard benchmark tools like 3DMark alongside games including Cyberpunk 2077, Black Myth: Wukong and Shadow of the Tomb Raider to get comparable results across the cards I have recently reviewed.

• Originally reviewed March 2026

• The buyer of an RTX 5090 in India got a nasty surprise
• They paid over $3,000 for a GPU box that contained laundry detergent
• This was from a third-party seller on Amazon, and it's a timely reminder to avoid the temptation to buy expensive goods from such sources

Someone who reportedly bought an RTX 5090 GPU from Amazon in India found a pack of laundry detergent inside the box instead of the flagship graphics card, serving as a clear warning about trusting third-party marketplace sellers.

VideoCardz spotted this tale of woe on Reddit, and of course, as with all such reports, we have to take it with some seasoning – though it appears to be authentic (it's a very detailed fabrication if it's made up).

The Redditor explains how they purchased a Gigabyte RTX 5090 on Amazon in India from a third-party seller, via an order that was 'fulfilled by Amazon', a GPU that cost around $3,200 in the local currency.

As you can see from the photo provided on Reddit (check it out below), what was actually in the battered box of the graphics card was a 1kg packet of Ghadi detergent.

The buyer has an unboxing video — though unfortunately for them, it's not continuously shot, and has been the cause of some skepticism from other Redditors — but it's clear the weight of the package (at 1.56kg on the official shipping label) is way too light for a boxed RTX 5090.

Hopefully, the latter point will prove key to getting a refund processed, but at the moment we're told Amazon has refused to issue a refund.

Seemingly this person is not the only one to have been sent laundry detergent in place of an expensive graphics card, as they have uncovered other similar complaints about the same third-party seller.

Analysis: scammers are more active in these days of pricey GPUs

Regardless of whether this is true (and as noted, that seems to be the case, although of course we can't know for sure), it serves as a timely reminder that, as GPU prices spiral, don't be tempted by deals from obscure third-party marketplace sellers on Amazon or any other retailer for that matter.

If it looks too good to be true, it probably is. And it just isn't worth the risk of spending thousands on a pretend product, whether that's a box with a rock or a lump of metal in it (as we've seen in the past with GPUs), or detergent (a new spin on this kind of scam). You're simply flirting with the possibility of getting burned.

Taking a punt on a cheap product isn't such a big deal, of course, but I really wouldn't consider a high-value purchase from a third-party seller, as it just isn't worth the potential grief. Yes, you should always be able to get your money back, but that process could be a stressful one.

And with expensive items, it really is sensible these days to record a full unboxing video as a matter of course (showing the whole package clearly unopened to begin with — or ideally, film from the moment of delivery).