• Nvidia response to "recent security incidents" by tightening restrictions on autonomous agents
• New software and hardware safeguards impose barriers if agents go rogue
• More than 100 customers have already agreed to work with the principles

Nvidia hasunveiled the new Open Agent Safety Platform designed to prevent autonomous AI agents from 'escaping' and acting beyond their established boundaries.

Following several recent alarming reports of AI-led attacks, the company admitted that safety should not just rely on safeguards built into AI models and applications, and that additional compute and hardware-level controls should also be used as more solid barriers.

"Recent security incidents have underscored the need to equip organizations with open, customizable tools," the company wrote in its announcement.

Nvidia adds even more layers of control for autonomous AI agents

The Open Agent Safety Platform consists of two key controls – OpenShell, which Nvidia describes as a software-based "secure runtime boundary," and Sentry, a hardware-based "watchdog" running on its BlueField-4 DPUs.

"Safety and security require full-stack engineering. NVIDIA Open Agent Safety Platform brings together industry, researchers and public-sector organizations to share best practices, align on evaluation methods and foster international cooperation," company CEO Jensen Huang summarized.

While existing model-level safety mechanisms are designed to tell AI not to behave in a certain way, the software-level OpenShell implements technical boundaries to actually prevent an agent from ignoring those instructions. Nvidia also argues that Sentry can quarantine and stop an AI agent within milliseconds.

Over 100 customers such as SpaceXAI, Anthropic and Microsoft are already using Nvidia's software and hardware-based safeguards.

The news, of course, comes after several high-profile instances where AI agents have operated beyond their intended controls, however while these new Nvidia-backed safeguards may be new, the overall principles aren't. The company is just stressing the importance of principles we're already familiar with, like least privilege, isolation/quarantining and detailed monitoring.

"As we continue to discover the frontier of AI capabilities, we must accelerate discovery at the frontier of AI safety," Huang added.

Nvidia made waves earlier this year when it announced the Nvidia RTX Spark, a new ARM-based SoC for small form factor PCs and laptops, and no one should be more concerned than Apple.

The Cupertino tech giant has enjoyed a very strong position when it comes to laptop sales for a while now thanks to its own ARM-based silicon, the Apple M-series.

With the refreshed Mac mini now on sale with the new Apple M6 chip, there’s going to be a lot of interest from creatives and developers around these two platforms, so I’m here to break down everything you need to know about the Nvidia RTX Spark vs Apple M6.

NVIDIA RTX SPARK VS APPLE M6: PRICE & AVAILABILITY

Neither SoC from either company is available on its own, so how much you pay will be entirely dependent on the PC or laptop it comes in, with things like memory and storage upgrades playing an outsized role in the final price.

The Apple M6 is only available in the new Mac mini (2026) for now, and it starts at $899/£899/AU$1,449 for the 16GB/256GB model. Adding more memory and storage will cost you extra, with the highest-spec M6 Mac mini featuring 24GB of unified memory for an extra $200/£200/AU$300, and an upgrade to 32GB for an extra $380/£400/AU$600.

Upgrading to 512GB SSD storage will add $200/£200/AU$300 to the price, while 1TB and 2TB will add $500/£500/AU$750 and $1,000/£1,000/AU$1,500 to the price, respectively.

Meanwhile, Nvidia is relying on its third-party partners to produce the PCs and laptops that will feature the Nvidia RTX Spark, so the prices will vary greatly among manufacturers, as well as which RTX Spark N1X core configuration is used (there will be an 18 CPU-core/5,120 CUDA-core entry configuration, and a more powerful 20 CPU-core/6,144 CUDA-core configuration).

While many units have been announced from Dell, Acer, and others, there's been no firm pricing yet, and much like the Mac mini, the amount of RAM the PC or laptop is configured with is going to have a major impact on its price. The first Nvidia RTX Spark devices are expected to go on sale in October, and some early indicators put the starting price of RTX Spark laptops and mini PCs at around $2,000/£2,000/AU$3,000.

• Advantage: Apple M6

NVIDIA RTX SPARK VS APPLE M6: SPECS

Fortunately, when it comes to specs, we know just about everything between these two SoCs, so we can do a proper side-by-side comparison of the Nvidia RTX Spark vs Apple M6.

As you can see above, Nvidia RTX Spark has a lot of hardware advantages versus the Apple M6. With nearly twice as many CPU cores on the premium configuration, almost four times the maximum memory headroom, and larger memory bandwidth, the Nvidia RTX Spark chips start out well ahead of the Apple M6 when it comes to specs—and that’s before we discuss the RTX Blackwell GPU.

Apple does have an advantage when it comes to maximum clock frequency, however, running nearly 800MHz faster than the best Nvidia chip. Will it be enough though? That remains to be seen.

• Advantage: Nvidia RTX Spark

NVIDIA RTX SPARK VS APPLE M6: PERFORMANCE

So far, I’ve only been able to benchmark the Apple M6 myself, as the new Mac mini went on sale on September 22, so there’s not a whole lot I can say definitively about the Nvidia RTX Spark vs Apple M6 when it comes to performance, but we’re not entirely in the dark here.

The RTX Spark N1X is essentially the Nvidia DGX Spark SoC, though it is running at a lower wattage for laptops (45W to 80W TDP, roughly, compared to the desktop DGX Spark’s 140W TDP). For RTX Spark desktops and mini PCs, I expect the TDP for the N1X to come close to or match the GDX’s 140W TDP.

Since we do know a lot about the performance of the GDX Spark across various workloads, we can expect the RTX Spark N1X to perform similarly, at least in small form factor PCs. What’s more, we also have some leaked benchmarks for the two RTX Spark N1X SKUs from Geekbench 7.

Note: RTX Spark N1X 3DMark scores estimated from RTX 5070 and RTX 5070 Ti laptop GPU benchmark results

As you can see, the Nvidia RTX Spark chips handle themselves very well against the Apple M6 in terms of GPU performance, though I need to caveat those results since they are approximations based on the listed core counts for the two SoCs’ GPUs. We don’t have benchmarks for these chips yet in 3DMark, but the GPU is built on the same architecture as Nvidia RTX 50 series laptop GPUs, and so we can ballpark where the N1X’s GPU is likely to end up given the number of CUDA cores they have available.

It’s also worth noting that the Nvidia RTX Spark’s multi-core performance definitely benefits from the increased core count over the Apple M6. However, the Apple M6 has a substantially better single-core performance, clocking in about 56% faster than the premium 20-core Nvidia chip.

This is going to translate to substantially better in-app performance for things like Adobe Photoshop and will help with agentic AI when an agent is processing a prompt or reasoning out a response, as these are inherently single-core dependent.

However, agentic AIs rarely work alone without some assistance from sub-agents, sandboxes, and other concurrently operating tools, which is where the multi-core performance of the Nvidia RTX Spark will definitely come in handy.

Of course, it also goes without saying that Nvidia’s distinct advantage over Apple when it comes to AI hardware thanks to native CUDA processing can’t be understated. Apple’s M6 might perform very well, but it will not be able to keep up with an Nvidia GPU that has up to 128GB of available memory.

And for gaming, well, Apple has really struggled to break Window’s stranglehold on PC gaming, and the Apple M6 doesn’t change that in the slightest, giving the Nvidia RTX Spark the edge there almost by default.

• Advantage: Nvidia RTX Spark

NVIDIA RTX SPARK VS APPLE M6: EARLY VERDICT

It remains to be seen how well the two Nvidia RTX Spark chips fare in real world testing, but early signs are very promising for Nvidia’s first SoC for the consumer market.

Apple isn’t out for the count just yet, either, and it will still hold the edge on price since the Nvidia RTX Spark is going to need a lot of very expensive RAM to process those 170B parameter models.

If you’re a more mainstream user who is just looking for a great PC for productivity, school work, and the like, the Apple M6 is the platform to go for.

Nvidia’s RTX Spark appears to be far more of a professional product with some gaming bolted onto the side, and it will surely have a price to match, so you only really need to consider the Nvidia RTX Spark if you can tell the difference between inference and model training. If you have to look those terms up, the RTX Spark isn’t really for you, but if those two are exactly what you plan on doing with your next device, you really can’t do any better than the Nvidia RTX Spark - as long as devices that include it are reasonably priced.

Normally we get to this time of year and the typical advice is to hold fire on any potential tech purchases. This is because Black Friday is just a couple of months away, and so it always seems prudent to think about being patient, and the bargain you might just secure in the biggest tech sale of the year.

Not this year, though, when it comes to GPUs – at least in my opinion.

During Black Friday 2025, we did see some decent deals on graphics cards. Not many, mind, but there was a scattering of them. I've noticed on Reddit that there are some gamers who have found a GPU with a decent price tag, but they're wondering whether to hold off and wait for something better on Black Friday. They are perhaps remembering some of the discounts that happened last year (thin on the ground as they were).

For example, this US-based Redditor asks: "Buy an open box [Nvidia RTX] 5070 now or wait until Black Friday?" They add: "I see a few open box deals for $745. What do people think about snagging this now at such an inflated price, or waiting until Black Friday to try and snag under $700?"

If you're in a similar quandary, my advice is this: absolutely do not wait for Black Friday. Just buy that graphics card now.

Let's break down the reasons why this isn't a 'waiters' market (by which I mean those biding their time, not the lovely people who serve us food and drinks).

Reasons to be fearful

For starters, as a legion of cynical Redditors are quick to point out, Black Friday isn't the best for graphics card deals anyway – it has never been the strongest suit for this sale in terms of PC components.

As noted, there have been bargains in the past though. Despite those Redditors who insist that GPUs just have their prices quietly upped in October so they can be artificially 'discounted' for Black Friday, there are actually some genuinely decent deals – or there have been.

However, things have just gone crazy this year, and as the second half of 2026 has rolled around, the RAM crisis has reached another tiring peak of frenzied hiking.

There were more rumors of imminent GPU hikes just a couple of weeks ago (following last month's negative chatter), and at retailers in the US we've already seen graphics card prices rising considerably over the past month. Compounding this are even more negative predictions about the memory shortage in general – which inevitably affects video RAM as well as system memory – and DDR5 prices which are spiking in a ridiculous manner right now. So you've got a recipe for a tumultuous rest of the year.

In short, I can't see 2026 panning out any other way than seeing more GPU price increases – so why wouldn't you buy now? At least from the perspective of whether Black Friday might somehow bring some bargains around – it surely won't.

I'm pretty convinced of that, and there are nuances here in terms of what kind of graphics card you're looking to buy. I'm talking about mainstream GPUs, the mid-range or lower tiers of the market, and not the higher-end graphics cards.

The beefiest GPUs like Nvidia's RTX 5080 (don't even mention the RTX 5090) are already exceptionally pricey, and while I don't think they'll get any cheaper – and indeed I reckon they'll probably still creep up in price – the cost of those products is already a very bitter pill to swallow.

I don't blame you if you don't want to pay out that much cash for a graphics card, because over $1,500 for an RTX 5080 (in the US) is a pricing nightmare. But you'll have to accept that you'll likely be in for a long wait for better prices, maybe not until 2028. A compromise might be to buy a card like an RTX 5070 to tide you over, or maybe an AMD RX 9070 XT (it's a touch less expensive in the US at the time of writing), then sell that second-hand later on when the RTX 5080 comes down.

The trouble is, there are no good options here... not for the pricier GPUs anyway.

Red Friday

I think that this year, Black Friday should be renamed Red Friday for its Herring-like nature, at least when it comes to graphics cards. Don't be swayed by the temptation of waiting for a deal, and if you see a reasonable price on the GPU you're considering, grab it now.

A $750 (open box) deal on an RTX 5070, the dilemma of the aforementioned Redditor, seems a sound enough proposition, even though that's a depressing reality considering this GPU was still going for around $650 just two months ago – because you're now looking at $850 or so (going by Newegg US pricing).

You can pick up an RTX 5060 Ti 8GB for only 10% more than this GPU was priced at in June (again, going by Newegg US), and I think there's an even stronger case to snag something like this if you're looking at the more affordable end of the market.

Obviously, do your own research based on the regional pricing for the GPU you're thinking about, but whatever the case, I'd still urge you not to be fooled into waiting for Black Friday, as I really think that'd be a mistake.

• Alibaba's T-Head unveiled the Zhenwu V900 with 216GB of memory, 1.2TB/s links, and a claimed 3x gain over the already-shipping M890
• Very few details associated with its raw performance: Alibaba gave no FLOPS, node, foundry, or power figures, and both specs it did share project it will trail Huawei's Ascend 960DT, which releases in the same quarter
• CEO Eddie Wu targeted more than 20GW of Alibaba Cloud-operated capacity by 2032 while admitting supply-chain shortages are limiting growth for the company

Alibaba opened its annual Apsara Conference in Hangzhou with three big numbers: a chip that it says triples the performance of its predecessor, AI clusters that could stretch to 500,000 of those chips, and a pledge, quoted by the South China Morning Post, that the data centers Alibaba Cloud operates worldwide will pass 20 gigawatts of capacity by 2032.

CEO Eddie Wu called the new Zhenwu V900, designed by the group's T-Head chip unit, the "most powerful AI chip in China today."

This was part of an address that distinguished Alibaba's offering as a GPU rather than an ASIC, landing it squarely in Nvidia's territory even as the latter's H200 GPU is highly sought after by AI labs.

A powerful offering on paper, but one that doesn't publish actual performance

The Zhenwu V900's spec sheet might be intentionally short, even as its performance figures versus comparable current-generation Nvidia GPUs, a banned option, could limit interest ahead of launch.

Alibaba has, however, added an impressive amount of memory and interconnect, allowing it to cluster a configuration it claims can run as many as 500,000 GPUs in parallel.

The V900, as per Zhidx, reporting on the launch, lists 216GB of memory and 1,200GB/s of chip-to-chip bandwidth and was originally expected to be available to clients in Q3 2027 before the recent revision pushed it two quarters earlier.

T-Head vice president Gao Hui now says the company will, going forward, keep a one-generation-per-year cadence, with a J900 due in the third quarter of 2028.

Despite the closer timeline, it is interesting that Alibaba has published no FLOPS figure, process node, foundry, power rating, or even the V900's internal memory bandwidth, making it hard to gauge its most powerful single-chip offering in terms of raw AI and compute performance.

This is particularly ironic at a time when the competition from Huawei, the Ascend 960DT, which was launched the same quarter after being pulled earlier by three quarters, is offering far more detail.

It lists 2 petaFLOPS of FP8 compute, 288GB of memory, 9.6TB/s of memory bandwidth, and a 2.2TB/s interconnect, making it a higher-VRAM and higher-interconnect offering based on the information Alibaba has already published.

The 500,000-card figure interestingly may just be a scaling limitation, not a system anyone has built. For scale, T-Head said in May 2026 that it had shipped just over 560,000 Zhenwu chips in total. One maxed-out V900 cluster, in other words, would need nearly as many chips as the company had shipped in its entire history.

It does, however, bring a secondary calculation into the equation: if each V900 drew what Nvidia rates its H200 at, up to 700W, the accelerators alone in such a cluster would pull a significant amount of power. A newer, larger chip could run hotter and be more power-hungry than Nvidia's Hopper offerings, even as China remains limited to older process nodes because of US sanctions on chips and chipmaking equipment.

A large cluster of 500,000 or similar GPUs would undoubtedly rack up power requirements considerably fast for the regional AI juggernaut, and 20GW by 2032 is not an unforeseeable event when one considers that per-GPU power draw is only heading up for the foreseeable future as the chips get faster, denser, and more monolithic at the same time.

Wu said medium- to long-term demand far exceeds what Alibaba can supply, and that shortages across the AI data center supply chain "are currently limiting the speed at which we can scale our compute infrastructure." The target covers capacity Alibaba Cloud operates, and the company did not say whether it means IT load or total facility power.

A Nomura note relayed by Sina puts Alibaba's current footprint at roughly 4GW and expects future buildouts to blend owned, leased, and third-party-financed sites, implying about fivefold growth in six years.

Whether the upcoming V900 displaces Nvidia's global market dominance, even slightly, is unclear, with Team Green already far ahead in a market where AI customers buy GPUs years in advance. The situation, however, is vastly different in Beijing.

In China, much of the disruption has already happened, with Chinese AI labs being 'persuaded' to stay away from Nvidia solutions by the state. Nvidia's latest 10-Q says shipments of data center Hopper products to China were under 1% of its $89 billion in data center revenue for the quarter to July 26, and the company said in its earnings report that objections in Beijing kept it from selling the full volume of H200s that Washington had licensed when relations briefly thawed.

Chinese labs have turned to a mix of homegrown solutions, including specific ASICs, GPUs, and CPUs, allowing local players such as Alibaba, Huawei, and others to thrive in a market devoid of foreign competition.

With the US government restricting Nvidia's power limits and the Chinese state taking a dim view of local labs depending on foreign chips, Alibaba's V900 GPU doesn't need to beat Nvidia's Rubin GPUs or even its Blackwell offerings. It simply needs to take on and provide a viable alternative to a GPU that is two generations older: the H200 (based on Nvidia's Hopper architecture).

Its bigger challenges will be Huawei's locally produced Ascend 960DT and similarly configured chips from upcoming local chip design firms, as well as supply chain limitations that could restrict overall production and, by proxy, deployment for a company looking to spend billions on AI infrastructure in the coming days even if its "most powerful" status, which is currently consigned to a claim holds at the time of its release.

What happened to Jensen Huang? The Nvidia co-founder and CEO seems about as disconnected as a person can be from other humans' life experiences. That wouldn't be so concerning if it weren't for the fact that he runs the most valuable company in the world and is responsible for the hardware behind the most life-altering innovation in our lifetime: AI.

Huang, who founded Nvidia 33 years ago, has been a CEO for decades and a billionaire since 1999; he's also now firmly on the side of rapid AI development and deployment with little-to-no regulation, putting Huang firmly at odds with a growing legion of, admittedly, some other billionaires and CEOs — among others — who are calling for a foundational model development pause or at least slow down.

It's in this moment that Huang has been talking — a lot. He's sat down with multiple journalists for chats, but none now more notable than the extensive New York Times podcast with columnist Ezra Klein.

Unlike some of the other interviews, Klein used Huang's own description of the AI as a Five-Layer Cake: Applications, Models, Infrastructure, Chips. Energy to frame the conversation, which invited Huang to opine on all these critical AI bits, and I have to say, many of his comments were eye-opening.

Here are the most startling things Huang said and maybe why he's saying them:

'There are a lot of skills that don’t matter'

"There are a lot of skills that don’t matter."

The topic here was studies in China showing that while AI in education may initially help students work more efficiently, it, on average, lowers their test scores by almost 20%.

Huang agrees with Ezra that the use of AI is, while helping them work faster, possibly degrading student performance, at least in certain skills.

"The multiplication table is starting to be forgotten. Doing square roots, my goodness. Basic math is being forgotten. Does it matter?...Yeah. I don’t think it does. I don’t think it does."

Huang believes that even as we lose some skills, or as students become adults entering the workforce, they will gain other new skills. Of course, that's sort of a zero-sum game where you can easily replace one thing with another.

I don't know about you, but I do consider the multiplication tables to be a core skill and useful even if you don't work in a math-related field. Naturally, Huang and most AI providers would like to believe that, like the calculator before it, AI will handle this skill and do all the math for you. Perhaps. But at what point do we lose the ability as a culture to double-check AI's work?

'I actually don’t know my address'

This next one is actually connected to the prior quote but is worth calling out:

"My first confession, I actually don’t know my address."

Not his email address, not his phone number or his partner's phone number, but where he lives. The street number and zip code (I'll assume he knows the town and state).

Huang used this as an example of a skill he no longer needs, but admits that realizing it while pumping gas and needing his zip code (I'm guessing for credit card verification) panicked him.

Listening to this while driving my own car, I almost pulled over. "What?!" I yelled at my AI-filled iPhone 18 Pro Max, which was playing the podcast. First of all, how? Second, Huang could not have crafted a better comment to undermine this and many of his other comments.

The only way you don't know your home address is if you've been shielded from the act of entering it on documents and driving yourself home because you always have someone else doing it for you. Huang's lived experience is thoroughly disconnected from the average person, and yet the choices he's making impact most regular people.

'[China] manufacture[s] everything in volume. They manufacture smart kids in volume'

Throughout the long conversation, Huang comes off as an industry Pollyanna and wildly self-serving.

He's asked repeatedly about China's approach to AI and if and how the US should be competing with them and ensuring that the US doesn't fall behind in this critical race. To put his comments in context, you have to remember that Huang personally asked the White House to allow it to continue selling AI chips to China. Huang did note, by the way, that he at least sells new technology to US companies first.

Overall, Huang essentially never criticizes China and, in fact, seems almost in awe of its approach on most fronts, especially in its use of open-model community (calling it "super-vibrant), and how it's raising an army of people to build its AI future.

"They have so many scientists and mathematicians. The number of engineers they have, they manufacture that in volume. They manufacture everything in volume. They manufacture smart kids in volume."

I don't know if that last bit was a backhanded criticism of the US education system, but it's not like he added, "Of course, we are creating just as many smart kids in the US."

'all of the rhetoric and all the alarmism, all the doomerism, all of the predictions — they’re scaring people'

If Huang has any criticism, it's reserved for his US counterparts, whom he calls "alarmists" and "doomers".

"I want to see us not ruin the opportunity for the United States to benefit at the highest level. And notice all of the rhetoric and all the alarmism, all the doomerism, all of the predictions — they're scaring people."

Huang insists that these AI models are still just programs running on operating systems, and wishes people would stop infusing them with human attributes.

'Just because it comes from a scientist doesn’t make it scientific'

While Klein mentions most of Huang's partners and occasional alarm-sounders, like Altman, Modei, and Musk, Huang doesn't actually mention any of them by name. The 'Godfather of AI' and chief alarmist Geoffrey Hinton, though, does receive special mention.

In response to a question about Hinton's assertion that there's a 10% chance AI will end society as we know it, Huang calls the statement "irresponsible" and adds, "Just because it comes from a scientist doesn’t make it scientific."

In a way, Huang is right. After all, Hinton is still just a person who can bring personal opinions to the debate. Huang argues that the "10% chance is not grounded on science."

But isn't it? If Hinton is the Nobel Prize-winning person who introduced the world to deep learning, which helped trigger the generative AI revolution, isn't everything he says, in some way, based on science?

Instead of saying he understands the concern but here's why he's wrong, Huang just claims the foundation of Hinton's argument is faulty, and therefore his statements are not really worth addressing. Huang would simply like everyone, all the doomers, to stop scaring everyone.

'The A.I. supercomputers are super energy efficient, but they’re still going to use a lot of power'

Huang says he wants AI to benefit every company and person, and he tries to offer a reasoned approach to the growing outcry over data centers.

He agrees that if people don't want them in their town, "then so be it," and encourages companies to be transparent about the impact, though he argues that their "use of water is really efficient." In the same breath, Huang tries to have it both ways: "The A.I. supercomputers are super energy efficient, but they’re still going to use a lot of power."

If, in Huang's perfect world, AI companies do generate their own power (building power sources takes time, probably more than it takes to build data centers) and they somehow lower property taxes, maybe data centers could someday be a net positive. Most, I think, would argue we're not there yet, even as the number of data centers being built across the US explodes.

'I think we just have to acknowledge that we got ourselves really gummed up in climate change and sustainable energy'

More concerning is Huang's shocking perspective on climate change and fossil fuels.

"I think we just have to acknowledge that we got ourselves really gummed up in climate change and sustainable energy, and as a result, we just didn’t plan enough energy production."

Klein, naturally, asked what Jensen meant by "gummed up," and, yes, it got worse.

"Well, in the near term, energy production requires fossil fuel. And because there’s just so much angst about fossil fuel energy production, if you look at our country, we’ve produced very little net new energy for a long time."

There's a lot to unpack there. It sounds like Huang is downplaying real climate change concerns, something that might align with the beliefs of his pal and "drill-baby-drill" and climate-change denier US President Donald Trump.

In fact, Huang never addresses whether climate change is real or not and instead seems fixated on how the loss of more fossil-fuel-burning energy plants puts us behind China in the energy race, and, more problematically for him, at a time when AI needs a lot more energy.

'They’ve got to inflict an enormous amount of pain and suffering on you so that they can save you'

It's not all bad. Huang believes, "If you want a future that is sustainable, lean into AI”

And then he says this:

"Yeah. It’s kind of like, in order to save you, they’ve got to hurt you first — that’s the nature of surgery. They’ve got to cut you open to save you. They’ve got to inflict an enormous amount of pain and suffering on you so that they can save you. And so I think A.I.’s kind of like that."

As for what level of "pain and suffering" Huang believes we should endure, he didn't elaborate.

If you were watching or listening to the interview hoping for some encouragement or a more rational, middle-of-the-road approach to regulating and governing AI in ways that truly benefit all, you might've been disappointed. Huang does say that when the builders see AI going awry, they should stop it, and when an AI acts out of alignment, "they shouldn’t release the product. That’s the simple answer." But he never addresses the concerns over what appears to be happening before Anthropic, OpenAI, and potentially others release their models. These systems are jumping fences in testing.

Huang may not understand the needs of the common person, but that pales in comparison to the dispassionate AI, which neither knows nor cares about us. We need leaders like Huang to start caring, but first they have to understand us and our very reasonable concerns.

• Tektronix's DPO718AX brings eight synchronized channels of up to 25GHz each to the 7 Series and can double as a Windows 10 PC
• The EPYC 3351, T1000, 96GB RAM, 1.6TB SSD configuration the oscilloscope offers makes it more than capable of running most modern OSes
• Prices start at an eye-watering $308,000 for the base configuration, which can be further modified as per end-user needs

Tektronix has added an eight-channel model to its flagship 7 Series oscilloscope line, and much of the attention it has drawn has gone to the computer it houses as part of the configuration.

The DPO718AX hosts a 12-core AMD EPYC processor, an Nvidia T1000 graphics card, 96GB of RAM, a removable NVMe SSD with at least 1.6TB of capacity, a 15.6-inch 1080p touchscreen, and an optional Windows 10 drive, all in a chassis that Tektronix's datasheet puts at 38.1kg.

This has drawn sharp comparisons to actual Windows-based PCs amid a RAM and memory shortage, including one by CNX Software, which said that the instrument "looks like a high-end PC running Linux (or Windows 10 LTSC)."

A powerful PC in its own right, albeit with dated components

None of the computer hardware listed for the DPO718AX is new or recently released. The AMD EPYC Embedded 3351 CPU is one of the oldest hardware offerings in play here, featuring AMD's first-generation Zen cores and a February 2018 release date.

While the SSD storage speed is unspecified, it is safe to assume it will be capped by the PCIe 3.0 support the EPYC CPU provides to the system. The 96GB of memory on offer can also safely be assumed to be DDR4, as the CPU supports only DDR4 configurations. Despite these limitations, the CPU does offer a 12-core, 24-thread configuration with a rated boost clock of 3GHz that should handle most tasks with relative ease.

The Nvidia T1000 GPU is an entry-level, workstation-class GPU built around its Turing architecture, with a release date of May 2021. The oscilloscope comes with an FHD touch-screen display and can output video up to QHD via DisplayPort, with a capped refresh rate of 60Hz.

The oscilloscope can also run Windows 10 natively, and it matters more than you might think: it is required for the compliance and RF analysis software the DPO718AX supports. The DPOs do not come with an installation by default; the Windows 10 LTSC 2021 installation is available on a secondary, removable drive that users can opt for if needed.

Since the integrated AMD CPU supports TPM 2.0, you could theoretically run Windows 11 on a device that costs hundreds of thousands of dollars, but the aging hardware, low-clocked CPU, and dated internals would make it a terrible PC replacement, even if one had the money to splurge on such an endeavor.

• Nvidia Smooth Motion has been modded onto RTX 3000 series GPUs
• The experimental mod allows driver-level frame generation in unsupported games
• Nvidia hasn't officially implemented this and other features on older RTX hardware yet

Modders continue to find new ways to implement Nvidia's latest technologies on older hardware, and now it's been done with Team Green's driver-level frame generation.

As reported by Wccftech, Nvidia Smooth Motion has been unofficially made to work on RTX 3000 series GPUs, with a new NVSmooth30 mod available on GitHub, confirmed to work on an RTX 3050 laptop and an RTX 3080 desktop GPU.

Nvidia's Smooth Motion functionality is essentially identical to AMD's Fluid Motion Frames, meaning that it enables frame generation in games that don't have official support for it — a tool that is most useful for older games or games running in emulators.

The mod works by modifying Nvidia's driver-level frame generation DLL (Dynamic Link Library) file, labeled NvPresent64, bypassing the usual compatibility checks that would stop it from working on Ampere GPU hardware, aka RTX 3000 series GPUs. It also works by "conservative metadata retargeting" from Ada Lovelace (RTX 4000 series) and targets Ampere instead.

The creator says that all users need to do is place the DLL file into the relevant game's folder and use the Nvidia Profile Inspector app to enable Smooth Motion. The process is very simple, but this is an experimental project, so it runs the risk of game bugs and crashes, or potentially other issues — and must not be used in online games using anti-cheat (doing so could result in a ban).

Gamers want new features, but new GPUs are unaffordable

Nvidia has been quite impressive with its latest technologies, notably with DLSS 5 Neural Rendering (which I saw at Gamescom 2026), and Smooth Motion has been one of many over the last few years.

The GPU market is in complete disarray due to the RAM crisis, which means prices for GPUs for both AMD Radeon and Nvidia GeForce GPUs are way too high for most people to consider an upgrade. Features like Multi-Frame Generation, Dynamic Frame Generation, and Smooth Motion are those that PC users would love, but can't use on older hardware.

Modders have consistently proved that these features can be backported in some way, with some even going as far as porting DLSS 5 onto the RTX 4080 (despite the performance hit).

DLSS 5 Neural Rendering coming to older hardware in the immediate future is unrealistic — but Nvidia has previously told me that it does plan on adding backwards compatibility at some point, though it's likely to come to 4000-series models first, and then we'll have to hope for 3000-series later in the future, if it's even possible.

But it feels as though features such as Frame Generation, and specifically Smooth Motion, deserve backporting attention now more than ever, as users are stuck with older GPUs.

Doing so would put Team Green in the good books of many PC gamers, and keep players from relying on unofficial mods to achieve better performance and enjoy GeForce hardware to the fullest extent.

I can't stress enough that Control Resonant is an absolute marvel in terms of visual enjoyment. Remedy Entertainment has crafted one of the most immersive visual experiences in gaming, doubling down on its predecessor's trippy, psychedelic, and mind-bending nature, and taking it to a new level using Nvidia's path tracing.

Putting my Nvidia GeForce RTX 4080 Super to the test, I ran Control Resonant on LG's 39-inch 5K2K OLED monitor, and I was pleasantly surprised by the significant enhancement to every detail on screen (albeit it did leave my GPU rather toasty).

At a 5120 x 2160 display resolution which is a 21:9 aspect ratio, you're getting 33% more pixels than a regular 4K gaming monitor at a 16:9 ratio, so the demand on the GPU is harder than most can handle — and even though Control Resonant was cooking my GPU, the frame rate was quite surprising.

With DLSS 4.5 Quality and Frame Generation enabled, and the high graphics and high ray tracing presets, performance remained fairly consistent within the high 50fps to low 60fps range. At 5K2K resolution, that's very reasonable, even if Control Resonant relies on Frame Generation to keep the gameplay smooth.

Dropping DLSS down to Performance mode pushed the frame rate to the high 80fps mark, and it's worth noting that it remained consistent even in moments of intense combat with tons of foliage and objects involved.

It's worth noting that the graphics with standard ray tracing are still fantastic, and the high (or even the medium) preset is likely the best route for any PC gamer who is using a low- to mid-range GPU (with Frame Generation). Lighting in all areas, specifically those where the Hiss is present (the really bright red zones), is phenomenal, and frankly, that applies to the experience without any ray tracing at all.

Path to victory

However, the star of the show is path tracing, which can be enabled by switching to the ultra ray tracing preset. I instantly knew my GPU was in for a rough ride once its temperature shot up to nearly 80C — but that's to be expected when running path tracing on a 5K2K display.

With DLSS Performance and Frame Generation enabled, I was able to achieve a solid 60fps in most scenarios, with occasional dips into the high 50s during traversal and combat. This aligns well with Remedy's system requirements for Control Resonant, which require an RTX 5080 for 60fps at 2160p using DLSS Performance, as the RTX 4080 Super is relatively close to this current-gen GPU for performance.

Realistically, this setup is out of the question for most gamers, as it will cost around $3,000 / £3,000 (or potentially more). LG's display is very expensive, sitting at $1,799.99 / £1,499 (on sale I should note), but an Alienware AW3926QW 5K2K alternative might help lower the cost.

The RTX 4000 series is discontinued, but pricing for anything equivalent to the RTX 4080 Super in the current-gen line-up (an RTX 5070 Ti or RTX 5080) would cost at least $800 (much more for the latter GPU), and then you need to add the cost of the other PC parts.

Handheld gaming PCs are still expensive, but more reasonable than an entire new PC build — and I tested Control Resonant on my Lenovo Legion Go S Z1 Extreme. That portable managed a solid 40fps on its handheld preset, and 70fps when dropping the resolution to 900p and enabling FSR Balanced with frame gen.

Overall, running Control Resonant at 5K2K is very demanding for any gaming PC, as expected, but I walk away shocked that it's even remotely possible given how much it elevates the gaming experience.

• XMG has announced price rises for its laptops due to the increased cost of components
• The laptop maker points out that DDR5 RAM for laptops is now close to 6x the price it was before the memory crisis
• We're also hearing that Micron has pulled the plug on its 2GB modules of GDDR7, and Nvidia's supply of VRAM could feel an impact from this

The RAM crisis sadly keeps rolling on and finding fresh pricing misery to heap on us, and that includes new laptop hikes and a hint that Nvidia GPUs could be in yet another spot of bother.

VideoCardz spotted the development with laptops, as manufacturer XMG announced on Reddit that its notebooks are subject to price hikes of either €100 or €150 for most models, or €300 in one case (that's for a Schenker portable, which is XMG's parent company, so it's all under the same umbrella).

Some laptop upgrades have got pricier, too, with a €120 premium on an RTX 5090 configuration, and as XMG observed: "The reason [for the price rises] is another increase in purchasing costs. This is no longer just about RAM and SSDs: PCBs, electronic components, CPUs, graphics cards and video memory are also contributing to the higher cost level."

In other words, everything is subject to price inflation, including the motherboard and associated chips, as well as the mentioned key components.

XMG highlighted data drawn from Geizhals (which tracks German retail prices) showing that DDR5 RAM for laptops is close to 6x the price it was before the memory shortage began (in mid-2025).

The laptop maker also made it clear that discrete GPUs and GDDR7 video memory have become more expensive, as have the prices of AMD and Intel laptop processors.

Also on the subject of GPUs, Wccftech reports that another slight worry is that Micron is no longer making 2GB modules of GDDR7 video RAM which are used in some Nvidia RTX 5000 GPUs.

Uniko's Hardware flagged up this news in a post on X, noting that these chips have "reached end of life" and that this is the case for all Micron 2GB modules, with only 3GB GDDR7 chips now listed on the memory maker's website.

With Micron apparently no longer supplying Nvidia with those 2GB modules, that means more of a squeeze on the availability of video memory for RTX 5000 graphics cards, at least in theory. This could hit stock levels and put a bit more pressure on pricing (which is already climbing fast).

Analysis: yet more pressure

We need to season the claims around Micron here, as this isn't a definitive statement from the company itself, and more to the point, it's only a minor supplier for Nvidia's gaming GPUs. Team Green gets the majority of its GDDR7 from Samsung and SK Hynix, and so the impact here should be relatively slight.

However, I'd argue that any impact is bad news given the current climate which is rapidly worsening for GPUs, particularly those from Nvidia which are heading upwards at a rate of knots for some models. The RTX 5090 has got completely ridiculous, the RTX 5080 is similarly out of hand, and even the RTX 5070 is now feeling the pricing heat, with a 30% price rise compared to two months ago in the US (and a similar picture elsewhere).

So, even small extra pressures are really not going to help Nvidia's GeForce GPUs here.

As for the laptop price hikes, this is a sign of the RAM supply times, and XMG is following other makers who have had to jack up prices (like Dell for example, which just bumped up the XPS 13 in a roundabout way).

It's a commendable level of transparency from XMG here, with the company making it clear that it isn't turning any extra profit from these laptop price increases. XMG also observed that other factors are in play with increased costs for notebook makers, and that includes rising air freight costs (due to conflicts in the Middle East) and difficulties with shipping by sea, too.

• Reports claim Apple is weighing an as-yet-unapproved enterprise AI server with two or four M8 Ultra chips, targeting 2029
• Apple is considering using Nvidia's NVLink Fusion to link the chips, with Thunderbolt-based Mac clustering falling short of data center bandwidth requirements
• The move comes as demand continues to be a key driver for Apple's higher-end Macs, with AI firms buying Mac minis and Mac Studios in bulk, even as Apple has turned enterprise buyers away

Apple has been working on plans for an enterprise AI server built on its own silicon with an interesting twist: it is seeking Nvidia's help on the networking front to make it work.

A report by The Information claims Apple is considering two configurations: one with two of its future M8 Ultra chips and a larger one with four, aimed at businesses that want to run AI inference on their own hardware rather than in a public cloud.

The timeline, however, is long and loose, with a potential launch in 2029, though the same report cautions that the project could be canceled before then, with neither the server nor any Nvidia arrangement finalized at the time of writing.

A market that Apple previously walked away from

If it ships, the machine would be Apple's first purpose-built server since the Xserve, which Apple announced it would discontinue in November 2010 and stopped selling at the end of January 2011.

Steve Jobs's explanation, in an email to a reader of French site MacGeneration, reported by MacRumors at the time, was blunt: "Hardly anyone was buying them." Gartner's data backed up that claim with an estimated 10,000 units being sold per quarter, a relatively low number for the tech giant whose numbers normally ran in the hundreds of thousands or millions for most of its other products.

Apple's suggested replacements to users were server configurations of the Mac mini and Mac Pro, desktops running server software rather than rack hardware. If the 2029 launch materializes, it would mean Apple's reversal would see it enter the server market 18 years after the Xserve stopped being sold.

This time, however, feels different: The Information has reported that OpenAI bought tens of thousands of Mac minis and Mac Studios to train AI agents through reinforcement learning, and that Anthropic has rented Mac minis through Amazon Web Services, as per Ars Technica.

Getting caught off guard by AI-centric demand

Apple reportedly was caught off guard by the AI-driven rush for its hardware, with no enterprise AI strategy, no business engineering team in place, and no specific developer relations staff to manage those orders. As a result, it also turned away businesses asking to buy access to its Private Cloud Compute infrastructure.

The project reportedly had a powerful sponsor: Apple's now-CEO John Ternus, when he ran Apple's hardware engineering team. However, shortages plagued it, leading many users to opt for Nvidia's DGX Spark and AMD's Ryzen AI Halo as viable alternatives.

The focus, however, remains on Apple's ability to interconnect a large number of chips or dies together: its current flagship, the M5 Ultra that ships in the new Mac Studio on September 22 2026, is Apple's first quad-die design, stitched together inside one package by the company's UltraFusion bridge, with up to 512GB of unified memory and 1.2TB/s of bandwidth, which users often stack for on-device AI needs.

A data center-esque, server-grade offering, however, would require considerably more power under the hood to meet its AI needs, which would also depend on how fast the interconnect between chips is; Apple's current limitation is Thunderbolt 5 speeds, which offer up to 10GB/s (or 80Gb/s) bidirectionally.

For context, Nvidia says the sixth-generation NVLink, available through NVLink Fusion, connects up to 72 accelerators at 3.6TB/s each. Comparing total two-way bandwidth, that is roughly 240 times a single Thunderbolt 5 connection's one-way capability of up to 15GB/s (or 120Gb/s).

Apple's own marketing hints at what that gap costs: It says four clustered Mac Studios deliver up to three times the inference performance of one, suggesting it already makes scaling tradeoffs of nearly 25% when linking devices.

This could explain why Nvidia is in the conversation at all: proven tech that could help Apple scale its AI server ambitions without the performance overhead it currently already has, at a time when its silicon would be even more powerful, might just be a no-brainer for the Cupertino-based giant.

• Apparently Nvidia's not launching RTX 6000 GPUs until 2028
• A leaker claims that these graphics cards have been delayed from the 2027 debut Nvidia originally planned
• Pushing back these GPUs makes sense given the RAM crisis, and some gamers are happy to wait anyway

Nvidia has pushed back its timeframe for launching next-generation graphics cards for gamers, and we're now hearing they won't arrive until 2028.

As ever, all this is theoretical – Nvidia hasn't uttered a peep about its RTX 6000 GPUs officially – but according to Kopite7kimi, a well-known leaker on X (via TweakTown), these products are now destined for a 2028 release which "means they have postponed again."

In other words, Nvidia was supposedly looking at a launch for next year, but the leaker now believes that isn't the case.

Another leaker, Moore's Law is Dead on YouTube, recently said that Nvidia is planning a 2027 launch for the RTX 6000 series, so this directly contradicts that idea. However, it's possible that this was the case, but Team Green has only just changed its mind.

Analysis: a sensible move

Of course, both of these assertions are just rumors, and we need to regard them with an equal amount of caution – but I think it makes more sense for Nvidia to delay the release of its next-gen GeForce GPUs until 2028.

Why? I have three predictable words for you: the RAM crisis. With the memory shortage – which also impacts video RAM – rapidly worsening as 2026 progresses, and predicted to descend further into nightmarish pricing territory in 2027, next year seems like a bad move for launching a family of next-gen GPUs.

Especially when you consider that the first graphics cards to emerge will doubtless be the flagship RTX 6090 and the 6080. How expensive will they be in such a climate, given that the RTX 5080 is already exorbitantly priced? The latter has reached around $1,500 in the US at this point, and is at similar levels in other regions, such as £1,350 in the UK (that's the cheapest GPU in both cases at the time of writing, I should make clear). And let's not even talk about the RTX 5090 which has got completely ridiculous.

Remember that these next-gen GPUs are going to require more memory to be an upgrade over their predecessors, and are also likely to use more expensive VRAM modules.

3GB modules will likely be used by Nvidia, or maybe even 4GB or 6GB chips, which as Wccftech reports, are rumored to debut in the next year or two (which would align with a 2028 release for RTX 6000, in theory). That's according to another prominent leaker over in Asia, Golden Pig Upgrade, as posted on Weibo.

All expectations are for the RTX 6000 series to be pricey, which is why launching them at what might be the height of the RAM crisis – 2027, going by current predictions – doesn't make a whole lot of sense. The following year seems a lot more likely, when hopefully the crisis has started to settle down and pricing is at least stabilizing to some extent (although some believe we're looking longer term for that to happen, more towards 2030).

Whatever the case, some gamers are getting jaded with the market. One of the replies to Kopite7kimi's post on X simply says of the next generation of Nvidia GeForce GPUs: "Nobody cares, nobody can afford it anyway." Others – like this Redditor, who says "I'd rather they delayed the launch to consumers" – indicate that they're happy enough to wait due to pricing or stock issues (launch quantities of GPUs would likely be thin on the ground).

The other question is whether we'll see those RTX 5000 Super refreshes that have been long rumored. I'm doubtful at this point, and I think Nvidia may be inclined to rely more on the AI and software side of the equation, principally DLSS 5, to impress gamers, rather than new (stupidly pricey) hardware.

• AWS deal with Nvidia adds 2 million Blackwell Ultra, Rubin, and Rubin Ultra GPUs, on top of more than 1 million committed in March 2026
• Totals more than 3 million Nvidia GPUs due before 2029, a ~1.9x multiple for each of Amazon's roughly 1.58 million employees
• Nvidia's CFO has said deliveries start this quarter, not in 2027, but neither company says what it counts as a GPU or what the deal costs

Amazon ended 2025 with about 1.58 million full- and part-time employees, most in fulfillment centers and delivery operations, with a much smaller share working in its AWS data center division (estimated at around 246,000).

By mid-2028, Amazon Web Services expects to have taken delivery of roughly twice that many new Nvidia GPUs, based on a recent deal it made with Nvidia.

The latest deal was announced on August 26, the same afternoon Nvidia reported quarterly earnings, and will see AWS plan to deploy two million additional Blackwell Ultra, Rubin, and Rubin Ultra GPUs across its global infrastructure in 2027 and 2028. This is on top of over a million Nvidia GPUs that AWS committed to at Nvidia GTC 2026 in March, and which Nvidia's Ian Buck told Reuters would ship from 2026 through 2027.

A mammoth deal that dwarf's Amazon's entire payroll

Together, this all amounts to over three million GPUs in play between the data center giant and the chip designer, which is also the world's most valuable company at the time of writing. At 3 million GPUs against a workforce of 1.58 million, that's nearly 1.9 Nvidia GPUs per employee.

This is only Amazon's commitment to Nvidia; it also has its own custom Tranium3 GPUs, which the company says are "nearly fully subscribed" or "almost fully booked" as of mid-2026.

At the same time, Amazon is not increasing job roles on its end; it cut around 30,000 corporate roles between October 2025 and January 2026, a restructuring its top HR executive, Beth Galetti, described as "reducing layers, increasing ownership, and removing bureaucracy."

Building on an already solid base

While AWS's current GPU base from Nvidia, consisting mainly of H100 and H200 GPUs, is smaller than Microsoft's 485,000 count, it is hardly a rounding error, with Omdia estimating that Amazon bought 196,000 Hopper GPUs in 2024 alone.

The interesting part is determining what counts as a GPU: Nvidia and Amazon's joint statement about 2 million GPUs across calendar years 2027 and 2028 might mean two very different things depending on how you count GPUs.

At GTC 2025, Nvidia said it would count Rubin-generation GPUs by compute die rather than by package, which is why the Vera Rubin rack was first unveiled as NVL144. It reversed course before CES 2026, and the flagship now ships as Vera Rubin NVL72, counting 72 two-die packages. To make matters a bit more complicated, Rubin Ultra, due in 2027, puts four compute dies in each package.

Whether the companies count each die as a separate GPU or the whole package as a GPU could make the numbers skew heavily in either direction. The deal also includes a national security element: approximately 100,000 GPUs are earmarked for US government AI workloads at Impact Level 6.

Amazon's limitation here is supply, not demand; AWS revenue rose 37% year over year to $42.2 billion in the second quarter, and its contracted backlog reached $496 billion, up from $364 billion three months earlier, as CloudTech News reported.

On Amazon's July earnings call, CEO Andy Jassy said capacity would fall short of demand this year and likely next, adding: "In fact, the demand we already have for 2028 is striking."

In addition to its own custom ASICs, including Tranium3, Amazon is gearing up for a few fiscal years of eye-watering AI infrastructure spend and AI demand combined, and it better be right: Amazon's free cash flow has already turned negative as it looks to fund its bid to be one of, if not the largest hyperscaler in play for the foreseeable future.

Via StorageNewsletter

• Samsung has backed a tiny Dutch chipmaker targeting Nvidia
• Euclyd raised $231 million before shipping its first commercial chip
• Euclyd plans to sell complete AI systems directly to enterprises but will not deliver its first hardware until 2028

Samsung has joined a group of investors backing a small Netherlands-based chip designer aiming to challenge Nvidia's dominance in artificial intelligence hardware.

The startup, called Euclyd, closed a funding round worth $231 million, drawing support from several firms beyond the South Korean electronics giant.

According to Bernardo Kastrup, Chief Executive of Euclyd, commercial hardware from the company will not reach customers until 2028

A chip built to rival Nvidia's grip

Euclyd's Series A round totalled €200 million and was co-led by Somerset Capital Partners, the Scaleup Europe Fund, and Innovation Industries, alongside Samsung.

In an exclusive interview with CNBC, Kastrup said that the capital would support two separate lines of business going forward.

One involves selling physical hardware and rack systems directly to enterprises wanting to run their own AI inference securely on-site.

The other involves licensing the underlying intellectual property to firms that want to build their own chips using Euclyd's designs.

Founded in 2024, the company is developing a chip system built around a different architecture than conventional graphics processors, covering both the processor and memory layers.

Nvidia's GPUs were originally built for video games but became central to AI training and inference and pushed the company to become the world's most valuable.

That dominance now covers the market for the very highest performing chips available today, though rivals are emerging quickly.

“AI is becoming a foundation of economic growth, scientific discovery and national competitiveness, but its potential will remain constrained unless we fundamentally change the infrastructure beneath it,” Kastrup said.

Why Samsung's involvement matters beyond cash

Kastrup revealed that Samsung's contribution extends well past the money it committed to the round.

"Samsung can help us in more ways than money. They are one of the biggest memory manufacturers in the world,” said Kastrup. “They do a lot of engineering, they know a lot about systems, they know the supply chain, they have a huge network."

Although Euclyd expects to commence shipping its chips in two years, the company plans to serve thousands of enterprise customers by 2030.

The company's systems remain unproven at any meaningful commercial scale so far, leaving real-world performance an open question.

Several major technology firms are pursuing similar paths away from dependence on Nvidia's processors for AI workloads.

OpenAI announced in August that its first in-house AI chip, named Jalapeño, delivered industry-leading speed and efficiency.

Google, Amazon Web Services, and Meta are each developing their own chips for internal AI use as well.

Whether a small Dutch startup with no shipped product can meaningfully dent Nvidia's position remains genuinely uncertain given the scale involved.

• Google and Nvidia are just two of the tech giants behind the new AI Energy Management Alliance
• The group is focused on building future data centers that coordinate power usage with the grid
• Moving training to off-peak hours and balancing on-site energy are some of the solutions

Emerald AI, Google and Nvidia have come together to form the AI Energy Management Alliance (AEMA), and it could be exactly what we need to tackle rising energy consumption within the data center field.

Together, the group hopes to deploy flexible AI data centers that can dynamically adjust how much electricity they consume from the grid based on a number of different conditions.

This important development comes in response to rising power availability pressures, which are leading to years-long delays for many new projects.

Future data centers could adapt how much power they use

In an announcement, Nvidia Head of Sustainability Josh Parker explained how these future campuses could shift compute workloads, discharge storage, use paired generation or respond to other system contingencies to dynamically change how much electricity they're drawing from the grid at any given time.

The concept involves shifting high-compute tasks like training to off-peak times, using on-site batteries or generation in conjunction with the grid and exporting excess stored energy back to the grid during times of stress.

Crucially, the AEMA must now set out technical specifications, performance metrics and open standards to allow this to happen at scale.

And while only Emerald AI, Google and Nvidia are behind the alliance from launch, a number of additional partners are set to join the scheme. SiliconANGLE reports that Anthropic will be one of them.

The concept itself isn't new – Google also announced earlier this year that it had over 1GW of demand-response capacity – but doing so at scale would likely make a considerable impact across the board.

Both Nvidia and Emerald AI have already developed software to handle power consumption in relation to the grid.

"By creating a common framework for performance, reliability and collaboration, AEMA aims to help the US build the infrastructure of intelligence at the speed and sustainability the moment demands," Parker wrote.

All of this, of course, comes against a backdrop of mounting energy pressures. Recent Bloomberg data suggests that US data centers alone could use as much natural gas as the entire of Germany and Japan combined by 2035, with a similar Moody's study projecting that total US data center energy consumption could top 426 TWh by 2030.

• The cheapest available US RTX 5090 now costs over $6000 against a $1,999 launch price, with third-party listings often running past $9,000
• The GPU that already pushed past $3,500 in Feb 2026 owing to memory constraints driving prices higher has now hit fresh highs at multiple retailers
• The move is linked to AI demand, as data center operators have entered the fray to secure compute despite Nvidia's GeForce driver license having banned 'data center deployment' on paper since 2017

Six weeks ago, the story was ultra-rare books being fed spine-first into high-speed scanners so that AI labs could train on text written before chatbots polluted the corpus.

Focus, however, has shifted considerably to AI labs bringing the same voracious appetite to Nvidia's flagship consumer-grade Blackwell GPU, the RTX 5090, which has essentially disappeared from shelves unless you want to pay a 200 percent or higher premium to grab one.

Photographs circulated by Hong Kong outlet HKEPC, and picked up by multiple publications including Guru3D, VideoCardz, and TechPowerUp, appear to show wooden pallets stacked with boxed, retail GeForce RTX 5090 cards at AI server assembly sites, waiting to be unpacked and slotted into multi-GPU rigs.

The sky is the limit for Nvidia's flagship consumer card?

This isn't the first time Nvidia's RTX 5090 GPUs have been deployed en masse on multi-GPU, AI-centric rigs, but this time feels different, even compared with previous shortages.

For context, Nvidia launched the RTX 5090 at $1,999 in January 2025. Tom's Hardware's price tracker, updated on September 14 2026, lists the cheapest available US card at $6,389, nearly 3.2x the former.

PCGamesN reported the same week that first-party stock at Amazon, Newegg, and Best Buy had essentially vanished, leaving third-party marketplace listings above $6,000 and, in some places, over $9,000.

The pictures in circulation don't show RTX PRO workstation GPUs, but rather GPUs from MSI, Gigabyte, and ASUS that the average gaming enthusiast or consumer would pick off a shelf.

An AI-generated image to create more FOMO?

The images have, however, been disputed by some quarters: Kevin Hofer at Swiss retailer Digitec, one of the few writers to examine the images rather than republish them, pointed at the box lettering in one frame and asked what a "50B0" or a "50R0" is supposed to be, concluding that AI was probably involved in producing the picture. Twitter has also added a "made with AI" moniker to the image in question.

TechPowerUp and TweakTown both carried the same warning in softer form. Guru3D, to its credit, noted that the report explains neither why these firms chose the RTX 5090 nor the scale of the purchases.

Coverage like this, even with disclaimers to the contrary, can create a "fear of missing out" (FOMO) where price increases become a self-fulfilling prophecy as businesses and consumers alike move purchases forward to avoid paying a further premium.

Examining the value that an RTX 5090 brings to the AI table

The Nvidia RTX 5090 is its most powerful consumer-grade card to date, packing a Blackwell-based GB202 chip and 32GB of GDDR7 memory, which checks multiple boxes for local AI use cases and gamers looking to max out games at 4K or higher.

Nvidia's GeForce driver EULA has, since late 2017, included a clause titled "No Datacenter Deployment" that should, at least on paper, prevent data centers from stockpiling or using these.

The chip designer, currently the world's most valuable company, has previously implemented technological restrictions such as LHR (Lite Hash Rate) on its RTX 3000 series GPUs to prevent GPU miners during the Ethereum gold-mine rush from hoarding supply.

It has also released a China-specific RTX 5090 D, which reduces AI performance while matching the RTX 5090 in gaming throughput. The RTX 5090 D, created to meet AI export restrictions, shows that Nvidia can limit the RTX 5090's AI performance, but it remains relatively indifferent to a market that was its core revenue source only a few years ago.

The RTX 5090's price hike, at least in part, may be due to the RTX PRO 6000's staggering, out-of-control pricing, which Nvidia nearly doubled to $16000. The underlying cost driving the price hike is the GPU's 96GB of VRAM, which also makes up a sizeable, if not dominant, chunk of its total bill of materials.

An RTX 5090 comes with 1/3rd the VRAM on offer, but nearly the same compute performance, and with AI-centric algorithms often assisting in breaking up tasks between GPUs and memory pools, even as the total memory pool remains similar, a triple stack of RTX 5090 GPUs should comfortably outperform the RTX PRO 6000 in certain tasks.

At the same time, Chinese OEMs have reportedly managed to slap on as much as 96GB of VRAM on an RTX 5090, which could make it an even hotter commodity in a country where both the RTX PRO 6000 and the RTX 5090 are export-restricted but do manage to make it on the regular.

The RTX 5090 is a manufacturing problem with a memory bottleneck attached for AI consumers, but with the alternatives having doubled in cost recently, AI consumers are looking to save money wherever possible, and both the stock RTX 5090 and a potentially modified one are some of the best candidates in a market that has already exhausted most of its last-generation supply of the same.

• Nvidia fits 28 GDDR7 memory chips onto one workstation graphics card
• The RTX PRO 5500 carries 84 GB of onboard GDDR7 memory
• Fourteen memory chips sit above the PCB and fourteen below

Nvidia has rolled out a new professional graphics card that carries an unusually generous pool of onboard memory for its class.

Nvidia says the RTX Pro 5500 workstation edition is built on Blackwell architecture and ships with a full 84 GB of GDDR7 memory.

That memory is attached to a GB202 processor carrying 21,760 CUDA cores, the same count found on the GeForce RTX 5090.

How Nvidia packed in so much memory

This particular graphics variant is certified strictly for professional workstation duties rather than for everyday consumer gaming applications and setups.

Reaching such an enormous memory capacity required an unconventional physical layout spread across the entire circuit board of this graphics card.

Nvidia mounted 14 GDDR7 modules on the front of the board next to the processor and another 14 modules on the rear.

That double-sided arrangement, often described as a sandwich configuration, squeezes a full set of 28 memory chips onto one compact card.

Each chip operates at roughly 27 Gbps per pin, which counts as a routine GDDR7 speed rather than the fastest option currently sold.

The resulting 416-bit interface yields about 1,398 GB/s of aggregate bandwidth, a weirdly narrow width given the enormous capacity involved.

This is unusual because GPU memory controllers usually operate in even pairs, producing familiar widths such as 256-bit, 384-bit, or 512-bit.

A 416-bit path implies 13 separate 32-bit memory channels, an oddly uneven number rarely seen in current graphics processor designs.

Buyers can also split the card through multi-instance partitioning, choosing either one full 84 GB block or two separate 42 GB segments instead.

Nvidia rates the board at a hefty 600 W power draw and builds it strictly for rack-mounted workstation deployment rather than desktop towers.

It ships in both air-cooled and liquid-cooled variants, and the air-cooled version relies on high-speed server fans bolted directly onto a sizable heatsink.

Where it sits against Nvidia's other workstation cards

Within the wider RTX Pro family, this graphics card lands just below the flagship RTX Pro 6000 in raw specification terms.

That top-tier card carries a much higher 24,064 CUDA cores alongside a full 96 GB of dedicated video memory as well.

It comfortably outpaces the RTX Pro 5000, a card limited to only 14,080 CUDA cores and a maximum of 72 GB.

Given those figures, the chip sits much closer in raw specification to the top-tier card than to its nearest numbered sibling below it.

Nvidia seems to be prioritizing sheer memory volume for large datasets over the kind of bandwidth efficiency typically prized in compute-heavy rendering workloads.

Whether that particular trade-off proves worthwhile in practice will likely depend heavily on the specific professional applications each buyer intends to run.

At the moment, there is no official price or launch date, but a steep asking price seems all but guaranteed once details emerge.

The most realistic customers for this card will likely be enterprise buyers rather than everyday consumers given its likely price and power demands.

• AMD is rumored to be working on its own version of DLSS 5
• A leaker says that the neural rendering tech will be launched with next-gen RDNA 5 GPUs
• That means we could be waiting until late 2027 or 2028 to see this debut, which might leave AMD lagging behind Nvidia considerably

AMD could be working on its own take on Nvidia's DLSS 5, and apparently this alternative attempt at neural rendering will be for its next-gen RDNA 5 graphics cards.

VideoCardz spotted that a known leaker in the GPU field, Kepler L2, posted on the Anandtech forum in a discussion around RDNA 5 speculation to say that: "AMD will have their own version of 'Neural Lighting' like DLSS5 next-gen."

Kepler then clarified that he didn't mean a version of Ray Reconstruction, but a take on DLSS 5 (neural rendering), and that next-gen RDNA 5 GPUs would be required because "this stuff is pretty heavy" (as in resource-intensive).

In a further post, Kepler elaborated: "There's a lot of issues with Nvidia's approach [with DLSS 5], but it's possible to have a version of this with minimal hallucinations and 'AI slop' look."

He then suggests a way that AMD could work its version to improve on DLSS 5, including giving the AI "direct access to 3D game data such as geometry, ray data, depth buffers, materials, etc so it doesn't have to guess and can accurately know how each object in a scene should interact with light."

Kepler's outline of a proposed alternative for AMD would have a separate model (AI) for lighting improvements, and another different model solely for the shading of complex surfaces. DLSS 5 bundles these together with its neural rendering system, and adds color-grading on top — to adopt what the AI judges as more natural colors — which Kepler advises that AMD should dispense with entirely.

Analysis: a race against time – or a chance to get it right?

I won't pretend to be able to critique what Kepler is suggesting as an alternative for AMD and the technical nuances therein, except to say that the idea is clearly to achieve a result that looks less like it's been 'messed' about with by AI and minimizes any 'slopification' therein — preserving more of the original artistic intent.

And that seems a laudable goal, of course, and it also brings up how AMD is in an interesting position here in lagging behind Nvidia with its graphics tech (which has, of course, been the case from the start, with FSR following DLSS — version 2.0 of Nvidia's upscaling tech was already in play, in fact, when FSR 1.0 arrived). Team Red can sit back for now and see how DLSS 5 plays out, as more games emerge that use it, and gauge the reaction of gamers, and forge its own neural rendering tech to tackle any perceived weaknesses.

The performance hit will be an obvious area to target, and if AMD can bring that down — it's substantial with DLSS 5 as it is now — that will obviously be a big win. That said, I'd expect Nvidia will be working with some haste to make DLSS 5 more performant, and as with DLSS 1, the tech will be improved considerably by the time Team Red has its own version out.

This is the other side of the equation here, where Nvidia could win out. With RDNA 5 GPUs not expected to arrive for at least a year, and quite possibly longer (the RAM crisis isn't helping), there's quite a lengthy timeframe on any AMD rival to DLSS 5. By the time it arrives, DLSS 5.5 may be in town with considerable improvements in tow. There's already been a notable turnaround in the perception of DLSS 5 with its official launch (compared to Nvidia's sneak preview earlier this year).

This is, of course, assuming AMD is indeed working on its own neural rendering technology, but I'd be surprised if this wasn't the case. For now, though, the word of a single leaker needs to be thoroughly seasoned, as ever, even though it's believable enough that Team Red would be aiming to use this on RDNA 5 graphics cards.

• Nvidia's RTX 5090 is now over $9,000 in the US
• A new rumor suggests that RTX 6000 gaming GPUs are coming in 2027, maybe in the first half of the year
• If true, the price tag that's attached to the RTX 6090 will likely be colossal, and lower-tier GPUs could be pricey too

Nvidia's RTX 5090 flagship has hit new heights in terms of ridiculous asking prices, just as a new rumor suggests that RTX 6000 (Rubin) GPUs are coming in 2027 — earlier than anticipated, and to say the price of those next-gen offerings is a worry given the current climate is a huge understatement.

A Redditor pointed out the current state of RTX 5090 prices, which are close to commanding $10,000 in the US for some models. Notably, the Asus ROG Astral RTX 5090 will set you back a princely $9,659 on Newegg US, although that's not sold directly by the retailer.

Indeed, none of the big retailers have any stock of the RTX 5090 anymore, and the only graphics cards you can buy are from third-party marketplace sellers, often shipping from Asia (that ROG Astral GPU comes from Hong Kong).

You can find more expensive RTX 5090s than that if you want to get the gold-plated version (Dhahab Edition) of the ROG Astral, which is now $11,999. The very cheapest RTX 5090 that I could find at the time of writing is an MSI Ventus model that's priced at $6,549 on Newegg.

What about outside of the US? Price inflation is rampant in other countries too. If we take the UK as an example, you can pick up an RTX 5090 for around £4,500 on Overclockers UK, which is still an eye-watering asking price. (And those are the cheapest GPUs — they go up in price from there to £6,600 for the aforementioned Asus ROG Astral RTX 5090).

It's an alarming situation, and one that heaps worries on top of a new rumor about the RTX 6000 launch as aired by Moore's Law is Dead (MLID) on YouTube (via TweakTown).

Previous rumors have contended that Nvidia's looking at 2028 for the arrival of its next-gen GeForce graphics cards, but a source at the firm told MLID that this is incorrect, and Team Green is aiming for a 2027 launch. Moreover, the current plan is still to get the RTX 6000 ball rolling for PC gamers in the first half of next year, meaning within the next nine months.

There's an obvious sticking point here, though: if the RTX 6000 generation is this close, how come we haven't had much in the way of leaks yet, and nothing of any real substance? The source claims that this is due to Nvidia's management successfully clamping down on leaks with the next-gen products.

The first offering will presumably be the RTX 6090 (or maybe an RTX 6080 as well), and it's going to have some kind of interesting spin on the design — though we aren't told what. It could have beefed up hardware to better cope with running DLSS 5, perhaps, as that would make sense — something to help deal with the performance hit of neural rendering would clearly be very welcome.

Analysis: house deposit or GPU?

There are obvious problems with a possible RTX 6000 launch next year, if we take this rumor at face value. As noted, the RTX 5090 is now being sold for beyond ridiculous prices — and so how much is an RTX 6090 going to run? The fiscal equivalent of an ultramarathon, presumably, and with AI demand likely coming into play with this GPU, the next-gen flagship could be 'unobtainium,' as the disgruntled gamers out there are already predicting.

When you consider that the RAM crisis is going to get worse next year, going by the more recent forecasts from analysts and industry insiders, video RAM is going to be more problematic — and how much of the stuff will be on an RTX 6090? — it all adds up to a frightening prospect for an MSRP. And the likely reality is that the price to actually buy the next-gen Rubin gaming flagship will blow well past that towering recommended cost. (The RTX 6080 won't likely be much different, either.)

For all these reasons, I find it difficult to believe that Nvidia will go ahead and launch the RTX 6000 series next year. Surely it makes sense to wait longer, although what Team Green may know is that 2028 isn't going to be any better for RAM price inflation, and so perhaps that's why next year is greenlit for next-gen GeForce graphics cards to arrive. It's a sobering thought. As one Redditor observed: "Can't wait to decide if I want a down payment on a house or an Nvidia GPU."

• Nvidia CEO Jensen Huang and Anthropic CEO Dario Amodei discuss AI at Dreamforce 2026
• Amodei says the pace of progress had surprised Anthropic
• Huang calls for greater safety, but hails the advantages AI can bring

Some of the world's leading AI minds have shared their opinions on the future of the technology, revealing some intriguinly diverging viewpoints.

Nvidia CEO Jensen Huang and Anthropic CEO Dario Amodei were both guest appearances during the opening keynote of Dreamforce 2026, where they were both quizzed by Salesforce CEO Marc Benioff on their opinions and views on the future of the technology.

The pair had some interestingly differing viewpoints, showing the trouble facing not just internal and external stakeholders, but also lawmakers and regulators.

Jensen vs Dario?

First up was Amodei, whose Claude platform has become increasingly partnered with Salesforce in recent months, culminating in the recent launch of Claudeforce.

Asked by Benioff about the state of the AI market right now, Amodei likened it to the motor industry, where safety records can be conflated, but companies can learn from each other - and he also seems keen to open things up internationally to set standards.

“I think that’s the way to lead the industry forward, to set an example, to say that everyone can always be better,” he pointed out.

Benioff also asked Amodei about the biggest surprises has had while developing his technology over the last decade.

The Anthropic head replied the "pace of the progress" - not just in the development of AI technology, but the economic impact of it too - noting his company didn't appreciate how quickly certain companies would grow and become central to everyday life.

Finally, Benioff asked what actions Amodei would like the public to take when using AI.

Amodei replied that even if development of AI technology was frozen right now, we'd only be using maybe 5-10% of the possible value we could get.

Making use of all the information with platforms such as Claude could open up new areas of business - "there's so much diffusion left," Amodei says.

Huang was much more positive in his appearance, leading Benioff in a walkabout around the keynote theatre as the pair discussed AI.

"We're going through a new industrial revolution," Huang pointed out,

However he did urge caution in some areas, particularly around safety.

“Safety is paramount in a lot of ways. It’s job one,” Huang said. “However, safety is an engineering problem.”

Speed and safety are not mutually exclusive, Huang noted, pointing out that companies should pace themselves until they are sure they are releasing something that the market will appreciate.

“It’s a false choice,” Huang told Benioff. “You could definitely have both at the same time.”

When it comes to model protections, Huang said that companies should, “run as fast as you can...but if you feel at any given point in time the company’s out of control or the product’s not going to be safe, take a pause and make sure you get it right.”

Wrapping up, Benioff asked Huang about his vision for the next few years - and what he wants to see from Salesforce.

Huang says he wants to "Agentforce every company", outlining an aim of "really deploying AI".

"You don't want to be left behind, this is too important of a technology revolution," Huang noted, "if it doesn't work for you straight away, don't give up on it."

"The most important thing - don't get left behind...the sky's the limit for us."

• Japanese retailer warns of further GPU price hikes
• The retailer doesn't specify which GPUs will see increases
• Consumers are urged to buy now, but GPUs are already overpriced and unaffordable in many cases

The PC hardware market is still in disarray, with RAM shortages impacting almost every component, and unfortunately, matters could get even worse for GPU hunters.

As reported by VideoCardz, Japanese retailer Applied has warned consumers about potential price hikes for GPUs (yet again). It's unclear which GPU models this might apply to, but the retailer urges PC owners looking to upgrade their GPUs to buy as soon as possible.

VideoCardz also highlights that Japanese retailers only recently adjusted GPU prices upwards. In August, stores in Akihabara warned that pricing would be around 20% to 30% higher than usual.

Considering the recent rumblings of price hikes from Nvidia's board partners, it's fair to suggest that these increases will likely apply to other regions outside of Japan (assuming there's anything in these rumors).

If it wasn't exactly clear already, these price hikes are caused by the RAM crisis which is affecting all manner of PC components. Graphics cards are hit due to the rising cost of the video RAM on board.

Panic buying still isn't the best solution

PC owners are now hard-pressed to find affordable hardware upgrades, and it's been that way since the start of 2026. The RAM crisis is showing zero signs of slowing down, too, especially with reports from Samsung and industry analysts suggesting that the memory shortage will last past 2027 or beyond that, even.

Despite that, I still don't think it's the best idea to panic buy now when it comes to a GPU. Many Nvidia GeForce or AMD Radeon third-party GPUs are either unaffordable or overpriced at various major retailers. Prime examples of the former are the RTX 5080 at $1,699 at Best Buy in the US currently, while the RTX 5060 remains overpriced between $450 and $500, despite its $299 MSRP.

The same goes for AMD's Radeon RX 9070 XT, which has a $599 MSRP but can now cost up to $929 (specifically the Gigabyte overclocked model). The Radeon RX 9060 XT (16GB) also sits at least $200 to $300 above its MSRP, and the list goes on (in the US and elsewhere).

Simply put, buying a new GPU doesn't seem worth it unless you're a gamer looking to dive into the PC ecosystem for the first time. That's a harsh reality to accept, but panic buying might do more harm than good by increasing demand (which could lead to further short-term price hikes).

• GPUThor is the first Rowhammer attack to defeat ECC on Nvidia GPUs and reach a root shell on the host
• It affects four Ampere workstation cards with GDDR6 memory, the RTX A4000, A4500, A5000 and A6000
• Non-uniform hammering, enabled by defeating GPU memory coalescing and finding that Target Row Refresh (TRR) mitigations fire once every 72 refresh intervals, delivers effectively 6.6 times the hammering intensity of prior attacks

Four researchers at the University of Toronto have disclosed GPUThor, a Rowhammer technique that breaks the error correction Nvidia has spent the past year recommending as the defense against exactly this class of attack.

Chris S. Lin, Joyce Qu, Aditya Rajeev and Gururaj Saileshwar are expected to present the paper outlining their approach and subsequent findings at ACM CCS 2026.

The attacks target Ampere-generation workstation cards with GDDR6 memory, specifically the RTX A4000, A4500, A5000 and A6000, and it turns an unprivileged CUDA program into a root shell on the host.

An attack that matters much more than its predecessors

Rowhammer works by repeatedly activating a DRAM row until charge leaks from cells in the physically adjacent rows and flips their bits. The attacker never touches the victim's data directly, making it an excellent precursor to tampering, sandbox escape, and privilege escalation, among other things.

This approach is possible despite an in-chip countermeasure called TRR that accompanies ECC-enabled chips on these GPUs. TRR aims to prevent Rowhammer attacks by tracking how frequently specific memory rows are activated and automatically refreshing adjacent rows before a malicious bit flip can occur.

This approach works well on paper and, when Rowhammer attacks hammer uniformly, offers decent protection. The problem arises when approaches such as GPUThor use non-uniform hammering, which is much more likely to succeed. Non-uniform hammering is not exactly new, having already succeeded on the CPU side of the spectrum thanks to the well-documented Blacksmith attack vector.

GPUThor is the third attack from broadly the same group in eighteen months, following GPUHammer in 2025 and GPUBreach earlier this year, and it matters because the previous two stopped working the moment a user typed the command to enable ECC.

ECC was essentially Nvidia's go-to response to reported Rowhammer attacks, but it may no longer be a solution in its current state. By hammering non-uniformly, the researchers opened another attack vector for Nvidia's Ampere-based GPUs, which are overwhelmingly affected by the technique.

With ECC disabled, GPUThor produced 72,000 to 377,000 bit flips per gigabyte across four Ampere-based cards, with Nvidia's A5000 being reported as the most vulnerable. That approaches the roughly 550,000 flips per gigabyte that Blacksmith achieves on DDR4, which is cause for concern: GPU Rowhammer is now in the same league as CPU Rowhammer.

When enabled, the researchers said ECC protectors reduced, but did not completely mitigate, the issue: they delivered double-bit errors and 2 triple-bit errors that the technique 'fixed' by choosing the wrong value.

On an RTX A6000 with ECC enabled, the GPUThor technique forces one GPU reset every 2 hours, killing all running processes on the GPU; as a result, the GPU flags itself as RMA-read within a day.

The team disclosed the attack pattern to Nvidia on 29 April 2026 and subsequently to Google, Microsoft and AWS, and held the work until the 25th of August. A code release is scheduled for the 15th of November, even though there is currently no CVE information or patch being deployed to address the issue.

• GPT-6 Astra has arrived, and it's being called AGI
• This includes by Nvidia CEO Jensen Huang
• Huang's celebratory X post also promotes Nvidia's GPUs, saying we need to get 400K of them online

Over the weekend, Nvidia CEO Jensen Huang took to social media to celebrate OpenAI’s latest achievement — “AGI has arrived” in the form of GPT-6 Astra. And of course it was developed using over 100,000 NVIDIA GPUs.

According to OpenAI, Astra represents a "significant step forward" for AI, with OpenAI's president Greg Brockman also saying "welcome to the AGI era" as Astra was debuted — with the company promising that GPT-6 boasts incredible aptitude at tasks across software engineering, science, mathematics, and professional workflows.

It’s also said to be much better at agentic work, meaning you can delegate tasks to GPT-6 with less fear that the AI will misunderstand your intent as it works independently.

We haven’t yet given GPT-6 a full shakedown — the update is steadily rolling out to subscribers — but if OpenAI’s claims live up to the hype, Astra could be quite the capable tool, and may actually live up to those early AGI claims, even if this isn’t the first time Jensen Huang has heralded the arrival of AGI.

But what is AGI, and why does it matter if Astra is AGI or not? Well, as Huang himself suggests, AGI matters because it’s a reason to quadruple the number of Nvidia GPUs OpenAI is using.

What is AGI?

Let’s address the elephant in the room. AGI, or artificial general intelligence, isn’t a well-defined term, so judging if we have or haven’t achieved AGI is a bit like judging if a piece of art is a masterpiece or not.

Broadly speaking, AGI is an artificial system that can beat humans across any task that involves thinking or reasoning. Crucially, it should also be able to adapt to new problems by applying what it has learnt in training. For example, an AGI could learn a new skill to overcome a challenge posed to it without being retrained or reprogrammed — it just applies what it knows to evolve itself.

Think J.A.R.V.I.S from Marvel. It’s a very powerful assistant that helps Tony Stark across all fields, even how to deal with otherworldly threats like Loki, without needing to be trained on each individual problem.

Does AGI matter?

Okay, but does it matter if it’s AGI or just a very, very smart AI?

For most of us, not really. An AI doesn’t need to be very good at everything to be a majorly useful tool. It doesn’t matter if your AI doctor can understand the deep complexities of the vast universe, it just needs to be able to diagnose your illness and suggest treatment. An AI lawyer doesn’t need to be able to comprehend five-dimensional topology to defend you in court.

It's not just me saying that; Jensen Huang recently admitted that AGI doesn’t matter — this was before his social media praise for OpenAI — noting that the term is effectively meaningless.

However there’s one important reason for achieving AGI, and it’s about reaching what comes after: ASI, or artificial superintelligence.

ASI would be an AI no human could ever hope to match, let alone beat. It would be able to grapple and solve problems humans aren’t capable of even understanding. AGI is distinctly not ASI, but its ability to self-train and develop tools for itself suggests that an AGI will eventually construct ASI through a process called recursive self-improvement.

For Hitchhiker’s Guide the Galaxy fans, it’s like how the supercomputer Deep Thought couldn’t find the ultimate question of life, the universe, and everything, but could build the computer which would be able to find the question.

Perhaps more importantly, AGI is an investment buzzword, and there’s more than a hint of that in Huang’s celebratory post. Because while he says Astra is the product of 100,000 Nvidia Grace Blackwell NVLink72, he adds that if we want to see bigger and better improvements we’ll need to bring 400,000 GPUs online next.

To do this OpenAI and Nvidia will need a heck of a lot of investment, and will also need to build more data centers, just as communities across the US and rest of the world are pushing. back against their construction.

The arrival of AGI could represent the last opportunity for investors to make it big in AI, or any stock, before ASI takes over productivity. So they need to invest as much as they can now (or keep their investments in AI) before they’re left behind. For data centers, the big concern is they are pollution nightmares that merely take from a community with no promise they’ll actually succeed long enough to give back. The advent of AGI would suggest that the AI hype has been justified all along — the bubble won’t burst, and local communities could finally reap the reward of accepting data centers.

I’m not saying Huang or OpenAI are definitely misleading us, or even just being hyperbolic. It's possible that Astra is in fact our first taste of AGI. I’m just pointing out that companies and CEOs have a massive incentive to claim that we’ve achieved AGI, even if they later make the same claim about the next new model — I won’t be surprised if GPT-7 is later called 'the debut of AGI'.

My point is that we won’t really know if or when we achieved AGI until much later, when we can look back with hindsight and see when our AI models reached a turning point in their ability to evolve.

In the moment, though, we will know if an AI is useful and can provide some genuine benefit to us. We’ll do that as a collective, as individuals and businesses not directly associated with OpenAI get their hands on the new model and determine whether or not it makes their life significantly easier. Try Astra out if you can, learn about the experience of others with the tech, and decide if you think it’ll be worth paying to be able to access it.

Don’t get caught up in the hype, and don’t worry about whether it's AGI or not. In time it’ll be very clear if it is — and we won’t need a social media post to convince us.

• More mods are out for DLSS 5
• They allow for the tech to be easily used with many more PC games
• One of these mods also alters the way in which DLSS 5 is applied to improve performance considerably — at a cost

As you've no doubt seen, modders have been having a field day messing about with Nvidia's DLSS 5, and the latest experiments include a trick to boost performance in a big way, and a method of easily running the tech in a lot more games.

Let's start with the latter, which as TweakTown reports is the DLSS 5 Swapper app which appeared on GitHub courtesy of developer Rakan Alkhaldi.

This takes the pain out of having to manually fudge DLSS 5 to work in unsupported games by automating the process, adding all the required files in the blink of an eye. You just drag and drop the game folder into the app, and it takes care of everything from there.

Swapper can even detect games on the PC it's installed on which can have DLSS 5 modded in. Not every game is supported, of course, but plenty are — and any given title doesn't necessarily have to have DLSS support at all, in any form (this is achieved via another community mod called DLSS 5 Feeder which is included with Swapper).

A similar tool, DLSS 5 Autopilot, published by 'Kizzuwatnaa' and also on Github, offers one-click DLSS 5 setup (via Feeder) along with an additional trick in the form of the 'neural upstream' add-on (from 'matiasLombo'). This runs the neural rendering (AI) process before the DLSS upscaling happens.

Normally, the AI polishing that DLSS 5 applies is performed as the last step of the rendering process to the upscaled final frame, but if you choose this option with Autopilot, it's happening earlier in the chain of events.

Based on experiments detailed on Reddit (posted by ToxicFatee), this makes a major difference to performance. The Redditor has an RTX 4080, and ran Assassin's Creed Shadows at 4K which went from 63 frames per second (fps) to 31fps with DLSS 5, but it was around 50fps with the neural upstream take on DLSS 5. That's a 20% loss, and a far more palatable hit on the frame rate rather than it being cut in half.

Others have aired similar results on Reddit, and there's plenty of excitement about what's being achieved with this mod (and the automated implementation of DLSS 5 across all manner of games). As one Redditor put it: "Absolutely amazing time to be a PC gamer."

Analysis: tread carefully with unofficial mods

All these latest developments with modding are great to see, but for me, it ends at just seeing this progress. By which I mean I wouldn't advise jumping in and installing these kinds of mods – as wonderful as they seem – and I'm content to merely observe, and see the frame rates and visual results, that others are getting. And we aren't short of these observations of late.

Why do I take that view? Well, for starters, I'm not keen on installing third-party software from unknown sources as a rule, because of the concerns around security that this involves. Granted, these are open-source tools (so can be checked over), and I'm sure they're on the proverbial level; but nevertheless, I'd still rather be careful around the risks of viruses and the like.

The second note of caution here pertains to the idea of messing with the rendering pipeline in terms of having DLSS 5 applied earlier in the process (with the Autopilot mod). This will likely involve image degradation or artifacts to some extent — or generally messing with the graphics more than Team Green intended — and I'm sure if this really was a genuinely better way of doing things, Nvidia would've used this route itself. As per observations on Reddit, there does appear to be a quality trade-off for the improved performance here.

All that said, perhaps there will be some kind of official option along these 'performance or quality' lines in the future, as some gamers are already hoping. However, DLSS 5 is, for now, officially designed to work with just one supported title, NBA 2K27, and it does an excellent job with that basketball game.

• Nvidia pays $11.9 billion for Hugging Face, and a further $1 billion in equity-based retention
• The company promises to keep Hugging Face "an open platform for the entire AI ecosystem"
• Nvidia CEO Jensen Huang has publicly supported open weight models

Nvidia has confirmed plans to acquire Hugging Face for $12.9 billion subject to regulatory approval, marking a major expansion for the chipmaker as it snaps up one of the most important AI model distribution platforms.

In a post, the company asserted it plans to keep Hugging Face as it is: "Hugging Face will remain an open platform for the entire AI ecosystem," CEO Jensen Huang wrote.

Huang explained Nvidia's "infrastructure, engineering and global reach" will help improve Hugging Face's "reliability, safety, model evaluation, inference and deployment capabilities."

Nvidia confirms Hugging Face acquisition

The platform currently counts more than 18 million developers, researchers and creators as users, with more than three million models shared on it.

"Hugging Face will continue to support open source and open weight models from across the ecosystem, from every model builder," Huang stressed again, noting his public backing for open weight models. Ordinarily in an acquisition, questions would be raised over whether, in this instance, Nvidia's own GPUs and software could receive preferential treatment, but the company is doing everything it can to assure us that won't be the case. A pending regulatory investigation will also ascertain this.

Nvidia also claims to be the biggest contributor to Hugging Face, having shared more than 500+ models and around half as many open datasets.

The news comes less than two weeks after reports started to circulate about a potential acquisition, and if it closes, it'll be Nvidia's biggest acquisition to date. The $12.9 billion payout comprises a Hugging Face valuation of $11.9 billion (way above its earlier $4.5 billion valuation in 2023) and an equity-based retention program of around $1 billion.

As for upcoming regulatory interjection, Nvidia warned in a SEC filing that "Government restrictions may negatively impact our business and the Hugging Face platform."

Following the leak of the tech and a raft of unofficial mods, this week saw Nvidia's official launch of DLSS 5, with the company showing it off in NBA 2K27 at Gamescom 2026 and providing some further illuminating details.

Before, after Nvidia's initial DLSS 5 reveal in March, I was skeptical on some points, but I wasn't tearing holes in the idea like some gamers were. However, this week, we've seen a rapid shift in opinions and the general online sentiment around DLSS 5 — and I'm a good deal more excited about this AI trickery myself.

Indeed, I think there are a number of reasons to be hopeful that DLSS 5 could prove to be a compelling step forward for PC gaming, from Nvidia's clarification on support for GPUs, through to what modders have shown that the tech can achieve since it was leaked — some of which is truly eye-opening.

Here are my five reasons why Nvidia's incoming neural rendering model could turn out to be a very literal game-changer.

1. Nvidia has clarified that DLSS 5 won't just be for RTX 5000 GPUs

While DLSS 5 is currently only supported by RTX 5000 graphics cards — the tech went live this week in NBA 2K27 — Nvidia has confirmed that it will come to older GeForce GPUs. Indeed, we won't have to wait that long before wider support is ushered in, which is good news.

VideoCardz spotted an update from an Nvidia rep on Reddit which stated: "Our current focus is on optimizing performance for the GeForce RTX 50 Series with model updates expected later this fall. Once RTX 50 Series performance is more fully tuned, we plan to work on expanding official support to the GeForce RTX 40 Series."

So, we could be looking at early 2027 for work to begin on bringing DLSS 5 to RTX 4000 models — and possibly previous generations of GPUs will be in line for the same treatment. Modders have already got the tech working on RTX 3000 and 2000 models, after all — although the performance levels could well be the sticking point here.

At any rate, this puts paid to fears that DLSS 5 could remain locked to just the latest Nvidia RTX 5000 graphics cards.

2. Continual optimization should improve performance a good deal

I just touched on performance, and this is the area which has proved to be the biggest downer with the mentioned unofficial experiments that have been conducted with DLSS 5.

As Tom's Hardware observes, the performance hit with DLSS 5 is a nasty one, and it's generally causing a 40% to 50% drop in frame rates. In some reports, it's less than this (more like 30%), and what we have to remember is that all these unofficial figures are based on a pre-release version of DLSS 5 being fudged into play by modders.

The final version of DLSS 5 will likely provide better performance, and in any case, optimization is going to be high on Nvidia's list of priorities. I'd be surprised if there weren't major strides taken in this department as 2027 rolls around and more games are supported.

3. Experimental results with DLSS 5 are pretty incredible

Those modders playing with the leaked version of DLSS 5 have clearly shown us that this tech can be used to produce seriously impressive results. Of course, on the official front, the demo of NBA 2K27 at Gamescom 2026 proved the case that this AI-powered graphics uplift can work wonders, too.

That said, the benefits of DLSS 5 are situational to an extent, and it's not a silver bullet for making all graphics better. It really shines in certain use cases, though. For example, even if you don't want the main character models in a game to have the tech applied because you hate that idea, it can still make a huge difference to background details, and the likes of the faces of NPCs in the lurking atmospheric crowds which are the backdrop for open world games.

As one Redditor points out: "NPC crowd faces are one of the best use cases for it [DLSS 5]. It sometimes takes you out of the game when you're talking to a character model that's clearly been hand crafted with a lot of effort, and as you said then there's some janky smooth skinned ghoul walking by you lol. Watching an LTT [Linus Tech Tips] vid where they showed the difference in random NPCs was actually super impressive."

DLSS 5 can also transform lighting and shadows to make graphics look far more realistic, including character models — some of the results shared by modders in a megathread on Reddit make that crystal clear. I was blown away by some of these examples, frankly. When it works, DLSS 5 works really well — although it doesn't always come off.

That said, some of the wonkier examples that have been aired are down to modders being daft with the sliders and options on offer, which brings me onto my next point.

4. Tuning options are key for DLSS 5

As noted, some of the more stomach-churning moments from the various modders messing around with DLSS 5 have come from those folks ramping up the intensity of the sliders — sometimes intentionally just to provoke an anti-AI reaction.

There's a decent level of granular control over various aspects of what DLSS 5 does (including intensity, tone, and structure), and when a game developer properly tunes the tech for their particular release, the effect is going to be more subtle, appropriate, and in line with the overall art direction. (Well, hopefully — you'd assume this will be the case, anyway).

Used judiciously, it's clear enough that DLSS 5 can really enhance the visuals of a game — modders have proved that in some cases, and NBA 2K27 is a great official example of how this works, too.

Yes, it is very possible to get 'uncanny valley' kind of effects with character models if you're extravagantly flinging sliders up towards their maximum limits — and not every game's art or graphics style suits DLSS 5, by any means. But we come back to the last point — when it works, it really does work.

5. DLSS 5 will probably age like a fine wine (ahem)

My final point is a simple — but important — one. And okay, I've said this before, but I think it bears repeating. Rewind time back to when Nvidia revealed the original DLSS, the very first take on this tech that was purely about upscaling, and it had some quite pronounced flaws. Problems like blurriness, ghosting and artifacts proved a blight on the upscaled graphics, but these were issues that Nvidia resolved with the next iteration of the tech.

I'd expect there will be examples of DLSS 5 which show the visuals going off the rails in a similar kind of way, although it already looks off to a far better start than DLSS 1. The point being that whatever we think now, at launch, may change quite drastically in a year or two, and there's a genuine potential for DLSS 5 to be revolutionary in polishing up gaming graphics — albeit that caveats around its misuse and situational nature aren't going to go away.

Gaming on the go has reached fresh heights over the years, with slim laptops and compact handhelds delivering great performance. AMD and Intel have spearheaded the charge with their mobile processors, but a new player is in town.

Nvidia revealed its RTX Spark at Computex in May, an ARM-based superchip designed for Windows on ARM laptops, with AI capabilities and obvious strong suits for creators – but also gamers, too, with a powerful integrated RTX GPU. It's designed for slim laptops and compact PCs, keeping workspaces less cluttered and making creation alongside gaming much easier when travelling.

I was lucky enough to go hands-on with an RTX Spark laptop at Gamescom 2026, specifically the HP OmniBook Ultra 16, which was running Borderlands 4. Gearbox Interactive's latest entry in the franchise was notorious for its rough performance at launch.

It has significantly improved since then, and the OmniBook Ultra 16 was running the game at an incredible frame rate, one I could tell was above 60 frames per second, with the help of Nvidia's Frame Generation x2.

The mention of Frame Generation may throw some off; after all, it's been frowned upon for a long time with the 'fake frames' nickname (a criticism I disagree with). However, the input delay and overall latency weren't an issue for me on this RTX Spark system.

I didn't get the chance to put the system through its paces other than playing that particular game, but it's worth noting that the other RTX Spark laptops at Nvidia's booth were running games like 007 First Light and Arc Raiders, both of which ran at smooth, solid frame rates.

If it wasn't clear already, I think RTX Spark is great for laptops and compact PCs, and my only concern is the price. I'm hoping for something that isn't too expensive, but that's probably wishful thinking due to the nature of these devices and the ongoing RAM crisis. However, it would be a shame if RTX Spark laptops don't blossom as a result of memory shortages.

Handheld manufacturers could use RTX Spark

Speaking with Nvidia's staff at Gamescom, I asked if the RTX Spark was intended to be used with handheld gaming PCs, and it was made clear that Team Green is completely open to any handheld manufacturer reaching out to use the superchip.

Handheld territory isn't exactly foreign to Nvidia either. The Nintendo Switch 2 uses the Tegra T239 processor, with access to DLSS across multiple games — and the upscaling technology does wonders for image quality and performance.

While AMD and Intel's processors continue to provide fantastic performance on most handheld gaming PCs, especially with Intel's new G3 Extreme CPU, I told Nvidia that DLSS on something like a Legion Go or MSI Claw would be untouchable.

Of course, performance has to be balanced with battery life and power usage, as well as pricing, and I'm very aware that this could be a tricky equation to deal with.

If the RTX Spark (or any similar chip in Nvidia's plans going forward) could be the engine of a handheld gaming PC, using more aggressive upscaling wouldn't be something to avoid in terms of any easy win to boost performance. DLSS Super Resolution is great at maintaining sharp image quality, and it would work even better on the smaller screen of a handheld.

Will this ever happen, though? That's down to the manufacturers of these devices.

• Nvidia's NVHBM moves the memory controller off the accelerator die and into the HBM stack's base die, allowing it to claim a potential 30% more bandwidth, 15% lower HBM power, and 25% more compute area versus standard HBM4E
• Nvidia's NVHBM performance comparisons are linked to HBM4E, which is still in the sampling stage, with a launch expected sometime in 2027
• Access to the custom solution is gated behind NVLink Fusion; Amazon's Annapurna Labs is the only named partner so far and has not said which GPU uses that memory

On August 26, Nvidia extended its NVLink Fusion program with NVHBM, a custom high-bandwidth memory architecture that the company says delivers up to 30% more memory bandwidth per stack, 15% lower HBM power consumption, and up to 25% more usable area on the accelerator die.

The performance was measured against standard HBM4E samples even as the memory is expected to ship in volume some time in 2027.

Amazon's Annapurna Labs is the first named partner for Nvidia's memory advance which aims to address growing performance limitations for frontier-level AI models centering around limited bandwidth.

What makes NVHBM tick?

Nvidia's approach doesn't reinvent how memory is handled; it reimagines where it is managed. Conventional HBM approaches split this between two companies: the memory vendor supplies the DRAM stack and its base die, and the accelerator designer puts the memory controller and physical interface on its own compute die.

JEDEC standardizes the connection between the two at the cost of a very wide, comparatively slow parallel bus. NVHBM dissolves the seam altogether by moving Nvidia's memory controller into the base die of the 3D stack, replacing the standard bus with a narrower, serialized die-to-die link that Nvidia designs and memory makers can build.

Nvidia's technical blog puts the resulting reduction in interface and support area at up to 67% against the JEDEC HBM4E standard, even as it improves key power consumption, memory bandwidth, and usable area.

The underlying tech is not completely new, however: Marvell announced the same basic idea in December 2024, naming Micron, Samsung, and SK hynix as collaborators while claiming up to 25% more compute area, 33% greater memory capacity, and a 70% reduction in memory interface power, figures that come close to what Nvidia is currently projecting. Counterpoint Research analyst Neil Shah put it plainly: "The technology is not new. The distribution is."

Even as HBM4E remains elusive, as it is not in mass production just yet, with Samsung shipping the first HBM4E samples in late May 2026 and SK hynix pulling its own sampling forward to around June, NVHBM is even more elusive. It is a building block gated behind NVLink Fusion, Nvidia's program for connecting third-party accelerators to its rack-scale platform, and access runs through that program's partner list.

Amazon's Annapurna Labs is the first named participant, with Nvidia's blog saying Annapurna will support NVLink Fusion with Trainium4 without explicitly mentioning NVHBM.

For now, NVHBM might be the future, but it may arrive well after HBM4E is already being deployed en masse. Nvidia's own technical blog describes NVHBM as built on the same technology the company will use for future GPUs, without specifying the first generation that would support it natively.

At IFA 2026, the huge tech show in Berlin, Germany, Nvidia announced that laptops running on its ARM-based RTX Spark chip will be available to buy from October.

The biggest names in the laptop industry, including Microsoft, Lenovo, HP, and Dell, will all have RTX Spark devices, and I got to spend a bit more time with some of them at Nvidia’s launch event.

Having already seen a lot of RTX Spark laptops at Computex 2026 earlier this year, quite a bit of what Nvidia showed at IFA was familiar. There was, again, a big emphasis on local AI performance, which is undoubtedly impressive, but a bit niche for consumers, and I can see how Nvidia tech such as DLSS upscaling and Multi Frame Gen (MFG), which RTX Spark devices can make use of, can help content creators working on complex 3D art speed up their workflows (those technologies have in the past mainly been aimed at improving the performance of PC games).

I also got to play a range of games on RTX Spark laptops, including DOOM The Dark Ages, and 007 First Light, and it remains impressive that these thin and light laptops are able to run such demanding titles with such impressive results. This is especially true when you consider that the games aren't running natively on ARM, instead they are being run via Microsoft's Prism emulation layer, which means games developed for traditional Intel or AMD (x86) hardware can run without devs having to make dedicated ARM versions. According to the Nvidia spokesperson I chatted to, the performance hit of running a game via emulation is negligible, at least with GPU-bound titles.

The spokesperson admitted, however, that CPU-intensive games (such as RTS games) could see bigger dips compared to running the game natively.

What is new at IFA is the announcement that RTX Spark laptops will go on sale in October. But, apart from that, details remain scarce - and that means it's hard to get excited about these laptops just yet.

Keeping their cards close to their chest

Despite being about a month out from going on sale, Nvidia was tightlipped about a lot of things regarding RTX Spark laptops. The biggest question mark, of course, concerns pricing.

These are powerful laptops aimed at creative professionals, and will come with up to 128GB of unified memory, so I can't imagine they are going to be anything other than very, very, expensive. The continued silence about prices doesn't bode well for anyone hoping for vaguely affordable laptops.

One spokesperson I talked to did suggest there will be multiple yet to be announced configurations that will appeal to different consumers and price points, though they looked slightly doubtful when I asked if there would be an RTX Spark laptop that students could afford.

We still don't know more about performance, either, as while there were live demos, raw performance numbers were not available, and I was not allowed to run benchmarks or change settings.

What I can say is that complex AI tools and generative AI features, all run locally on the laptops, and appear fast and smooth. Games also ran well. I was also assured by Nvidia that performance would be roughly the same if the RTX Spark laptop is plugged into a charger, or running on battery.

So, no power-conserving throttling it seems, something that Apple has long boasted about with its M-series chips.

A number of laptops from Nvidia's partners were on display, but were turned off, and journalists were asked not to even pick them up, again an odd request considering these devices are supposed to go on sale next month.

I'm certainly impressed with what I've seen, and the potential of RTX Spark is huge. I'm also glad that Windows laptops look like they could finally give Apple's modern MacBook Pros a run for their money.

But, on the topic of money, I really won't be able to ascertain just how excited we should all be with RTX Spark laptops until we know more about the price - and with an October launch, time is running out.

• Lenovo announced the 11th-generation 15" Yoga Pro 9n and 16" Yoga 9n 2-in-1 at IFA 2026, both running NVIDIA's RTX Spark with a 20-core Grace Arm CPU and a 6,144-core Blackwell GPU
• The understated highlight for business users and professionals is the 128GB of unified memory that the Pro 9n offers, essentially allowing for large LLMs to be run on-device
• This is essential for companies that are required not to transmit sensitive data or use cloud APIs for their AI requirements

Lenovo announced two Yoga laptops running NVIDIA RTX Spark at its Innovation World event during IFA 2026, and the spec that may matter most to professional buyers isn't the GPU but the massive maximum memory both offer.

While the Lenovo Yoga 9n is limited to a 16-channel 256-bit 64GB LPDDR5X configuration, the Pro SKU doubles this to 128 GB.

The change allows the Yoga Pro 9n to run large models on-device, an impressive feat for a device that weighs only 1.65kg but houses a capable Blackwell RTX GPU that can muster as many as 6144 cores for local AI needs.

Peaking under the hood of the RTX Spark-powered Yoga laptops

The underlying silicon is NVIDIA's RTX Spark, unveiled on May 31 ahead of GTC Taipei and long rumored under the codename N1X. Both laptops pair a 20-core Grace-class Arm CPU with a Blackwell RTX GPU with 6,144 CUDA cores, roughly comparable to a laptop with a GeForce RTX 5070, co-developed with MediaTek and joined by Nvidia's proprietary coherent chip-to-chip interconnect (NVLink-C2C).

The Lenovo Yoga Pro 9n is the performance-centric model, offering 2 NVMe SSD slots and a total advertised storage capacity of 4 TB, while the non-Pro model caps at 2 TB. It also offers an extremely color-accurate 15.3-inch 2.5K PureSight Pro OLED that runs from 48 to 165Hz at 1100 nits peak brightness, plus a large 92.5 Whr battery with rapid charging that lets it eke out an advertised 3 hours of battery life after 15 minutes of charging. It also offers 2 USB-A and 2 USB-C (USB4) ports; the latter support DisplayPort 2.1 and USB PD 3.0 for power users, and it also includes an HDMI 2.1 connection and an SD port.

With WiFi 7 and BT 5.4 under the hood, plus a 9.2MP IR camera and a quad-speaker setup, the Pro 9n is built for creators who need plenty of connectivity in a slim, portable chassis.

For those who need a slightly larger screen and potentially a 2-in-device, the Yoga 9n skips the 'Pro' moniker but offers a 2.8K OLED display with a slightly lower capped refresh rate (30 to 120Hz), half the maximum memory configuration, and a slightly larger battery (99.9 WHr), even as it sees its weight increase to 1.94KG as a result.

A portable for-local AI offering that holds its own

For business buyers looking to localize their AI needs, run large proprietary AI models, or keep data on-device, the unified memory pool both Lenovo offerings bring to the table is essentially their biggest selling point.

This is in sharp relief from how the first wave of Copilot+ PCs stressed AI performance as a unit of NPU TOPS, even as the emergent bottleneck was that of memory capacity and bandwidth, where both laptops excel.

A machine that holds a large model in memory can essentially run inference on material that never leaves the device, even as both laptops offer a decent mid-range GPU that can generate more tokens than most comparable laptops for similar models.

This is crucial for firms that cannot send client documents, patient records, case files, source code, or export-controlled technical data to a third-party cloud API; this is not just a performance feature but a compliance one, and it marks the difference between using a model on real work with information that is often sensitive versus on test data.

The laptops also bring relief for developers who spent the last few years trying to use unified memory-based Apple laptops to work with Nvidia's CUDA stack with varying amounts of success, to build locally on a computer that essentially eliminates that compatibility and testing gap by allowing it to run the CUDA stack natively on the RTX Spark-based machines.

One could argue that many applications still have compatibility issues on ARM-based CPUs, but that gap is narrowing as Microsoft continues to publish updates and emulation improves, and as more developers publish ARM-centric compilations of their software.

Despite their AI-centric focus and catering to high-end professional users, Yoga generally isn't where Lenovo's business customers are found; its business fleets run on ThinkPad, with ThinkShield security tooling, business-grade manageability, extended parts availability, and the lifecycle commitments that enable predictable refresh cycles.

Lenovo has not announced pricing for the Yoga Pro 9n or the Yoga 9n currently, stating that it will do so at launch, even as the accompanying Snapdragon 8 Elite-powered Lenovo Yoga Tab Plus Gen 2 and MediaTek Dimensity 9500s-powered Lenovo Yoga Tab Gen 2 both reach shelves this September with price tags of $999.99 and $729.99, respectively.

At IFA 2026, Acer announced its upcoming Small Form Factor (SFF) PC, built on Nvidia’s new RTX Spark chip, and from what Acer (and Nvidia) has shown, I should be buzzing with excitement.

After all, it ticks a lot of boxes for me; it’s compact, beautifully designed, and thanks to the RTX Spark, it should also be very, very powerful. And yet, much like the recent announcement of the latest Mac mini with Apple’s new M6 chip, I just don’t feel excited at all.

Despite sharing the same ‘RTX’ branding as Nvidia’s gaming GPUs, it’s clear that early devices using the RTX Spark will be of little interest to consumers like you and me, and are instead aimed at people who need to run heavy-duty local AI tasks.

Even though Acer states that RTX Spark devices are “designed for creators, AI developers and gamers,” the brief information provided focuses solely on the AI capabilities, with Acer saying that its RTX Spark device “is built to deliver up to 1 petaflop of AI performance and up to 128 GB of high-speed unified memory, delivering a combination of compute and data privacy.”

Terms like “1 petaflop of AI performance” are a sure-fire way to make any interest I might have in a device evaporate faster than PC gamer’s tears on a red-hot AI data center. It certainly doesn’t make me feel like the product is aimed at me, despite the RTX branding, brief mention of gamers, and stylish design.

I’ve written a lot in the past about my love of small form factor PCs, mainly when it comes to gaming, as these compact computers can be great alternatives to games consoles, as they can sit in your lounge and plug into your TV without looking out of place. RTX Spark devices, such as Acer’s, could potentially combine Nvidia’s gaming technology with compact designs, something I’ve always wanted. So why am I just not excited?

AI above everything else

There's a simple reason: AI. Now, this isn’t about my general distaste of generative AI, or the fact that things like Google’s AI Overviews are taking written content from websites, such as this one, and featuring them elsewhere without compensation — it’s because from what I’ve seen of RTX Spark hardware so far seems to be lazer-focused on local AI capabilities, which is something that simply doesn’t appeal to mainstream users.

If these were products solely aimed at AI developers, I wouldn’t mind, but it feels like RTX Spark has been teased by Nvidia to be something that consumers, especially gamers, would also be interested in. Hopefully as we get closer to the release of the first batch of RTX Spark devices, we’ll be given some non-AI reasons to be excited.

At the moment, though, I feel the same way about RTX Spark PCs as I do with Apple’s new-announced M6 Mac mini.

Mini heartbreak

Recent Mac minis have been some of my absolute favorite products, and up until a few weeks ago, if anyone was looking for an affordable desktop PC, I would almost always just tell them to buy a Mac mini. They were powerful, well-built, had an impressively small form factor which allowed you to install them almost anywhere, and launching at $599 / £599 / AU$999, you’d be hard pushed to find a better value small form factor PC.

In fact, I awarded a very rare five stars in my Mac mini (M4, 2024) review. So, I should be over the moon with the new model, especially as it’ll be the first device to pack Apple’s M6 chip — quite an achievement for the little PC that could.

And yet, I feel a bit deflated. The latest Mac mini will come with a higher price tag, something that feels all to familiar at the moment, and now starts at $899 / £899 / AU$1,449, a considerable jump up from what the previous model launched at, and instantly makes the Mac mini less of an easy recommendation.

One of the reasons why it’s been hard to get hold of a Mac mini recently is because a lot of people are bulk buying the tiny PCs to run OpenClaw and other local AI tools on them. Apple appears to be leaning into this popularity (I can’t blame them for that, to be honest), with a big focus of the Mac mini, and the M6 chip, being improvements to AI performance (Apple claims four times the AI performance of the previous model).

That’s all well and good, I guess, but that AI focus, along with the price increase, means one of my favorite bits of hardware doesn’t feel like it’s aimed at me any more. I’ll be less likely to recommend it to people as even if it turns out that it’s a great bit of gear, the new price means it won’t be for everyone. And I personally don’t know anyone who is looking for a Mac mini to do heavy AI tasks with it.

It's the same with RTX Spark devices. We’ve not had price announcements, but Acer’s RTX Spark PC not only features cutting-edge Nvidia hardware, but “up to 128 GB of high-speed unified memory.” There’s little doubt that it’ll be very expensive.

So, it leaves me wondering who the RTX Spark PCs and new Mac mini are really for. The Mac mini had a real chance to become a mainstream hit, but its higher price will put a stop to that (the cheaper MacBook Neo now has a better chance), and I can’t imagine anyone except developers who need to run local AI models being interested in RTX Spark, despite the stylish designs.

Bascially, I’d like the likes of Nvidia and Apple to remember us consumers who don’t have a huge amount of cash, have little interest in AI, and are feeling increasingly left behind.

• Chris Malone, OpenAI's head of data centers, has left after seventeen months, with the company confirming the exit but giving no reason
• His responsibilities had already been split before he went, with his reporting line moved to VP Sachin Katti and the job now spread across three executives plus an unnamed CTO for computing capacity
• The move comes as OpenAI is reportedly reviving plans to lease entire facilities rather than build them, a shift from construction to procurement

Chris Malone, the executive who ran OpenAI's data center buildout, has left the company. The Wall Street Journal broke the story last week, and an OpenAI spokesperson confirmed the departure to Bloomberg the same day.

Neither OpenAI nor Malone has given a reason for his departure or his next destination, and a message from Bloomberg to his LinkedIn account received no reply.

The top-level executive joined the company in March 2025, against the backdrop of OpenAI's Stargate announcement in January of the same year, a commitment to invest $500 billion in 10 gigawatts of US AI infrastructure over four years alongside SoftBank, Oracle and MGX.

A job cannibalized by changing priorities at the helm?

With a total tenure of 17 months, a relatively short duration for the job, Malone arrived at a time when the job was incredibly demanding, as OpenAI fended off competition from rival Anthropic, a resurgent Google with its Gemini offerings, and what can perhaps be described as a love-hate relationship with Microsoft.

His exit may have been written on the wall weeks ago, according to the Wall Street Journal, which noted that Malone stopped reporting to OpenAI president Greg Brockman and began reporting to vice president Sachin Katti, who took over leadership of the group.

OpenAI has since appointed a chief technology officer to lead computing capacity, though it has not publicly named that person. Responsibility for what Malone did is now spread across at least three people: Uday Ruddarraju leads the data center team, Brent Mayo runs the build-and-delivery program, and Spas Lazarov, who came from the data center and energy industries, leads data center engineering.

All of this indicates it wasn't a sudden vacancy to fill, but one that had been on the cards for some time. This comes as reports indicate OpenAI is reviving data center initiatives involving leasing entire facilities, with others leading that effort. This would mark a significant shift from its initial plans to get involved in the building side of the business, which it seems to have focused less on lately, and a major strategic shift in how the organization plans to operate in the future.

OpenAI's own narrative points in the same direction, without conceding much, when it told TechCrunch it had "recently reorganized" its "infrastructure organization to support the scale and pace of our work," adding that it retains a strong and deeply experienced data center team with clear leadership. It, however, has not addressed Malone's exit directly.

The move is not isolated, as OpenAI continues to lose high-level executives; Business Insider tallied thirteen senior departures at OpenAI in 2026 as of August 21, and Malone is the fourteenth to leave the company to date. The Next Web counts seven senior departures or replacements since April alone.

Malone is also among the least senior names on those lists by title. The Head of data centers is not a named C-suite role, and after the reorganization, he reported to a vice president rather than sitting alongside a chief operating officer or a chief revenue officer for OpenAI.

Axios reported earlier this month that OpenAI's recent changes are "driven by strategy more than the revolving door" that characterizes the wider AI talent market, with OpenAI president Brockman deliberately building a leadership team to compete in the enterprise space. Brockman has denied this, telling the Journal that the spotlight on his company means "every departure gets scrutinized in a way that it doesn't otherwise."

The scrutiny comes with the ambitions OpenAI currently harbors, many of which seriously affect its direct partners, including Nvidia, AMD, and Microsoft. CNBC reports OpenAI is targeting roughly $600 billion in compute spending by 2030. It is planning a $30 billion data center in Georgia. Nvidia agreed on August 17 to provide up to $105 billion in financing for an OpenAI-leased campus in Ohio, after earlier reports said it was weighing a considerably larger guarantee.

The move comes at a time when the company is getting ready for an IPO that would inject fresh cash while also allowing employees to essentially cash in on their equity of what is one of the world's most valuable startups: OpenAI filed confidentially with the SEC in June, and chief financial officer Sarah Friar told staff last week the company will go public in 2027 or sooner, having originally targeted this year.

An exodus of key employees, including Malone, might just be a strategic change in how the company operates or otherwise, but given the sheer scale of the company involved and the potential consequences of such moves, the scrutiny is well-warranted in an industry that has otherwise progressed relatively unchecked in the name of progress over the past few years.

• Nvidia's latest graphics driver has a frustrating bug
• Version 616.56 is causing distracting screen flickers and color corruption
• A patch is coming, and there are potential workarounds — but gamers are getting fed up with the prevalence of bugs in GeForce drivers

Nvidia's latest graphics driver is causing chaos for some GeForce GPU owners in the form of a glitch that makes the screen flicker.

Wccftech spotted the bug with Nvidia's Game Ready Driver version 616.56, and as flagged by Renan Maniero on X, Team Green has said it's testing a fix for the issue (as revealed on the GeForce forums).

The screen is reportedly flickering white or black and exhibiting other brief flashes of color corruption, and this is happening in certain games (The Last of Us and Minecraft are mentioned, among others) or on the Windows desktop. It's also occurring in web browser sessions and while playing YouTube videos, based on Reddit reports — of which there are plenty.

There's no shortage of gamers losing their temper with Nvidia, too, and blasting Team Green for the quality of its graphics drivers. Many are venting with an observation that GeForce drivers just haven't been the same since the launch of the RTX 5000 series.

For example, this Redditor says: "It's been particularly bad since Blackwell [RTX 5000] release. Just on Blackwell alone, between the whole DisplayPort 2.1 not working with HDR, screen flickering, and black screens on the initial release, it's been a complete mess."

Another gamer complains: "It is pretty shocking seeing this from a trillionaire company on a stable build. Nothing about this driver is stable."

Yet another Redditor grumbles: "It's crazy how things are now. You used to be able to update each time or be one driver behind at worst. Now I'm half a year outdated and thinking I'll just leave it."

Analysis: more drivers, more problems? There are possible workarounds though

This seems like quite a widespread issue and one that affects modern Nvidia graphics cards all the way back to RTX 2000 models.

If you're getting frustrated by this bug — random screen flickering gets pretty old, pretty fast, if it's happening regularly — Maniero also shared an apparent potential workaround on X that you can try while waiting for the official fix.

Seemingly, this is a bug that mostly affects monitors in 8-bit mode, and if you switch to 10-bit color (assuming your display supports this), the problem may disappear. Maniero also suggests that you try turning on Windows Auto Color Management (ACM).

Other folks on Reddit have shared various possible remedies, including switching from DisplayPort to an HDMI connector instead, and disabling Multi-Plane Overlay (MPO) in Windows 11, as one Redditor suggested. Nvidia provides a Registry file for turning off MPO (and enabling it again), but as ever with anything in this area of Windows, proceed at your own risk. (Although this method should be much safer than trying to edit the Registry directly yourself).

Your other choice is simply to roll back driver 616.56 to an earlier version, and there's no shortage of gamers who've done exactly that and report that the problem vanishes.

Hopefully Nvidia's official flicker-begone patch will be coming soon enough in the form of a hotfix, because this is an unduly annoying gremlin in the GeForce works that needs to be sorted out promptly.

• Nvidia unveiled DLSS 5 at Gamescom 2026, showing it off in NBA 2K27
• DLSS 5 will launch alongside NBA 2K27, and be available from September 3, 2026
• DLSS 5 is exclusive to RTX 5000 GPUs, but Nvidia is working to eventually make it available on older GeForce cards

It's no secret that Nvidia's reveal of DLSS 5 earlier this year led to instant controversy and a backlash from gamers, not to mention game developers. The technology was branded as generative 'AI slop', effectively changing the look and style of character models, notably Leon S. Kennedy and Grace Ashcroft in Resident Evil Requiem.

Since then, Nvidia has worked on developing its neural rendering tech, and DLSS 5 is now ready for launch — and it's arguably being used in the game that's most appropriate for it.

DLSS 5 will debut on September 3, 2026, and will be first available in NBA 2K27 on September 4. The new tech will be exclusive to RTX 5000 GPUs, but Nvidia made it clear that it will explore ways to bring DLSS 5 to older generations of GeForce graphics cards eventually.

At Nvidia's Gamescom 2026 booth, I was treated to a brief hands-on sample of DLSS 5 running in NBA 2K27, on multiple systems including an RTX 5060 gaming laptop and also on GeForce Now.

Much to my surprise, DLSS 5 looks absolutely amazing with NBA 2K27's environments and character models, providing a level of realism that can only be described as the next leap in visuals for gaming.

The level of detail in character faces in terms of lighting and shadows is truly next-level, and it's all possible thanks to AI-enhanced lighting on the rendered frame, which Nvidia states is trained to 'preserve artistic intent'.

When DLSS 5 was first revealed in March, I was bewildered by what Nvidia was attempting, which appeared to be generative AI used to make everything in-game more lifelike. If it wasn't clear already, I have strong feelings against generative AI in gaming, but my initial fears were misplaced, as this isn't what DLSS 5 is about.

DLSS 5 uses three different models, with each including options for structure intensity, tone intensity, and masking. Structure and tone are both responsible for aspects such as ambient occlusion, contact shadows, reflections, and broader lighting.

Masking allows manual or automatic control of where each effect is applied, and Nvidia has designed DLSS 5 for developers to have full control over the final frame that's rendered.

The DLSS 5 model running in NBA 2K27 was fine-tuned directly by the game's developer, not Nvidia, and the result is way different from the first glimpse of DLSS 5 in March. What was illuminating here is that the team at Nvidia stressed that the initial reveal had the sliders for each of those models at maximum levels.

As I saw with NBA 2K27, when put in the hands of developers, it's easier to see just how great DLSS 5 is when used appropriately. Developers will be able to adjust the intensity for each scene for both the environment (foliage and other objects) as well as character models.

Performance is a different ball game too

DLSS 5 isn't exactly easy to run, which isn't a huge surprise considering how realistic the results are. However, Nvidia has made significant progress in six months: DLSS 5 required two RTX 5090 GPUs to function at the initial reveal, and now works with any RTX 5000 series GPU.

The RTX 5060 gaming laptop running NBA 2K27 displayed very smooth performance, only using Frame Generation x2 with DLSS 5 enabled. This was a big surprise to say the least, and suggests that it may not take as long as expected to introduce this new tech to RTX 4000 series GPUs (and older).

My only concern at this stage isn't related to Nvidia at all, in fact. DLSS 5 on its own now looks very useful for producing lifelike visuals, and seems set to be the future for impressively detailed graphics.

Visual Concepts, the developer behind NBA 2K27, has done enough here to quell my fears that DLSS 5 would somehow mess with game art and design direction in a negative way.

However, we're yet to see how other developers will implement the tool in their games. I'm somewhat worried that many could fall into the trap of using DLSS 5 as a shortcut for great game visuals, rather than an added bonus on top of already well-crafted graphics.

I shared similar concerns regarding Nvidia's Multi Frame Generation, and how developers may rely on this very useful tool as a band-aid for poor game optimization.

Fortunately, though, if NBA 2K27 is anything to go by in terms of what to expect from the likes of Capcom, Bethesda, and Ubisoft, then I'm excited to see DLSS 5 implemented in upcoming games. After all, it is an optional game setting, so if you don't like it, you don't have to use the tech.

Skynet has missed another appointment. Depending on which Terminator timeline you prefer, the murderous artificial intelligence should have become self-aware in 1997, 2004, 2011, 2017, or perhaps last Tuesday. Each date has passed without a nuclear exchange. The machines have mostly occupied themselves by flattering users, mangling fingers in photos, and confidently inventing facts for gullible people to believe.

That has not stopped Silicon Valley from treating artificial general intelligence as both an imminent miracle and an approaching monster. AGI supposedly marks the moment a machine can match human intelligence across a broad range of tasks, though nobody can agree on which tasks, which humans, or what counts as intelligence.

Nvidia CEO Jensen Huang thinks it already happened and nobody noticed. “For many tasks, we could say that we’ve already achieved AGI,” he said during Nvidia’s latest earnings call. He added that such milestones are “kind of senseless at this point.” So much for Skynet.

But he is right that AGI has become close to useless as a measure of progress. Companies can redefine it according to whatever their newest model happens to do well. It's a promotion, not a scientific standard.

Apocalyptic decoy

But AI isn't harmless even if it's not inclined to play with nuclear bombs. There are less theatrical dangers from AI fueled by old-fashioned human greed and malevolence.

Today’s AI can produce endless fodder for scams. It can clone voices and faces well enough to trick loved ones out of money, use phishing messages to hack secure servers, and flood social networks with synthetic accounts that never tire of arguing. None of this qualifies as AGI under any serious definition, but the distinction offers little comfort to the person hearing a fake recording of a relative begging for money.

The fixation on AGI may actually make life harder for regulators.

Profit isn't a good yardstick

Huang argued that the concept of AGI should be replaced. We should consider if it is “doing productive and useful work” and “generating profitable tokens.” That makes sense for Nvidia, but to ordinary people worried about AI, profitability is not much of a safety test.

Scams are profitable. That's why there are so many. Firing human employees for an AI model that is 30% as good at 10% the price is profitable (in the short term). Neither result is something to brag about when describing the result of your product.

Retiring AGI as the organizing obsession and fear of the industry makes sense, but that doesn't mean AI companies can relax. Intelligence is too complex to serve as the sole trigger for concern. Power might be a better lens. We can measure who uses AI, and how, as well as who is expected to clean up when they fail. Then we'll know how explosive an impact it's had on our lives.

Conversations about liability, identity verification, ownership, and other legal details don't have the visual excitement of mushroom clouds, but regulators should make AI companies discuss them instead. We need to keep an eye on the human hands on the keyboard, not the perpetually delayed Skynet.

Announced during Computex 2026, Nvidia's RTX Spark is billed as "a new beginning" for gaming laptops, productivity laptops, and small form factor PCs. Described as a "Superchip", it's an SoC that seeks to combine the company's AI technologies and its RTX graphics utility into one unified package for work and play.

The proposed RTX Spark specs are impressive on paper. 6,144 Blackwell GPU cores combined with 20 Grace CPU cores, and a maximum of 128GB of unified memory. Or, up to 1 Petaflop of AI performance, promising an all-day battery life in a slimline chassis that's purpose-built for Team Green's bespoke tech, such as DLSS, Reflex, and G-Sync.

On this side of the announcement, RTX Spark is confirmed to be coming to the Asus ProArt P16, the Dell XPS 16, the HP OmniBook X 14, the Lenovo Yoga Pro 9n, the Microsoft Surface Laptop Ultra, and the MSI Prestige N16 Flip AI+. Clearly, Nvidia is targeting not only the best laptop manufacturers, but also vying for a spot against the best gaming laptops.

Can one chip really do it all, though? While these are some bold promises, I have reservations. With the way the computing landscape is now, things will change, but that's not necessarily a good thing.

Monopolization of our modern lives

Few tech companies have had the meteoric rise that Nvidia has over the last decade. While the company made its name with graphics cards for gaming, it would then innovate time and time again as GeForce matured. The GTX generations saw graphics and rendering technologies get better than ever, with playable framerates at higher and higher resolutions achievable for even entry-level components.

I'd argue that it wasn't until the launch of the RTX 20 series and the AI boom that the rest of the world really took notice of Nvidia. It's easy to forget its history now, what with it being the biggest company in the world by market cap, beating out the likes of Apple, Alphabet (Google), Microsoft, and Amazon. This (clearly) didn't happen just because people were buying graphics cards to game on. No, Team Green made its serious cash from AI, and that's also the core selling point of RTX Spark, with its gaming functionality seemingly a secondary concern.

The AI revolution was spearheaded by Nvidia, with its chips now utilized in a myriad of different ways. Nvidia was the frontrunner in how GPU architecture could be used for wider AI applications, realizing that it's far more energy efficient than using CPU for AI, which means it's able to handle far more sophisticated demands as a result. The core Nvidia ecosystem is used to scale cloud facilities with its Blackwell, Hopper, and Grace Hopper chips, and its CUDA platform and libraries remain unrivalled.

In truth, GeForce and RTX tech is just a small part of Nvidia's business now, and it's been that way for some time. You only need to look at the Nvidia Drive infrastructure, using AI for autonomous vehicles, or the Nvidia Jetson modules for edge computing and wider mainstream robotics use. Team Green is no longer concerned with being a rival to Intel, Apple, or AMD. Instead, it wants to be the sole provider of hardware for every facet of our modern lives.

How does RTX Spark perform inside of laptops for gaming?

In a video published on August 26, Nvidia claimed that RTX Spark was Game Ready. The three-minute video outlined what consumers can expect from the upcoming unified Superchip and how it compares to a traditional CPU-and-discrete-graphics (or iGPU) setup.

The language around the RTX Spark's utility states that the Superchip is designed for "creators and developers who work on large projects who also game". What we can glean from this assessment is that being "Game Ready" here appears to be a tertiary concern for Nvidia. It's backed up by the boast of "not having to choose" between the gaming laptop and the work one, because "you'll just have the one".

Exactly how good is RTX Spark for gaming, then? We know that it runs on Windows 11, so confirmed support is standard for Steam, the Epic Games Store, and Xbox Game Pass, among others. Specifically, Alexander Mejia (Senior Product Manager) claimed that "Triple-A gaming is great on RTX Spark". Bold claims indeed. This is followed by footage of Alan Wake II, Cyberpunk 2077, Pragmata, F1 25, Fortnite, Neverness to Everness, Doom: The Dark Ages, Naraka: Bladepoint, Apex Legends, Arc Raiders, Anno 117 Pax Romana, and The Finals, shown to be running on the platform.

Good game stack there, sure, but what is the expected performance actually like? Nvidia gives us a (somewhat) satisfying answer: "100 FPS 1440p Gaming" when using Ray Tracing and DLSS. Unfortunately, that's all we get in terms of performance info, with no quoted statistics or more granular information available. There's quite the disparity in running Fortnite in High/Epic settings compared to pushing Cyberpunk 2077 or Doom: The Dark Ages to the max of their sliders. It's vague, but at least a little encouraging.

How does RTX Spark rival other SoCs for gaming?

We only have 100 FPS in 1440p to go off with RTX Spark's projected gaming performance, so the next logical course of action is to weigh that up against the likes of Qualcomm's Snapdragon X2 Elite, the Intel Arc G3 Extreme, and the AMD Ryzen AI Max series as a point of comparison.

We know that AI-powered upscaling (DLSS / XeSS / FSR 4) and AI-generated frame interpolation (Frame Generation / MFG) must be used to achieve playable framerates at target resolutions. In 1080p, with 2X MFG, Team Blue's chip can do 121 FPS in Cyberpunk 2077 and 178 FPS in Naraka: Bladepoint. Doubling that to 4X MFG, the incoming chip can go as high as 199 FPS and 267 FPS, respectively. In 1080p, the Radeon 8060S iGPU baked onto the Ryzen AI Max 395+ APU could do 66 FPS in Cyberpunk 2077 when rasterized and 45 FPS on average with ray tracing enabled.

This is about what can be expected from the native performance of the Qualcomm Adreno X2-90 iGPU baked onto the Snapdragon X2 Elite, which is able to achieve 43 FPS in Cyberpunk 2077 (via Notebookcheck) on High settings in native (no AI-scaling) 1080p. Where this SoC shines, though, is in its power consumption, with an estimated 0.841 FPS per Watt, only lagging behind Apple's M5 chip, as found inside the Apple MacBook Air 13 M5 model.

Taken as an average, the RTX Spark looks to be about on point with what those devices can do, but we won't have a full remit of the performance potential until we see the full game-by-game benchmarks closer to the proposed launch date. The real deciding factor for unifying between a CPU and a GPU for both work and play comes down to the factor that can sway people away from (or towards) a dedicated gaming laptop: the price tag.

Intergrated hardware you cannot afford

Computing components are more expensive now than ever; there's no other way to put that. Anything that utilizes memory in particular, such as graphics cards, flash storage, and RAM, has seen its street prices rise far above the usual MSRP, and they're not coming down any time soon. It's the case for price hikes of even the best phones and the best games consoles, nothing is safe from the supply-and-demand loop here, so RTX Spark-enabled laptops aren't expected to be cheap.

Ultrabooks in particular, which are the primary demographic market for RTX Spark, have never been affordable, but 2026 has meant an entirely new definition. You only need to see how the pricing has escalated from competitors to gauge how bad it could be. The Apple MacBook M5 range MSRP increased by 20%, and something similar could be observed with the likes of the Galaxy Book6 Ultra, which also shot up by 20%, just a month after launch, too.

If the prices of components that we know have historic rates are increasing month-on-month, then what about something as unproven and unknown as the RTX Spark Superchip? Nvidia has full dominion over pricing its CPU + GPU combo however it likes. As the most profitable and in-demand company on the planet, it holds all the cards here. We're at a turning point where products can almost double in price overnight, so who is to say that the cheapest RTX Spark-enabled laptop won't carry an eye-watering MSRP?

The idea behind RTX Spark is solid: a simple, unified approach built by Nvidia that combines all you need for work and play, similar to what Apple did when it started manufacturing its own silicon in 2021, albeit with more of an AI focus. RTX Spark will sink (or swim) based on its price-to-performance offering and whether consumers want an all-in-one device. One thing's for sure: Nvidia has the cash to muscle in on the market and can afford to lose face burying its competition in the process.

TechRadar reached out to Acer, Asus, Lenovo, MSI, and Razer for their takes on how Nvidia's RTX Spark could impact the computing market. A PR representative from Acer declined to comment, and despite PR representatives from each company forwarding our questions to the relevant teams and chasing the answers, none have commented on RTX Spark at this time. Team Green's Superchip stands on the precipice of change and progress, but at what cost?

• AWS plans to add 2 million Nvidia GPUs between 2027 and 2028
• The expanded deployment follows an earlier commitment exceeding one million chips
• New Vera-based CPUs will support increasingly demanding artificial intelligence workloads

Amazon Web Services and Nvidia have expanded their long-running partnership to add far more computing power for artificial intelligence workloads.

The cloud provider intends to add two million more GPUs across its global infrastructure between 2027 and 2028.

This expansion follows an earlier pledge to add more than a million chips starting in 2026, as customer demand outpaced expectations.

A bigger push into AI infrastructure

The companies also plan to bring new Vera-based CPUs into Amazon's cloud to support increasingly advanced AI applications.

Additional networking upgrades, including extended NVLink Fusion technology paired with custom high-bandwidth memory, aim to boost performance across large computing clusters.

Separate upgrades to Amazon's analytics service using Nvidia's cuDF software promise processing speeds nearing 3.7x faster than typical standard configurations.

The same upgrades are expected to deliver roughly a 30% improvement in price performance versus setups that rely only on standard chips.

Vector search capabilities on Amazon's search service are also improving, with index construction now completing roughly 9x quicker when run on GPUs.

Amazon's robotics division is separately working with Nvidia on simulation tools intended to speed up training for next-generation warehouse robots.

Government agencies are also included in the plan, with 100,000 chips reserved for sensitive government and defense computing needs nationwide.

"Customers want the freedom to choose the best tools for their AI workloads, and they want confidence that everything works seamlessly together," said Matt Garman, CEO of AWS.

“That’s why we’ve invested deeply with Nvidia to make AWS the best place to run Nvidia AI technologies, optimizing performance across our infrastructure from networking and security to deployment. This expanded collaboration gives frontier labs, enterprises and governments even more ways to build and deploy AI on AWS.”

Executives point to rising demand

Demand for accelerated computing across global cloud storage has continued to outpace projections that were made only months earlier this year.

“Nvidia and AWS have built one of the great growth engines of the AI era, and demand is running ahead of every forecast,” said Jensen Huang, founder and CEO of Nvidia.

“For 16 years, we have scaled Nvidia computing in the cloud together. Now, we are expanding our partnership across the full stack — GPUs, CPUs, networking, open models and software — to make agentic and physical AI real at an unprecedented pace and scale that only AWS and Nvidia can deliver.”

New processors delivered under the expanded plan are expected to offer notably faster inference and improved graphics performance for customers.

Recent hardware upgrades already deliver up to 4.6x faster inference and 2.1x stronger graphics output than the previous generation of systems.

Such a large hardware commitment suggests both companies expect artificial intelligence spending to keep climbing well beyond today's already elevated levels.

Whether actual demand keeps pace with these ambitious plans will only become clear once the new infrastructure is fully operational.

• Nvidia is looking to set up a political action committee (PAC)
• Many companies have set up PACs to help shape US policy
• The US mid-term elections have seen a record level of corporate funding

Companies using their money to shape policy in the United States is nothing new - in fact, it’s the whole reason the US is so dependent on car-centric infrastructure and transport thanks to the work of numerous motoring lobbying groups.

Groups such as Political Action Committees (PACs) donate to politicians aligned with their interests in the hopes of helping them win elections. The capital they provide is tax exempt and can be used for campaign advertisements and canvassing.

Now Nvidia might be getting involved in the PAC party, as the company plans to set up an employee-funded committee called NVPAC, according to a source familiar with the matter (via Reuters). Nvidia has refused to comment on the plans, but what sort of policy could it be looking to influence?

Nvidia wants its own PAC

The corporate world has already shown its hand in the US political election cycle, but tech companies are becoming more and more involved in electoral donating and funding. Donald Trump received a whopping $242 million from Elon Musk in the run up to the 2025 election.

Following his inauguration, multiple tech companies including Meta, Amazon, Google, Nvidia, and Broadcom made voluntary $1 million donations to Trump’s Inaugural Committee Fund.

Nvidia has seen huge growth during the AI boom. Its hardware is used across the AI ecosystem and the company has scaled up production and capacity to become the largest supplier of AI GPUs.

These GPUs are used in data centers to provide the necessary compute for AI models to be trained, run, and used by many different AI firms. But plans for data center developments across the US have seen some serious hurdles in the form of local and national opposition.

So much so that many state representatives are implementing moratoriums on data centers, or introducing legislation to make it harder for data centers to be built. In Q1 of this year, $130 billion worth of AI data center projects were cancelled or delayed due to public opposition.

The Trump administration’s policy has made clear that the US must lead in AI at any cost, particularly for reasons of national security. But that is at odds with many of Trump’s supportive MAGA base. Data centers have now become a key issue of the 2026 midterm elections, which has seen a record $646 million in donations from corporate entities.

The majority of Americans have made it clear that they have are concerned about AI and locally opposed to data centers, and to many of Trump’s inaugural donors that's bad news for business.

But there are few candidates willing to say they are pro-data centers. Texas Gov. Greg Abbott - who was once an advocate for data center development in Texas - recently criticized data center developers for their public image, stating they “got the backlash they deserve.”

Candidates who campaign from an anti-data center standpoint are taking a serious risk. The corporate entities funding candidates want them built, but those they represent don’t. This puts many candidates between a rock and a hard place.

Nvidia's DLSS 5 has been nothing short of a bubbling cauldron of controversy ever since the next-gen gaming tech was first aired. That initial reception shocked Nvidia as what seemed like a legion of PC gamers took up arms and vowed, well, all sorts of colorful things — most of them involving the term 'AI slop' in some fashion.

However, there are signs that this overwhelmingly negative tide of opinion is turning courtesy of a fresh revelation this week. This comes in the form of a leak rather than anything official from Nvidia, with Team Green's only DLSS revelations at Gamescom 2026 pertaining to the current incarnation of the feature (namely DLSS 4.5 Ray Reconstruction for better quality ray-traced graphics, alongside some interesting GeForce Now announcements).

To refresh your memory and recap — in a nutshell, as there's a lot more to it than this — Nvidia has billed DLSS 5 as a 'real-time neural rendering model' that polishes up a game's visuals using AI (without altering the game assets, and with the developer being given a decent level of control over the exact implementation).

What actually happened here was that on Friday, VideoCardz spotted that following NBA 2K27's early access going live, a DLSS 'neural rendering' DLL was discovered in the build. It didn't take long at all before modders (in the RenoDX community) fudged things to get this working in games ('SpeedLemur' being the principal modder who "made this happen").

So, in theory this is effectively our first glimpse at DLSS 5 in the wild, with those modders demonstrating what it looks like by using the third-person fling-stuff-about action-adventure Control – as seen on Reddit. However, do note that the tech is only working for application to character models for now (not the environment or objects).

Nuts and bolts

There have been some very interesting revelations from those experimenting with this apparent leak of DLSS 5 in terms of how it works, how it runs — and perhaps most telling is the broad reaction to this spillage, which I'll return to shortly.

First, the nitty-gritty. In this leaked form — so add a whole heap of seasoning, because nothing here is remotely official — DLSS 5 seems to have a small set of adjustable parameters, including seven different 'styles' and various sliders to play with intensity, tone and structure. We're told that neural rendering doesn't need much in the way of data to do its stuff, and works off much the same inputs as regular DLSS (basic upscaling – where all this started, remember?).

It's also for Nvidia RTX 5000 GPUs only (although that's still not fully confirmed, it was always likely to be the case anyway). The news on the performance front goes a bit awry, though, with the discovery that in Control the frame rate is cut in half by the AI polishing up the visuals. With an Nvidia RTX 5070 Ti running Control at 4K with ray tracing on high and DLSS (Quality), the frame rate was 71 fps and it dropped to 35 fps with neural rendering.

That is, of course, a nasty hit indeed. However, this is a piece of leaked code – in an unknown state as far as the stage of development is concerned (one clue suggests that the model may date back to the start of July) — extracted and fudged to work on an old game. Given all that, we can't take this as anything like an indication of what the finished version of DLSS 5 might be like performance-wise.

Although it's at least comforting that it works in a playable (enough) manner on a single RTX 5070 Ti graphics card, because if you recall, Nvidia's demos used not one, but two, RTX 5090 GPUs. Clearly a 50% frame rate drop won't fly, granted, but Nvidia wouldn't release this neural rendering trick if the hit was anything like that, because no gamer would use it in that state.

Change in tone

What we can really take from this leak and the modding efforts thereafter is quite limited, then. But what's clear is that as mentioned, there's been a marked turnaround in the commentary and debate on the image quality on offer with neural rendering.

As one Redditor observes: "Remarkable difference in tone of the reactions compared to when the [initial] Resident Evil footage was released."

Someone replying to that says: "People freaked out about what was obviously just a bad demo, and failed to see what was actually going on with the underlying tech.

"There were a few talking about how different tone mapping fixed the issue with minimal effort, or how it wasn't actually adding/changing details, and it turns out they were correct. People freaked out over nothing imo."

Another gamer notes: "This looks like legit magic. I was saying this when the reveal happened and people wanted to burn Nvidia down: the tech is amazing, the demo might be off because it's too heavy handed, but the tech itself is incredibly impressive."

And a further Redditor enthuses: "Insanely huge graphical update. There's no going back."

You get the idea, then, and it's quite an eye-opener how many comments there are along these lines. Quite a lot of gamers are impressed by this leaked spin on DLSS 5 — and I can see why, frankly, at least with the Control material presented here.

And remember, this is with DLSS 5 working on character models, with the facial tinkering and tweaking being one of the main causes of concern with Nvidia's initial demos. The likes of Flight Simulator addicts are positively drooling about what this might mean for the graphics of the landscapes beneath their virtual wings.

And there's a lot of excitement about how the tech might be applied to older games like Control to make them look a good deal better.

Skeptics remain

Naturally, not everyone is convinced. The detractors remain and are unswayed by the material which has popped up around this leak so far. They argue that the AI-enhanced Jesse (Control's protagonist) is still all too 'uncanny valley' in appearance, and that DLSS 5 is still very much a visual downgrade.

Others also worry that there aren't enough fine-grained controls for the developer in what we see here. Although again, we can't take this leak as evidence of what DLSS 5 will turn out to be eventually, whether that's the end result of the graphics themselves or the suite of dev controls under the hood.

The performance hit has been seized on, too — as noted, though we can't judge on that yet, not with an early leak — and some feel that while screenshots might look good, this doesn't hold up in motion.

Simply put: it is still too early to judge, and we need to grab large handfuls of seasoning with all this. But the general sentiment on DLSS 5 appears to be turning around in quite a sizable shift of opinion — and Nvidia may yet convert PC gamers to its AI-enhanced vision of the future (providing any performance hurdles are cleared, of course).

• Nvidia GPUs are about to run out of road in Windows 10
• Game Ready Driver support finishes in just two months
• With the last such driver coming in October, you need to bear in mind issues around game performance optimization and fixes – but you'll still get security updates

Gamers running Windows 10 need to be aware that if they have an Nvidia GPU, they're only two months away from that graphics card running out of road for support with drivers.

VideoCardz reminded us that Nvidia's deadline for ditching Windows 10 support is now nearly upon us, with the final Game Ready Driver that supports the older operating system arriving in October 2026.

Does that mean you need to be thinking about upgrading to Windows 11 if you have an Nvidia graphics card?

No, it doesn't – well, not necessarily, although there is a major downside to remaining on Windows 10 after October of this year. In case you're at all unsure, let's dive into the exact ins-and-outs of what's happening here – as well as touching on the subject of whether Nvidia might just have a change of heart here.

You'll still be secure with an Nvidia GPU

The key point to remember is that while the Game Ready Driver that comes out in November 2026 won't support Windows 10, Nvidia will still provide security updates for your graphics card. As Team Green explained in a short FAQ: "Windows 10 driver support will transition to a quarterly security update for a period of three years where critical bug fixes will be delivered to end users through October 2029."

That means Windows 10 users will get an update every three months to fix any critical bugs or security flaws with the Nvidia drivers. This will continue for another three years, so effectively to the end of the decade.

At that point, when 2030 is about to roll around, you will then potentially be risking exposure to a vulnerability as any driver security holes will no longer be patched. But that's still a good long way off.

So what does the end of Game Ready Driver support really mean?

After October, Windows 10 PCs will only get the benefit of these quarterly security updates, and they won't be covered by Nvidia's regular Game Ready Driver packages. That means as new games come out, Windows 10 users won't get the performance optimizations that these driver releases contain – or bug fixes for these titles.

As time goes on, then, more and more PC games will be left out in the cold in terms of that tweaking and optimization. That's bad news, of course, although it doesn't mean those games will be somehow unplayable. In fact, in many cases they might be just fine – but there are no guarantees, and you may get subpar frame rates (indeed, that's quite likely to be the case to some extent).

Of course, existing games will continue to run just as they always have, and you shouldn't run into any issues on that score.

Could there yet be a reprieve from Nvidia?

If you're not happy about the idea of not getting the latest patches, tweaks, and optimizations for new PC games, then you'll have to seriously start to think about planning your upgrade to Windows 11.

Assuming you have an upgrade path, that is, because your PC may not meet the hardware requirements of the newer OS. (Although bear in mind that a simple BIOS tweak may be all that's needed to ensure compatibility with the TPM requirement). If you're looking at a major PC upgrade, or an entirely new rig, that's a painful prospect given the RAM crisis and all the price hikes around that.

As one Redditor puts it: "'This is terrible news considering PC prices."

However, as another gamer on that Reddit thread observes, there may yet be a glimmer of hope of a reprieve from Nvidia. If you recall, just a couple of months ago, Microsoft quietly extended consumer support for Windows 10 for a further year, through to October 2027.

Now, when Nvidia announced the end of driver support for October 2026, which happened last year, as far as Team Green was concerned, it was aligning the curtailment of Game Ready Driver releases for Windows 10 with Microsoft's original consumer support extension. But now Microsoft has pushed out extended support for another additional year to October 2027 – could Nvidia mirror this move?

It doesn't seem likely, in all honesty, and I'd think Nvidia would have announced this already if it was the intention. Concrete driver plans are likely already in place that can't be changed. That said, you never know – and we can't fully rule out the notion of a last-minute reprieve. Watch this space.

• Nvidia is reportedly preparing to acquire Hugging Face for $12.9bn
• A 2023 investment round valued the company at $4.5bn
• Nvidia is on a roll of investing heavily in other companies

Nvidia has reportedly spent the past couple of weeks actively engaging with Hugging Face to explore an acquisition deal worth an estimated $12.9 billion, according to Business Insider reporting, citing an unnamed source familiar with the matter.

To date, the companies are yet to reach an agreement, but with the current valuation speculations, it would make a Hugging Face acquisition one of Nvidia's largest acquisitions to date.

Importantly, this wouldn't be the chipmaker's first run-in with Hugging Face – it took part in a $235 million funding round in 2023. At that time, Hugging Face's valuation was around $4.5 billion - making today's valuation nearly three times higher.

Nvidia and Hugging Face

While this may be one of Nvidia's biggest acquisitions to date, it's just one of a growing number, as the company recently admitted it had $18 billion committed to equity investments for the rest of the financial year, and it already holds $47.9 billion in private company stock.

However, there's still a shadow over the potential Nvidia acquisition, with Hugging Face previously turning down a $500 million Nvidia investment over concerns that a single shareholder could have such a big influence.

The high valuation also makes the prospective acquisition a costly one – at around 86x Hugging Face's annualized revenue ($150 million), Nvidia would be paying a premium for the platform's strategic position and existing relationships, against a backdrop of ongoing investments in models and development tools.

Regulator scrutiny might also question whether competing chipmakers and cloud service providers could be put at a disadvantage, particularly if Nvidia imposes hardware and software restrictions on the platform after a takeover.

Neither Nvidia nor Hugging Face responded to requests for comment.

• Nvidia's GeForce Now is officially inbound for the Steam Machine
• Support for the Steam Controller is also coming
• This gives owners of Valve's PC more flexibility to stream top-end games that won't run on the Steam Machine's more modest hardware

Nvidia has revealed that its GeForce Now service is coming to the Steam Machine.

As The Verge reports, Team Green made the announcement at Gamescom 2026, where it said that GeForce Now streaming will be arriving on the Steam Machine before the end of the year, and also that the Steam Controller will be supported. (The Steam Deck already is, of course).

In other words, you'll be able to enjoy the GeForce Now app on the Steam Machine, with the controller too (and support for the Steam Controller will be debuting for Windows 11 and macOS later in 2026, too).

Nvidia also told us that more Fire TV Sticks will be supported, with GeForce Now subscriptions set to be available directly from Amazon.

GeForce Now has just launched on the Firefox browser as well, and the streaming service is getting DLSS 4.5 goodies, which Nvidia notes include Super Resolution (upscaling), as well as dynamic Frame Generation and Ray Reconstruction.

The arrival of DLSS 4.5 Ray Reconstruction was announced at Gamescom, too, offering better image quality with ray-traced (and path-traced) games.

Analysis: Steam Machine dream team

GeForce Now support is something that several Steam Machine buyers (or would-be purchasers) have indicated they'd love to have. Why? Because Valve's PC hasn't got the beefiest of specs due to the hardware compromises that have been made to ensure this is a smartly compact box that fits nicely in the living room without drawing attention to itself (in terms of size or indeed noise).

This means that you can't play more demanding PC games on the Steam Machine simply because it doesn't have the graphics muscle. And this is why GeForce Now is viewed by some as the perfect partner for the Steam Machine — you can play many games locally, on the device, but when you want to enjoy a cutting-edge title that Valve's hardware can't hope to handle, you can stream it via GeForce Now instead (assuming it's supported by Nvidia, of course).

As one gamer on Reddit who was hoping for this move to be made noted: "Play the AA games on the [Steam] Machine and the AAA games with max graphics via GeForce Now."

That dream is soon to become a reality, although this setup sounds more like a nightmare to some who are quick to point out the costs involved, with the streaming subscription for Nvidia's service coming on top of the weighty price of the Steam Machine.

I can't argue with that, really, but if you do have a Steam Machine, dipping into GeForce Now waters does make sense in terms of playing (almost) everything you want in your living room with a slick and super-compact PC. (Assuming your internet connection is beefy enough for game streaming, that is). In this scenario, you are getting some serious gaming versatility, power, and convenience — it's a tempting, if pricey, combo.

The alternative is building a more powerful Steam Machine clone yourself (or buying a prebuilt along these lines), but bear in mind that this route comes with compromises of its own. Whatever the case, GeForce Now offers more options with the Steam Machine; there's no denying that.

• Nvidia's open source NeMo Switchyard 'smartly' routes each agent request to the cheapest model that can handle it
• The approach allows it to claim a 74% cost cut against a frontier-only model in return for a 6% reduction in accuracy
• NeMo Switchyard joins an increasingly well-established territory that already sees players such as RouteLLM, LiteLLM, and OpenRouter, in addition to in-house efforts at OpenAI and AT&T to save costs

Nvidia has released NeMo Switchyard, an open source model router that sits between an application and a pool of language models and decides, per request or per turn, which model should handle a particular task.

The aim is to maximize efficiency by picking the right model for the right task: pushing frontier-level models to do simple tasks that much smaller or cheaper models could handle is not only inefficient, but also costly for enterprise customers.

This comes at a time when enterprise customers are already grappling with rising costs that can quickly spiral out of control as they increasingly adopt AI.

An open-source addition to a growing chorus of AI routers

The problem Nvidia is trying to solve isn't new, and industry giants are already attracting a lot of attention. The emerging field itself is potentially lucrative, even if it focuses on cutting costs, and major players are looking to get in on the action; Stripe's upcoming $7+ billion acquisition of OpenRouter, underscores this.

Switchyard is no different from its peers; it is essentially a proxy that uses a routing algorithm to decide which AI model to send queries to, while delivering an answer in the format that the calling application or user requires.

It accepts OpenAI, Anthropic, and Responses API requests, translates between them, and documents the selected model, decision rationale, token usage, and latency for each call, enabling transparency and letting it tweak or tune its approach over time.

This makes sense compared with a one-size-fits-all approach that would otherwise be prevalent in a world with limited frontier-level AI compute, as LLMs continue to grow larger and more demanding. One example is what Nvidia points to: Nemotron Parse, a one-billion-parameter model primarily designed to extract structure from PDFs.

The question remains, however, whether such an approach is always feasible. Leveraging such a routing tool comes at a cost; LangChain's test of Nemotron 3.5 Lightning found that its judge model, which determines whether the agent is still on track after each turn, consumed up to 21.2% of total cost, second only to what it spent on Claude's Opus 4.8.

Running a more optimized judge model could therefore yield greater savings or lower overhead, but it could also hinder cases where users need a more basic AI model, and the judge model increases costs by reading or checking output at every turn.

A secondary concern would be variance: while costs are noticeably lower than running just Opus 4.8 in testing, it does swing from anywhere between $2.16 and $3.61, a mammoth 67% movement essentially guided by when it escalated queries to a bigger or smarter model. This tradeoff makes cost hard to predict: while routing lowers average spend, it widens the distribution around it.

LangChain also says users should not use Switchyard if their workloads are short or latency-sensitive; it estimates it adds about 700ms of latency because the judge model has to read all output.

Nvidia's take here is simple: it is pushing a packaged, open-source solution that offers a broad set of integrations in what is essentially a relatively fragmented space. Routing work to smaller open-weight models pushes inference toward hardware enterprises own, which Nvidia sells.

At the same time, cheaper inference per task has historically meant more inference, not less, which is the outcome a company selling accelerators would choose to woo enterprises, many of which are still on the fence when it comes to directly investing in the hardware that powers it all.

• GPU price hikes are coming later this year, PC Partner Group warns
• The parent company of Zotac notes that entry-level GPUs will suffer 'more severe shortages'
• A manufacturer of gaming monitors, KTC, has also warned on price hikes

PC gamers should be aware that there are further signs that GPUs could get more expensive, and gaming monitors too, so you might want to consider buying now rather than waiting.

First off, the gloomy GPU news which comes from PC Partner Group (PC PG), an Asian graphics card giant based in Singapore and the parent company of Zotac, Inno3D, and Manli.

VideoCardz reports that PC PG just published its latest fiscal results that came with a warning on GPU pricing as part of the trends and conditions which are likely to affect the company's finances in the upcoming year.

PC PG notes that "graphics memory costs are expected to rise further, leading to a substantial increase in VGA Card costs in the second half of the year." VGA Card simply refers to discrete PC graphics cards, and although it's a well outdated term now, it's one still used behind the scenes in the industry.

PC PG says that shipments of GPUs "declined substantially" in the first half of this year, and that they're likely to drop further in the second half of 2026. The firm writes that: "Beyond the price increases, entry-level VGA Cards are expected to face even more severe shortages, which may further drive ASP [the 'average selling price'] upward in the second half of 2026."

The company also highlights that other PC components, including system RAM and CPUs, are taking longer to get hold of.

VideoCardz also noticed that monitor pricing could be on an upward trajectory, too, as manufacturer KTC has issued a notice of a likely 'product price adjustment' in the future.

KTC observes that the "production cost of monitors has increased significantly" and that it can no longer absorb those pressures, therefore: "KTC's monitor product line may see a price increase in the future."

While that's just a 'may' rather than a 'will' or a certainty, it sounds like there's a high likelihood of it happening. Particularly because KTC advises: "For users with a clear need to purchase a device [monitor] in the near future, the current pricing window offers excellent value for money, and we recommend planning your purchase time accordingly."

And at the end of its statement, KTC talks about price adjustments being a "necessary measure", so it sounds pretty much like they're coming no matter what.

Analysis: is anything immune to the component crisis?

This isn't the only warning on monitor price increases, as AOC recently said that the rising cost of components for its products will mean price hikes starting from this month. Is this the start of a growing trend? The bigger monitor producers will, of course, have more financial wherewithal to weather component rises, but it may only be so long before even those firms start to jack up prices.

The way things are going, I wouldn't bet against it, and this latest news has people on social media asking what isn't immune to the RAM crisis – and what might we expect to see hit next?

How about pricier PC cases, one Redditor jokes: "Wouldn't you enjoy a simple case costing like $300?"

The crisis does feel rather relentless at this point, but the clearest worry is now GPUs. PC Gamer is tracking graphics card prices on a weekly basis, and there have been some clear cost increases coming through in the last few weeks, which suggests that PC PG's prediction is on the money here. And there are multiple other rumors of late warning that graphics cards are set for considerable price hikes. For me, this noise is becoming too consistent to ignore.

As I recently advised, if you need a graphics card upgrade, or think you will in the next year or two, I believe now is the right time to get in and buy. It's particularly notable that according to this latest rumor, entry-level GPUs are mentioned as apparently coming under more pressure, meaning that mainstream buyers are going to be suffering.

The effects of pricier video RAM, and other pressures besides, are no longer going to be mostly about high-end (or 16GB) cards becoming ridiculously expensive, and we could see some unfortunate hikes with lower-end GPUs.

There's very little good news of late across the gamut of PC components, and as we've already seen today, RAM and SSDs are getting even more expensive.

• Epic's launcher is being ported over to Linux
• This is happening just as Nvidia's GeForce Now streaming app came out of beta and officially launched on Linux
• This year is getting better and better for Linux gamers, as GOG is also porting its Galaxy client over too

Epic will be making a native version of its launcher for Linux, and on top of that, Nvidia's GeForce Now app for Linux is also official, having emerged from beta.

The delivery of the Epic Games Store for Linux will be a big win for Steam Deck owners, and other Linux gamers who want to access Epic's gaming platform with no fuss, and as Steam Deck HQ reports (via VideoCardz), that native incarnation is coming.

An Epic employee (@onepercentnachos) was questioned on this subject at an AMA ('ask me anything' session) on the Discord server for the Epic Games Store community. They replied that a native version of the store is coming "soon" albeit not in the next preview build. The staffer noted that "we need to do more than simply have a build of the launcher that can run natively on Linux".

Nvidia also just announced that the Linux app for its streaming service, GeForce Now, is no longer a beta. The officially released full version of the app "focuses on performance, stability and polish while continuing to support Ubuntu 24.04 and later", we're told.

And while it's not related to gaming, OpenAI's release of a ChatGPT app for Linux is another milestone for the platform. As TechCrunch reports, the Linux app for ChatGPT is still a preview, but it gives users a native app experience for a number of different distros. Those include Ubuntu 24.04 and 26.04 LTS, Debian 13, as well as Fedora 43 and 44.

OpenAI said: "Linux has been one of the most-requested platforms for the desktop app, and this launch extends ChatGPT and Codex across every major desktop operating system."

Analysis: a rosier future for Linux gamers — but Fortnite remains a bone of contention

The Epic Games Store arriving natively is great news for gamers running Linux — including SteamOS — as it means a much more convenient way of playing games from the store, as opposed to the current situation with fudging and workarounds (using a third-party app such as the Heroic Games Launcher).

It's worth noting that the distros which will be supported remain unconfirmed, but presumably the gaming-focused SteamOS will be one of them. Also, there will still be limitations even with this native implementation, notably games with anti-cheat. Indeed, Epic's own Fortnite doesn't work on Linux (and that's down to Epic actively blocking the game from running due to issues around cheating, which remains a source of controversy).

Given the apparent changing attitude here with its launcher being ported over, maybe Epic will reverse course on Fortnite eventually. Some gamers on Reddit are highly skeptical about that possibility though, and as one doubting Redditor put it: "I wouldn't hold my breath."

Another went just a touch further than that, observing: "I wouldn't be shocked if Tim Sweeney [Epic's CEO] would rather die than let Fortnite run on Linux, honestly."

At any rate, the Fortnite controversy aside, with GOG also working on a native Linux spin on its Galaxy client, there are some considerable strides being taken for gamers – and Nvidia's GeForce Now becoming official for Linux is another useful step forward to boot.

• Ukraine found 5,816 foreign parts inside 202 Russian weapons systems
• Russia reportedly plans to manufacture 11,000 strike drones this month
• Ukraine says foreign technology supports several Russian missile and drone programs

Ukraine's military intelligence agency says it has identified 5,816 foreign-made components inside 202 Russian weapons systems as of early August 2026.

The Main Intelligence Directorate of Ukraine's Defense Ministry, known as HUR, presented these findings after showing diplomats recovered missile and drone parts.

Officials say the recovered components came from cruise missiles, ballistic missiles, strike drones and armored vehicles used across the battlefield today.

Foreign parts recovered from missiles and drones

Among the samples shown to Ukrainian diplomats was a Jetson Orin microcomputer manufactured by Nvidia, officials said.

That component was reportedly recovered from the wreckage of Russia's newer S-71M Monochrome cruise missile, intelligence officials said.

HUR also presented an inertial module containing parts manufactured by the US firm Texas Instruments and Japan's TDK Corporation, among others.

Dozens of additional foreign-made elements were reportedly found alongside these parts, though HUR did not name every single manufacturer involved.

Intelligence specialists further displayed a Geran-2 strike drone alongside components recovered from a Geran Seeker drone and the Banderol cruise missile.

“Showing and providing evidence of the use of foreign technologies in Russian weapons of terror is an important task in monitoring compliance with sanctions,” said officials at Ukraine's Main Intelligence Directorate, HUR.

Sanctions evasion continues despite restrictions

Western sanctions targeting Russia's defense sector first emerged in 2014, after the European Union imposed an arms embargo following Moscow's annexation of Crimea.

That regime expanded dramatically after the Russia – Ukraine conflict started in February 2022, prompting a coordinated response from Western allies.

The US, the EU and the UK each imposed export bans covering dual-use electronics, computers, telecommunications gear and sensors.

These measures restricted semiconductors, microprocessors and navigation components capable of enhancing Russia's military and technological capacity on the battlefield.

Despite these restrictions, investigations show Russia continues obtaining Western components through transshipment networks routed via Hong Kong, mainland China and Turkey.

A recent investigation found nearly 800,000 shipments of restricted goods reached Russia between 2022 and 2024, worth close to $4 billion.

Some shipments reportedly passed through a single Hong Kong address before reaching Russian defense manufacturers linked to missile and drone production.

The EU sanctioned 60 additional third-country entities this year, many based in mainland China and Hong Kong, over such practices.

Analysts say weak coordination among allied sanctions regimes allows re-export networks to keep supplying Russia's defense industry despite years of restrictions.

These same supply routes are now reportedly feeding a sharp jump in Russia's planned drone output for the coming weeks.

Russia's military-industrial complex plans to manufacture roughly 11,000 strike drones this month, according to Ukrainian intelligence officials.

Vadym Skibitskyi, deputy chief of Ukraine's Main Intelligence Directorate, told the outlet RBC-Ukraine that this month's output includes several Geran variants currently used along the front lines.

These reportedly include the Geran-1, Geran-2, Geran-3, Geran-4 and Geran-5 strike drones, all now entering serial production lines this year.

Harpy, Gerbera, Gerbera strike and Gerbera seeker variants are also reportedly included in this month's expanded production schedule, officials indicated.

HUR claims these drones make use of parts that typically originate in Europe, Asia and North America before reaching Russian factories through indirect supply routes.

Moscow reportedly relies on so-called gray-market schemes and third countries to obtain restricted technology despite ongoing international sanctions.

Kyiv is now using this finding to press allies for tighter export controls and stronger enforcement of existing sanctions regimes.

Via United24Media

• Nvidia open sources its cuFile APIs and storage stack into a new GitHub organization with Google, Intel, and Meta as founding maintainers, and formally launched Storage-Next with 40-plus flash and storage vendors
• Nvidia's key unveiling is its SCADA framework, which moves the storage control path onto the GPU, allowing parallel GPUs to pull data directly from storage
• The driver is KV cache economics: inference fetches data in a few hundred bytes at a time, while SSD controllers tuned for 4KB spend the same effort on either size

Following the recent Future of Memory and Storage conference, Nvidia has argued that the next leap in AI rests as much on the storage feeding accelerated computing as on the silicon doing the computing.

The company open sourced its cuFile APIs and the storage stack beneath them, and formally launched an industry initiative called Storage-Next with more than 40 flash and storage vendors.

It also put a name to SCADA, the framework that lets GPUs pull data from drives without the CPU brokering every request.

A 512-byte problem that comes into focus as storage becomes key

Nvidia's approach here is not new. cuFile was introduced in 2019 as the interface component of GPUDirect Storage and has shipped since 2021; its role is to take the CPU out of the data path. Bytes move by Direct Memory Access (DMA) straight between the drive and GPU memory, with no bounce buffer in host RAM. What stayed on the CPU was the control path: host software still decided what to fetch and issued every request, with the GPU as the DMA target rather than the initiator.

That split is invisible at large transfer sizes. A one-megabyte read essentially amortizes the per-request cost. At 512 bytes, the ratio inverts, the fixed cost dominates, and the CPU saturates long before the drives do. SCADA is the piece that moves the control path onto the GPU, letting it construct and complete its own storage requests and absorb per-operation latency, just as it already absorbs memory latency by keeping hundreds of thousands of operations in flight. The two are complementary rather than successive: cuFile for bulk transfers, SCADA for high volumes of small random reads.

The problem it solves is one that enterprise SSDs exhibit, having been tuned around 4KB random reads for nearly a decade to match virtualization and databases. As a result, most controllers do the same amount of work to serve 512 bytes of data as they would a 4KB read request.

AI inference access patterns are considerably smaller than that: embeddings run a few hundred bytes, and the KV cache blocks sit well under a kilobyte. Serving them from 4K-tuned drives imposes something like eightfold read amplification, and at tens of terabytes of small objects, that amplification decides whether flash works as a memory tier at all.

The KV cache, essentially the attention state for every token already processed, grows with context length, and agentic deployments run thousands of concurrent conversations. It quickly outgrows GPU memory, and recomputing evicted entries costs more GPU time than reading them back, so the standard design spills from GPU memory to system memory and flash, and refills through high volumes of small random reads.

Serving that from flash rather than DRAM increases the context length and the concurrent user count each GPU can support, which in turn affects the per-user cost of serving a model. This also explains why Nvidia is focusing on 512-byte IOPS rather than raw bandwidth, since inference performance depends heavily on the former.

Opening up cuFile is a departure; this layer has historically lived inside CUDA, and the logic is not charity. A GPU-initiated storage interface only pays off if drive, controller and array vendors build to it, and vendors do not build to a proprietary interface owned by the company whose GPUs they are feeding.

Publishing the interface, open-sourcing the implementation, and convening 40-plus vendors to standardize the underlying hardware behavior is how Nvidia aims to make GPU-initiated storage the industry default. It also stands to benefit the most from that outcome, since it sells most of the GPUs in question.

StorageReview notes Intel's participation as a significant development; a leading supplier of the x86 silicon currently sitting in storage controllers has signed on to maintain software designed to remove that silicon from the I/O path. Google and Meta co-maintaining a layer that standardizes how accelerators reach storage, while building their own accelerators, points in the same direction.

Nvidia's take is a coherent, well-argued push at a real bottleneck, with unusually credible partners attached. Partner systems from DDN, Dell, HPE, IBM, VAST Data and WEKA are due in the second half of 2026. Kioxia's XL-Flash drives built for 512-byte access are in development under Storage-Next.

Nvidia's roadmap calls for Gen7 SSDs sustaining 100 million IOPS each, which is a target controller vendors are designing toward rather than a product anyone can buy. Storage-Next itself has been discussed publicly since GTC 2025; this week, it acquired both a membership number and a framework to build against.

• 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.