• Nvidia's RTX 6060 may not arrive until 2029 a leaker claims
• They note that when there are supply issues (the current RAM shortage), Nvidia tends to drag out the release of lower-end GPUs
• They also believe it's unlikely the RTX 6060 will match the PS6 for frame rates

Nvidia's budget GPU for its next-gen range may not be released for three years yet, if a new rumor about the RTX 6060 turns out to be correct.

Wccftech spotted a post from leaker Kepler on the NeoGAF forums, who chipped in with speculation on Nvidia's graphics cards in a thread about the cost of the PlayStation 6.

Kepler was responding to a comment observing that the PS6 is going to go up against the RTX 6060 when it launches in terms of comparisons to PC gaming, and the leaker said — add seasoning with this as ever — that the "6060 will launch much later (and based on recent trends I doubt it will match PS6)".

In a further post, the leaker added: "When there's a supply shortage Nvidia tends to really drag the release of lower end cards. There's a good chance RTX 6060 comes out in 2029."

This would mean we're looking at an early-to-mid 2028 launch for the RTX 6090, 6080 and 6070, which lines up with more recent rumors. Of course, that's not a foregone conclusion, and Kepler is clearly speculating here.

Recent reports from analysts indicate that the PS6 will likely launch in 2028, though some have argued that Sony's next-gen console could appear late in 2027 (alongside other devices, perhaps).

Analysis: late, as well as short on performance?

Of course, what Kepler contends here is what Nvidia is supposedly planning right now, and just as with the PS6, a lot of the direction that Team Green goes in with its next-gen GPUs will depend on the memory crisis. Graphics cards do, of course, use video RAM (VRAM), and the next generation of GeForce products will be expected to up the ante on that score. (Gamers are already unhappy with Nvidia's current-gen VRAM configurations).

The problem is that there's no shortage of predictions that RAM cost inflation is going to get even worse in 2027, after it has already spiked in a big way through the middle of this year. The theorizing then runs that 2028 is also going to be a difficult year, which lends weight to the argument that the RTX 6060 will indeed be late to the GPU party.

In short, I wouldn't rule out the possibility of a 2029 arrival for the RTX 6060 as suggested, which obviously isn't great news for those hoping to score a more wallet-friendly next-gen Nvidia GPU.

It's also interesting to note that Kepler believes the RTX 6060 won't be as powerful as the PS6 for gaming frame rates, but I'd take that claim with even more salt than the rumors around timing.

Gauging hardware capabilities at this stage is obviously a trickier task, but Kepler's assertion isn't an impossibility of course. Note that the current RTX 5060 matches the PS5 Pro's gaming performance (roughly, for rasterization as opposed to ray tracing), so if the RTX 6060 couldn't equal the PS6, that would be a clear disappointment for PC gamers on a budget (particularly if the VRAM loadout again turns out lean).

Having been at Nvidia's RTX Spark event in San Francisco earlier this month, I was lucky enough to get a firsthand walk through of the many things the 'superchip' can do. Since then I've spent almost every waking moment putting the ARM-based RTX Spark through its paces and seeing what makes it so different from other chipsets that have come before.

The RTX Spark is Nvidia’s first System on a Chip (SoC). Combining an 18-core CPU and 5120 Cuda core Blackwell RTX GPU all in one, or a 20-core CPU and 6144-core GPU for its meatier version, it offers an integrated chipset in the vein of Apple’s M series chips. While Apple’s silicon is known for its oomph, that Blackwell RTX GPU means you’ll also get considerable gaming performance here to boot.

How does this translate in terms of raw numbers? It’s certainly got a lot going on under the hood, as the RTX Spark N1X chip I’ve been playing around with squeezes 20 cores into its CPU. While the 2,623 single-core performance I clocked on Geekbench 7.1 falls somewhat short of the 4,288 we got for Apple’s M5 chip, its 21,899 multi-core score massively outstripped the latter’s 17,926. That means that for tasks like content creation, software development and AI, you should be able to expect more oomph in your corner.

So given the potential it promises across agentic AI, creative workflows and gaming, let’s take a look at how it performs across each of those areas in turn.

Hybrid agentic AI

During the launch of Nvidia’s RTX Spark SoC, there was a huge focus on its potential for hybrid agentic AI. Now, cloud-based AI models are undoubtedly pretty powerful but I absolutely have security concerns about uploading my most private data to the cloud and I’m not entirely sure I want to blindly trust the companies behind some of these models.

Using local, open-source models that I can vet myself and that never transmit sensitive data outside of my network feels like a much safer alternative — but not everybody necessarily wants to cut themselves off from the power of frontier models. That’s why Nvidia has introduced a hybrid-system for the RTX Spark instead.

Instead, the hybrid-workflow that the RTX Spark supports allows you to select your choice of local models and ring-fence them for certain parts of an agentic workflow, while outsourcing specific, demanding tasks to cloud frontier models. As a result, users should be able to take a far more granular approach, deciding exactly how much they want to rely on public vs private models.

For people who like to vibe-code complex products or are generating demanding 3D scenes, not being forced to choose between binary security options of a lax, YOLO approach and a full-blown lockdown will likely be a valuable tool.

So now that I’ve spent some hands-on time with the RTX Spark, how has its agentic performance delivered on these promises? To get a broad picture of I ran the MLPerf Client benchmark to simulate data analyst and SWE agentic workflows using the Qwen 3 8B local model, with each run hitting an average duration of 63.7 and 50.8 seconds and processing up to 14,005 and 10,238 tokens respectively.

I also benchmarked some generative image and video workflows using ComfyUI. When testing workflows using Flux2.Dev (NVFP4 and Flux2.Klein 4B Base) NVFP4 models for image generation, it took 46.5 and 16.2 seconds to generate respectively, while the LTX 2.5 (Distilled) NVFP4 model took 88 seconds. That’s broadly in line with what Nvidia has been promising and shows how rapidly the RTX Spark can generate media using local models.

Raw data will only tell you so much though. That’s why I spent days training my own AI agent using Hermes and the Qwen3.8 27B open-source local model to see what the RTX Spark could do.

Certainly, it seems to have absolutely ripped through some tasks on its Extra High setting. Feeding it some analytics data in CSV form, I asked it to tidy up the spreadsheet, analyze the data and present its key findings and after 12 minutes of pondering and tweaking, it happily spat out a pivot table and some observations on the data. While I wasn’t hugely surprised by any of its conclusions, I’ve spent months poring through this data and the fact it could identify the most essential points without any of my secure information leaving the laptop was pretty impressive.

Some experiments I conducted involved a little more patience and tweaking of the agent’s workflow to get the best results though. The first time I asked my Hermes agent to connect to my online email client and provide a digest of the day’s emails, it spent 75 minutes trying various methods to exchange tokens with Outlook’s online client, before crashing unexpectedly. However, the second time I played a slightly more active role, providing it feedback when I felt it was drifting or had come up against hurdles and it successfully set up the brief in 70 minutes.

So with some patience and judicious prompting, you can definitely get some good results utilizing a local model. Additionally, you don’t have to go through this full process every time — ask Hermes to save something as a skill and it will automate this process in future or even schedule it as a recurring Cron Job. You can even ask it to work out ways to improve the efficiency of existing skills, meaning it should be easier to minimize the run time and resources they require in future.

Creative workflows

You’d be forgiven for thinking this means the RTX Spark laptops are predominantly focused on AI tools. But that couldn’t be further from the case: while a lot of the press surrounding the chip has been on its agentic AI tools, this really isn’t the sole string to its bow.

As you can tell from the fact that the RTX Spark is used in the latest Asus ProArt P16, creative work is a big focus for the new SoC. Even if you’re not interested in the power it offers for generative AI, the sheer number of RTX-compatible creative apps out there, whether that’s Blender, Adobe Creative Suite, Canva or Unity shows how many creative giants are throwing their weight behind the new SoC.

Eager to test how well the RTX Spark handles creative work, I used Puget Bench to automate a range of tasks in Photoshop and Premiere Pro. PugetBench’s standard Photoshop workflow took 10.83 minutes and netted a score of 11,801, while its standard Premiere Pro pipeline took 8.12 minutes and achieved a score of 76,234, putting it firmly in the range of a true creative workstation. That means it sits in the rough region of Apple’s M5 chip, which scored 13,755 and 69,887 respectively, besting it on complicated video work but not quite equaling it for still imagery.

For those who use generative AI in their work, I also benchmarked some generative image and video workflows using ComfyUI. When testing workflows using Flux2.Dev (NVFP4 and Flux2.Klein 4B Base) NVFP4 models for image generation, it took 46.5 and 16.2 seconds to generate respectively, while the LTX 2.5 (Distilled) NVFP4 model took 88 seconds. That’s broadly in line with what Nvidia has been promising and shows how rapidly the RTX Spark can generate media using local models.

While I’m neither an aspiring cinematographer or a budding Blender artist, I’ve spent decades tinkering with static images so I dug out an old 4K, 300dpi Photoshop file. It was already pretty huge as it featured lots of hi-res layers, clipping masks and adjustment layers but I tripled the number of layers for good measure and added a bunch of filters and effects. Processing time remained less than a second and even halving the image size only took about five, which is pretty impressive given the sheer number of layers and groups I was working with.

And the RTX Spark should even be able to speed up these applications’ built-in AI tools, thanks to the recently announced collaboration between Nvidia and Adobe. Optimizing the apps to take full advantage of the chip’s GPU, Adobe promises that RTX Spark laptops offer 2x AI processing, editing, color and effects. That’s a real advantage for anyone that uses these AI tools as part of their workflow.

Gaming performance

Given Nvidia’s history, gaming is naturally also a big focus for the RTX Spark chipset. As most games run on x86 architecture, SoCs haven’t been the most natural home for gaming in the past. However, Nvidia’s stature in this space means plenty of developers launched RTX Spark native versions of their games when the chip was launched on October 16, including Cyberpunk 2077, Doom: The Dark Ages, and 007 First Light — while the Call of Duty series is due to land in 2027. And for those that aren’t yet compatible, Prism emulation will mean you’ll be able to run many titles with only a slight impact on your CPU performance.

When it comes to gaming performance, the RTX Spark is certainly no slouch. It netted a 3DMark Port Royal score of 8,133, Steel Nomad score of 2,846 and a Speed Way score of 2,872. That’s a touch below Asus Zephyrus G14 with Nvidia GeForce RTX 5070 Ti’s respective 10,119, 3,488 and 4,031 scores. But it’s considerably better performance than you’d get out of most laptops with an integrated GPU such as the pre-RTX-Spark Dell XPS 16, which secured values of 3,888, 1,488, and 916.

But while these simulated benchmarks are helpful for getting a broad view of gaming performance, in-game benchmarks are the best way of gaining real insight into how actual titles perform. While testing the RTX Spark, Cyberpunk 2077 delivered a stable 30.16fps at 1080p on Raytracing: Ultra settings and Doom: The Dark Ages averaged out at 48.58fps at 1080p — very similar to the 32.33fps and 55.63fps we got out of the Asus TUF Gaming A14 with Nvidia GeForce RTX 5060.

Nothing compares to actually trying out games first hand though, so I fired up a few of my favorites and tried them out. Cyberpunk 2077 honestly felt pretty playable even using the above Raytracing Ultra setting and once I’d switched down to Ray-tracing Medium, it ran at a wonderfully fluid 60fps.

Meanwhile, running Gears of War: E-Day at Ultra settings felt as smooth as tahini drizzled over a marble countertop. As I crunched through waves of the Horde, there wasn’t any noticeable slowdown even at Ultra settings, with the action never dipping below a seriously fluid 115fps. So it might not match the RTX 5090 for sheer power but anyone barring elite gamers will find a lot to like here still.

So even though it’s naturally not at the level of dedicated gaming laptops like the MSI Vector A18 HX A9W, I’m still pretty impressed with gaming on the RTX Spark chip. And that only becomes more pronounced when you compare it to similar lightweight SoC systems. While the MacBook Pro M5 is similarly impressive in terms of creative performance, it was barely able to scrap 11.5fps in Cyberpunk 2077 on Raytracing: Ultra settings.

As a result, if you’re looking for a chipset that will give you not just great performance in one specific task but great performance across everything you do, the RTX Spark has proven it will be a solid choice.

Nvidia CEO Jensen Huang has sung the praises of Microsoft's new Surface Laptop Ultra, describing it as "so beautiful" and "incredible".

Speaking at a developer event in London, Huang outlined the development process that went into building the hardware powering the new product, which is the first Microsoft device powered by an Nvidia chip.

“This computer is going to be incredible - it took us four years to build it," Huang told attendees including TechRadar Pro, "this project is really a work of love - we had several thousand engineering years dedicated to it, we build a brand new revolutionary chip, a whole new line of PCs, it’s so beautiful - you’re going to love it.”

The end of the traditional PC?

Revealed in September 2026, the Surface Laptop Ultra is targeted towards developer and AI usage, powered by Nvidia’s RTX Spark platform with an N1X processor and RTX Blackwell graphics, supporting up to 128GB unified memory

This is alongside 24GB RAM and 512GB storage, while the next version has 32GB RAM and 1TB storage, making it ideal for creators, AI specialists, engineers, scientists, developers, and other users running demanding applications.

But as Huang tells it, the creation of the device, set to launch on October 16 2026, was a long and arduous process.

“It took four years to do this," Huang said of the hardware development process. "We were thinking about agentic systems, AI and autonomous systems, and how they’ll be able to access your files, and be grounded on information, and be able to reason about it and use tools - but the ultimate tool is your personal computer, right?"

"So wouldn’t it be cool if we could create a personal computer, a PC that actually runs an agent locally - so it can go off and just work on things for you - and not only could it just run locally, it would be smart enough to use the tools."

Huang explained his vision of "texting" your PC to ask for help, to get it to do tasks like rendering images and updating slides, then delivering them straight to you.

"Wouldn’t it be awesome if you could just text your computer - you don’t have to bring it with you anymore - just leave it at your desk!”

“So we created this thing - it took us four years to do it, so I called Satya (Nadella, Microsoft CEO), and the two of us talked about it…and so we had to create a brand new chip, we had to wait until Blackwell was done - and this thing has 1 petaflops of computing, and because we knew the models were going to be big, we had to redesign how the GPU uses memory - so this is the first Nvidia GPU with shared memory, one petaflops, Blackwell architecture - and also it has to run everything a PC doesn’t.”

“There’s three types of PCs, right," Huang outlined, "there’s the PC that basically you use to type in - that’s called a PC, there’s the Nvidia gaming PC, you’ve got GeForce in that, and you’ve got the Nvidia workstations that runs all kinds of applications…so we figured that if you’re going to have an AI go use the tools, it should be able to use every single tool…it runs everything, it runs every AI framework, it runs any AI model as well."

• Microsoft will make Maia its main inference engine at multi-gigawatt scale, following Google and Amazon into in-house silicon
• Microsoft's upcoming Maia 300 trades a modest 36% compute gain for 2.5x Maia 200's memory bandwidth, betting on memory-bound decode, with production in 2027
• Hyperscalers supply just over half of Nvidia's data center revenue at a 75% gross margin, so each move to in-house chips affects not only its pricing power but the chip industry's sustainability as a whole

In 2024, Microsoft told the European Commission that Google was the only company vertically integrated at every AI layer, from chips to a mobile app store, and that "everybody else must rely on partnerships to innovate and compete."

Now, it has revealed plans to follow the same route without breaking a sweat.

In an Official Microsoft Blog post, Scott Guthrie, executive vice president of Cloud + AI, said Microsoft will scale its Azure Maia accelerators to multi-gigawatt deployments starting in 2027 and make Maia the primary inference system for Microsoft's products and services.

The vehicle of its choice is Maia 300, which Rani Borkar, president of Azure Hardware Systems and Infrastructure, and Saurabh Dighe, corporate vice president of systems planning and architecture, detail in an architecture deep dive.

Feeding the beast, not growing it, as it takes the Google route

The move is a pointed statement from one of Nvidia's biggest customers, and it lands as bubble worries grow louder. Gartner forecast this week that AI providers will spend nearly $1 trillion on infrastructure in 2026 alone, and that AI revenue won't cover the build-out profitably until at least 2032.

If the largest buyers of AI hardware stop paying Nvidia's margins for the work they do most, the most richly valued part of that build-out starts to look exposed. Whether Maia 300 is the final catalyst for such a move, though, is a harder case to make.

Maia 300 moves from Maia 200's single die to a multi-chiplet package, with compute on TSMC's N2P process and memory-controller and I/O functions split onto separate dies. Eight HBM4 stacks deliver 288GB at 18TB/s per accelerator, against 216GB at 7.1TB/s for Maia 200, according to StorageReview's breakdown of Microsoft's spec sheet.

Microsoft hasn't abandoned compute so much as rebalanced around memory. In decode, the token-by-token phase that dominates long reasoning chains and agent loops, accelerators spend much of their time waiting on data rather than doing arithmetic.

The Maia 300 tries to close that gap by keeping data near the compute that uses it, through a non-uniform memory layout across chiplets, software-managed on-die SRAM, and narrower number formats, including an enhanced MXFP4 and sub-3-bit storage formats, with hardware conversion between them. The Maia 300 announces something the rest of the industry already knew, albeit more subtly: Custom silicon is no longer a side project for hyperscalers.

Google, the company Microsoft singled out two years ago, began recognizing revenue from TPU system sales delivered to customer data centers in the second quarter, CFO Anat Ashkenazi said on Alphabet's July earnings call, while CEO Sundar Pichai said the company is "seeing strong demand for TPUs as standalone systems from other customers."

It isn't the only one either; Anthropic has lined up 3.5GW of TPU capacity through Broadcom for 2027, on top of an earlier deal for up to a million TPUs, and Meta was reported in 2025 to be in talks to rent-cum-purchase them. Amazon has gone the same way. CEO Andy Jassy said in April 2026 that Amazon's chip business, spanning Trainium, Graviton and Nitro, had passed a $20 billion annual revenue run rate and was now "one of the top three data center chip businesses in the world." OpenAI, meanwhile, has lifted the lid on its own Jalapeño accelerator.

Nvidia's margins: the AI holy grail?

The incentive is plain from Nvidia's own results. In the quarter to July 2026, the company reported $96.2 billion in revenue, $89 billion of it from data centers, at a 75% gross margin. CFO Colette Kress put hyperscale revenue at $49 billion on the earnings call, meaning a little over half of Nvidia's data center business comes from a small group of buyers, the biggest of which now design their own accelerators.

Every inference workload that moves in-house is one where that buyer pays closer to manufacturing cost instead of Nvidia's margin. Microsoft told investors in July that Maia 200 was up to 40% cheaper to operate than Nvidia's latest chips on OpenAI and Microsoft models.

It did, however, fail to name a particular GPU it called a "leading GPU-based system," an important distinction given Nvidia's focus on efficiency via Vera Rubin, which OpenAI even refused to compare against outright.

The reveal didn't move stocks much; on Monday, Nvidia shares closed down 1.29% after the reveal, while Microsoft's rose 1.14%, and Microsoft's opaqueness about efficiency gains may be one reason.

The other reason is equally important: Evercore ISI analysts estimate Maia will account for only about 10% of Microsoft's AI chip spending by the first quarter of 2027, CNBC reported, and Nvidia has guided to $108 billion in revenue for the current quarter.

Scale is arguably an ongoing constraint, however; at 167 kW per rack, a gigawatt of rack power is roughly 6,000 Maia 300 racks, or about 575,000 accelerators, before cooling and facility overheads.

Going the Google route hasn't stopped anyone buying from Nvidia either. Amazon, with its "top three" chip business, is still set to take more than 3 million Nvidia GPUs by mid-2028.

Alphabet, which now sells its own TPUs, raised its 2026 capex guidance to between $195 billion and $205 billion in July, lists Vera Rubin alongside its own TPUs, and Ashkenazi said it is "still in a supply constraint environment."

Microsoft keeps Nvidia and AMD for training, and Satya Nadella said earlier in 2026 that "because we can vertically integrate doesn't mean we just only vertically integrate."

Jensen Huang's answer to the custom silicon question on Nvidia's August call could have been written about Maia: "Whereas many of these XPUs are inference-specific chips for one cloud or one service, NVIDIA is a platform." Microsoft's counter-bet is somewhat different: one cloud is big enough.

The deeper problem for the pin theory is that custom silicon changes who collects the margin, not how much gets spent. Gartner warns that the industry needs about $8.5 trillion in revenue between 2026 and 2030 to earn a reasonable return on its build-out, versus no more than $3.6 trillion it expects AI to generate. Microsoft isn't responding by building less; it is planning gigawatts more, and cheaper tokens arguably help close that revenue gap rather than widen it.

Some of what Microsoft saves will also flow elsewhere in the supply chain. Maia 300's pitch rests on HBM4, the same constrained memory Nvidia's Rubin uses at the same 288GB per package, and its compute dies come from TSMC, which supplies everyone.

Google's TPUs are developed and supplied under a long-term agreement with Broadcom, whose CEO Hock Tan said that "many investors in AI don't understand what they're putting their money into," and who expects chip shortages to ease only around 2028 or 2029. Nvidia isn't sitting idle either; it already paid $20 billion to license Groq's SRAM decode technology, a sign it sees the same inference threat coming.

Until production silicon ships and someone outside Microsoft can measure it, Maia 300 is a well-argued design whose most important numbers are ones that only Microsoft has seen, and a reminder that the biggest threat to Nvidia's margins may be its best customers rather than its rivals.

It's been a theory for quite some time now that Nvidia is — what's the polite way to put this? — let's say deprioritizing PC gamers, perhaps, or more specifically, its gaming graphics cards. But this week has added more evidence to the collective weight that GeForce GPUs are increasingly at risk of being sidelined further by Team Green.

I'm talking first about the story that emerged a few days ago, where a leaker claimed Nvidia is diverting all GB202 (Blackwell) chips to make AI (RTX Pro) graphics cards. That's the graphics processor used in the RTX 5090, but apparently not anymore; it's for AI boards only now, we're told.

That makes some sense as the RTX 5090 is already disappearing from shelves, certainly in the US, where you can't buy one for less than the best part of 10 grand (from gouging third-party sellers, with no major retailers actually having stock anymore). So arguably this effective discontinuation of the RTX 5090 is already happening.

On top of that, the latest developments are that Micro Center is reportedly listing the Blackwell flagship as 'not carried', and Nvidia itself has removed all mention of the RTX 5090 from its own GPU marketplace (though it is still mentioned elsewhere on the site). Search for the RTX 5090 on that marketplace, and you won't find it any longer — the highest-end graphics card listed is now the RTX 5080 (hat tip to Wccftech for spotting this being flagged up on Reddit).

Aside from an actual announcement from Nvidia (which probably wouldn't ever happen anyway), that seems like about as clear an indication you can get that the RTX 5090 has been shoved out of the exit door, never to return, because Nvidia needs those GB202 chips where they are most profitable. RTX Pro GPUs offer a much heftier premium that Team Green can heap on top.

A new Blackwell flagship?

So, what does this mean? For starters, if it is true that the RTX 5090 has been scrapped, the RTX 5080 becomes the flagship Nvidia GeForce GPU. Once the existing stock of RTX 5090s has been depleted, this could mean the price of the RTX 5080 is inflated. When I say inflated, I mean jacked up even more, because it has already seen some nasty price hikes. Currently, the RTX 5080 is at around $1,800 in the US on Newegg, or £1,450 on Overclockers in the UK – and those are the lowest prices; it goes up from there. It's a very similar story in many other regions.

This is clearly a concern for gamers as you can see from the Reddit thread I pointed out above, with comments such as: "If you want a 5080 better buy one while you can still afford one." Really, it's debatable if the card is anything like 'affordable' relative to its graphical frame rate muscle even now.

Here's where another rumor comes into play. Elsewhere on the grapevine, there's talk that Nvidia may actually release the RTX 5080 Super variant which has long been speculated about. This would be a 24GB graphics card, and it'd essentially replace the discontinued RTX 5090 – with the theory being that Nvidia would also ditch the vanilla RTX 5080 (16GB). This would leave the rumored RTX 5080 Super as the sole high-end GeForce graphics card, quite some distance above the RTX 5070 Ti.

If that happens, imagine the pricing concerns here – what MSRP would Nvidia attach to the RTX 5080 Super? And then consider the cost inflation that will be added when you take into account that this card will also be sought after for AI usage (as seen with the RTX 5090).

If an RTX 5080 Super is indeed inbound for early 2027, that would also suggest that the next-gen RTX 6000 family is further away than some rumors believe – as this Super version would be a stopgap GPU to keep the Blackwell range ticking over. The RAM crisis may have a fair bit to do with this strategy also, given that most predictions foresee it getting worse next year (and the misery continuing into 2028, and maybe beyond). Of course, launching a new GeForce range into such memory headwinds wouldn't be easy.

Software focus and streaming dreams

All in all, it feels to me like there's a stream of subtle – and not so subtle – hints and rumors that Nvidia is very much slowing down the development of gaming GPUs (while not wanting to give that impression, naturally). The other part of the equation here is the increased focus on the software side, of course, rather than the hardware. DLSS 5 is out and Nvidia is already pushing hard with honing and refining that neural rendering tech, and something else that happened earlier this week was that Team Green highlighted some big leaps in performance here. Pretty huge strides forward, in fact.

Of course, you could argue that all the development resources being ploughed into DLSS – including frame generation, which got a hefty boost alongside DLSS 5 – shows Nvidia isn't abandoning gaming GPUs for PC gamers. And I concede that's true to an extent, but there is very much a vibe of a major slowdown leading towards the feared deprioritization of GeForce GPUs – and there's another angle to consider here too: cloud gaming.

Don't forget that GeForce Now also benefits from DLSS. Just this week, Nvidia added DLSS 4.5 override controls to the streaming service to help push up frame rates – those on the RTX 5080 (Ultimate) plan can use Dynamic Multi Frame Generation of up to 4x now. Ray Reconstruction for bolstering ray-traced visuals is available, too, among other options.

Would Nvidia rather have you subscribed to its cloud gaming service, making monthly payments, than buying a GeForce GPU for a one-off sum? It seems an obvious direction for the company to be heading in, and with increasing warning signs around Team Green's gaming GPUs, I'm becoming more convinced that this is where Nvidia's priorities are shifting for the future of PC gaming.

This isn't to say GeForce graphics cards are definitely on their way out, or that an RTX 6000 line-up won't happen – it's already well underway after all – but beyond the next generation, I'm increasingly dubious about where GeForce GPUs might fit into the big PC gaming picture. Between AI demand and cloud gaming (plus increasingly faster internet connections), there's a squeeze underway that could prove painful for the GeForce line-up – and the ongoing RAM crisis is hardly helping, either.

Nvidia CEO Jensen Huang has laid out his vision of how OpenClaw will help shape the future of technology - with a particular focus on robotics.

Speaking at a “Build a claw” event in London, Huang explained how OpenClaw and AI agents as a whole can spur on incredible advancements in a range of fields.

However it was the field of humanoid robotics that seemed to catch his fancy, with Huang outlining the need for proper controls to ensure the potential of the technology can be reached.

AI is "becoming useful"

Huang opened by noting how OpenClaw was “one of the most important models” ever seen, helping to further progress across multiple industries.

“We’ve now seen an inflection in artificial intelligence - it’s become useful,” he declared. “The type of things people are doing with it, as simple as software coding, being a marketing agent assistant, research assistant, lab assistant, helping scientists do drug discovery and engineers run experiments - we’ve got agents everywhere.”

Spotting a robot built by Nvidia partner Humanoid, Huang noted how OpenClaw is the “first credible” agentic harness, which he likened to LLMs inside an exoskeleton, before extolling the possible benefits to using the technology for robotics.

“When it first came out, we thought wow, what a really terrific digital robotic system,” he said, “kind of like a human robot, that is basically the physical harness of a large language model, essentially - the motors and the articulation and the wheels and the arms, those are just tools that are used by the agent that’s sitting inside the computer.”

“So when we first saw that we said, this is incredibly useful, and we’re going to help make it even better - but the one observation that we made was that these agentic systems, if they were able to be deployed in a safe and secure way, we’re going to have to create some kind of container for you.”

Huang noted that the current situation was similar to the birth of the internet, when a browser itself was not isolated from the computer, so when a user interacted with a website, it would download an app which had full control of bare metal - and often delivering what he described as “a special package to your PC”.

“The obvious thing to do is create essentially a container, an isolation layer - essentially another operating system inside the operating system - so we created this thing called OpenShell,” Huang said, highlighting how this creates an isolation system for agentic systems.

“When you’re building an autonomous system, the first thing you should do is assume zero trust. So you give it a policy, you get minimal privileges - so if you’re asking an autonomous system to make you an omelet, you don’t have to give it access to a chainsaw - all you need is a frying pan…so whatever the task you give your agent, you give it minimum rights, minimum privileges, and then you get to monitor it to make sure that it’s compliant with policy”

“Zero trust, minimum policy, continuous monitoring, contained inside an isolation system that is separated from bare metal - that’s how you develop and deploy agents safely.”

But what if unexpected incidents or complications arise? Huang was optimistic that things would not get out of hand.

“We will monitor the autonomous systems continuously, and if that robot were to start to violate or do something that’s inconsistent with the privileges we gave it, it would escalate to us, and we can decide whether we want to shut it up!” he laughed.

“These kind of systems, these escalation systems, these security systems, all have to be built so that we can give all these autonomous machines and autonomous agents and deploy them in a way that is able to do so safely and securely, so we can realise the incredible future potential of the technology that we’re building.”

• Nvidia claims that for each gigawatt an AI data center uses, only 70% is used for AI work
• Inefficiencies include cooling and power distribution, failures, and restarts
• While a chip shortage was considered to be the main bottleneck to AI data center capacity, it is now a facility’s connection to the grid

AI data centers are inefficient, says Nvidia, with various physical and systems issues contributing to power use beyond AI processing. This opinion, shared by data center builder Vertiv at its recent annual EMEA event, underpins future development steps, in which it plans to assemble sustainable facilities.

Vertiv and Nvidia have been collaborating since early 2026 on co-designing the next generation of data centers, with Vertiv providing power and infrastructure management for Nvidia’s GPUs.

With a maximum of 70% of electricity drawn from the grid used for AI processing, the remaining 30% represents inefficiencies that optimized data center designs for the future should reduce.

That's not efficient

Although the various inefficiencies affecting AI data centers may not come as a surprise, the scale of them might. According to Nvidia – via its Vice President of Supercomputing, AI Cloud Infrastructure, and AI/HPC for EMEA Rod Evans – that 30% consists of infrastructure and system issues.

For a one-gigawatt data center, 20% is lost through cooling, power distribution, and conversion. A further 10% is lost to incorrectly configured racks, as well as failures and restarts.

Evans says that “You sign a contract for 100 megawatts, but you never actually get that. With a bit of luck, you’ll get 93 megawatts, and at certain times of the day, even less.”

Nvidia believes some of the 30% (which can be as high as 40%) can be recovered. Evans notes that “A data center is actually designed for peak load and outages, not for what it typically uses.” This means that unused capacity can be tapped, and power and cooling can be revised to find the optimum configurations.

Bottlenecks of power

Rod Evans states that Nvidia and Vertiv are planning data centers that are more efficient and sustainable. “Together, we want to build sustainable AI data centers where the focus isn’t on using as much power as possible, but on getting the most out of every watt.”

One of the most significant details from the press conference is the challenge posed to AI data centers not by a shortage of chips, but by a lack of power. Bottlenecks are resulting in underpowered data centers, with some waiting for several years to receive the power to match their capacity.

This can be impacted by location. Vertiv’s EMEA president Paul Ryan explained that while you can get power in some locations but no permit. “In another, the permit is granted quickly, but there’s no power. And sometimes you have both, but the location is so remote that you can barely get the equipment there.”

• Nvidia has considerably improved the AI model for DLSS 5
• This means the tech now offers up to 40% faster frame rates
• Team Green has also improved frame generation, and is bringing DLSS 5 to two new games: Hogwarts Legacy and Where Winds Meet

If you were wondering how soon Nvidia might start ramping up the performance of DLSS 5, well the answer is that it just happened.

Team Green's neural-rendering tech, which officially debuted in NBA 2K27, has only been out for just over a month, but Nvidia is claiming that it has doubled the performance of the AI that drives DLSS 5.

When it was first previewed in March 2026, DLSS 5 needed a pair of RTX 5090 GPUs to run, with the company improving the performance of the AI model by 5x for the release of the tech in NBA 2K27. With this latest update, it's now 10x more performant than that baseline.

Nvidia also made it clear that it has reduced the video memory (VRAM) usage of the tech considerably, noting that: "The DLSS 5 October update shrinks the [AI model] footprint by 24% to roughly 118 million parameters while reducing 4K VRAM usage by 20%. The result is half the GPU overhead of the September [launch] version while maintaining comparable visual fidelity."

Based on Nvidia's testing in NBA 2K27, that translates to boosts in frame rates in the order of 30% to 40%. Check out the video clip below to see the gains in the basketball game.

This new DLSS 5 model launches on October 16, with availability for RTX 5000 GPUs, RTX Spark laptops and the GeForce Now streaming service.

Nvidia also announced that DLSS 5 is being introduced for Hogwarts Legacy on November 5, and also Where Winds Meet in the "near future", although we aren't given a specific launch date in the latter case.

Finally, away from DLSS 5, Nvidia announced that it's bringing in a new performance mode for frame generation, speeding up the tech considerably. The company explains that this benefits from a "faster and lighter algorithm" that boosts frame rates even further, and that it'll offer up to 35% better frame rates with an RTX 5060.

This performance mode is called 'Preset C' in the Nvidia app's frame-generation override.

DLSS 5 is also coming to RTX Remix on October 23, meaning old games can get a graphical makeover with added neural-rendering goodness. That only makes sense. given that gamers have been fudging support (in various ways) with classic PC games anyway.

Analysis: a laudable step forward — but gamers want more

It's remarkable to see DLSS 5 performance being boosted so quickly, although Nvidia needed to act in this regard. One of the main criticisms of this tech has been that the performance hit is too hefty, and it requires a powerful RTX 5000 GPU to run smoothly enough — at a time when the prices of such graphics cards are getting rapidly more expensive.

That said, an increasing number of gamers seem to be accepting that DLSS 5 is worth the cost to the frame rate for the visual uplift. Those folks will doubtless be pleased with Nvidia's progress here, and likely wouldn't disagree with Team Green's assertion in its blog post that 2026 has been a "year of relentless DLSS innovation."

Of course, there are still skeptics who'd rather Nvidia didn't bring AI into play in this way with DLSS. And it's also true that these early optimizations are where the larger gains will be carved out, and as the AI model is further refined, performance improvements are likely to become a lot more modest.

There are also gamers out there who are asking whether, now that the AI model is much improved, this going to usher in wider support for Nvidia's older graphics cards? As this Redditor put it: "Great so could we get official support on the [RTX] 40 series now since the new model is so efficient?"

It's a good question, given that Nvidia has promised that after tuning RTX 5000 performance it's going to work on bringing support to the last-gen RTX 4000 range. I'm guessing this won't happen until early next year, though.

The other work on DLSS frame generation is obviously welcome, too, albeit the new 'Preset C' option has attracted some criticism for causing a bit more glitching and artifacts while boosting frame rates by a decent amount. Not everyone is seeing this, mind, and how obvious it is may be somewhat subjective going by reports (and it could obviously depend on the specific game, too).

At any rate, this preset should be useful for laptop gamers looking to further bolster frame rates with relatively modest Nvidia GPUs — and indeed, it's likely tied into the release of RTX Spark laptops to help their gaming chops.

• Dell Pro Max 18 Plus leads current local AI rankings, yet Nvidia's launch could unsettle results
• RTX Spark offers 128 GB shared memory for larger locally run models
• Spark's 300 GB/s bandwidth trails Dell's graphics memory at 896 GB/s

The Dell Pro Max 18 Plus still holds the title of fastest AI laptop, topping local AI rankings ahead of every other machine tested.

Its RTX PRO 5000 chip reached 185.1 tokens/s on the Phi model, 140.5 tokens/s on Mistral, and 119.7 tokens/s on Llama 3.

However this lead faces a test on October 16 2026, when Nvidia's RTX Spark laptops go on sale from several manufacturers, with preorders already open.

RTX Spark arrival

Microsoft is expected to field the Surface Laptop Ultra from $2,599, while Dell is bringing the XPS 16 Creator Edition from $3,799.99.

Asus, HP, Lenovo and MSI are also launch partners, while Acer and Gigabyte are expected to add models later.

Nvidia rates RTX Spark at up to 1 petaflop of FP4 AI performance, with 6,144 CUDA cores and up to 128 GB of shared memory.

The 1 petaflop figure covers only FP4 calculations that exploit sparsity, an upper limit that everyday workloads may not reach.

The company also claims the chip can run large language models of about 120 billion parameters locally, with a context window of one million tokens.

That is slightly above the 109 billion parameters that the Dell Pro Max 18 Plus can reportedly handle locally.

This does not automatically mean that RTX Spark will outperform Dell's workstation in AI workloads, as model capacity alone does not determine inference speed.

The price Dell buyers paid for the lead

The Pro Max 18 Plus offers Core Ultra 5, 7, or 9 processors and RTX PRO 1000 to 5000 graphics, with prices starting at $3,488.

Dell's top configuration, equipped with an RTX PRO 5000, 128 GB of memory and two 1 TB drives, cost $9,245.52 when the laptop launched.

A similar configuration later appeared at roughly $14,000, while the laptop now sells for almost $18,600, double its launch price after 14 months.

The top model is built around the Intel Core Ultra 9 285HX processor with 24 cores, a 5.5 GHz boost clock and a 55 W rating.

Its RTX PRO 5000 graphics chip has 24 GB of GDDR7, a 175 W power ceiling and a graphics clock of 1,807 MHz.

Memory comes from a single 128 GB CAMM2 module running at 6,400 MT/s, while storage consists of two 1 TB Gen5 SSDs configured in RAID 0.

The 18-inch QHD+ display runs at 2560 × 1600 and 120 Hz, covers 100% of the DCI-P3 colour space and contributes to a minimum chassis weight of 7.17 lb.

Nvidia's case for displacing Dell rests partly on RTX Spark's shared memory, which can accommodate AI models exceeding Dell's 24 GB graphics capacity.

However, Spark's shared memory delivers about 300 GB/s of bandwidth, compared with 896 GB/s for Dell's RTX PRO 5000. Spark also has 6,144 CUDA cores, fewer than Dell's 10,496

To overtake Dell, an RTX Spark laptop must beat its token-generation rates for Phi, Mistral and Llama 3 under the same UL Procyon tests.

Matching those results while consuming 45 W to 80 W of chip power could also improve battery endurance beyond Dell's recorded three hours and 39 minutes.

Via StorageReview

• Asus confirms the ProArt GR1X will use the 20-core RTX Spark N1X, mainly in 64GB and 128GB configurations, shipping before year-end via premium retailers
• The GR1X matches Asus's Ascent GX10 on size, weight, fans, and power brick, and joins RTX Spark boxes from Microsoft, Dell, Acer, Honor, amongst others
• The GR1X still has no price, while Microsoft's Dev Box lists at $5,999, DGX Spark at $6,950, and GMKtec's 192GB EVO-X5 Pro at $6,599

Asus has filled in more of the blanks on the ProArt GR1X, the RTX Spark-powered mini PC it first showed at Computex 2026.

The company now says it will sell the machine mainly in 64GB and 128GB unified LPDDR5X memory configurations through a limited set of premium retailers before the end of 2026, according to its recent launch announcement.

Reports also confirm a 240W USB-C power supply built to the USB Power Delivery 3.1 EPR specification, a weight of 1.48kg (3.26lb), and a chassis of 150 x 150 x 51mm, or roughly 1.1 liters.

Plenty of details sans a price tag

The same reporting claims that the GR1X will rival the Mac Studio, even as the published spec sheet now points to a single-chip option: the 20-core RTX Spark N1X with a 6,144-core Blackwell GPU and a 140W TDP.

Asus announced the device on June 1 as the ProArt Mini PC, with up to the same 20-core chip, up to 128GB of memory, and up to 140W of thermal headroom, and promised availability from Fall 2026, but it has yet to publish a price tag, even in recent updates.

In the recent update, it has solidified the specifications on offer; when Asus renamed the machine in late August 2026, it suggested several RTX Spark chip options, dropped the 140W reference, and listed 180W of USB-C power delivery. The 140W figure has returned as a TDP, the power input has risen to 240W, and the cut-down 18-core N1X appears to be reserved for laptops, which matches Nvidia's RTX Spark spec table, which lists only the 20-core part for desktops.

This might not be a completely new product, though, but rather something of a reskin: Asus's Ascent GX10, its Linux-based take on Nvidia's DGX Spark, also measures 150 x 150 x 51mm, weighs 1.48kg, and ships with a 240W adapter. Asus's October 5 release for a 64GB GX10 describes two 140 x 80mm fans, the same fan size the GR1X page lists.

The overlap runs deeper than the chassis: RTX Spark is based on the same GB10 silicon as DGX Spark, as The Register has noted, and Nvidia CEO Jensen Huang said in September 2025 that the N1 processor was "going to go into the DGX Spark."

None of the GR1X's headline performance numbers have been independently tested yet; its 1 petaflop of FP4 performance is a sparse figure, to say the least. Nvidia's DGX Spark datasheet, Asus's own GX10 release, and Microsoft's Surface RTX Spark Dev Box announcement all footnote it as relying on sparsity, while the GR1X page carries no such caveat. Dense FP4 throughput will be lower.

The promise of 120-billion-parameter models with up to 1 million tokens of context is conditional too, and Asus's October 8 release concedes that what fits depends on model architecture, quantization, and context length. How quickly those models run depends mostly on memory bandwidth, which Asus's product page doesn't list, even as competitors already offer nearly 1.5x the memory.

A beefed up threat from a rebadged APU?

ASUS's most direct competition does not come from Nvidia's partners at all, but from AMD. Its Ryzen AI Max+ 395, better known as Strix Halo, has been the default chip for 128GB mini PCs for some time, including AMD's own Ryzen AI Halo developer platform, which went on sale through Micro Center in June for $3,999.99.

Like RTX Spark, it pairs a 16-core CPU with a large integrated GPU, in this case a 40-CU Radeon 8060S, and a single pool of LPDDR5X, but its 256GB/s of bandwidth trails Nvidia's chip.

Its successor, Gorgon Halo, narrows the bandwidth gap and opens a capacity lead. The Register describes the Ryzen AI Max+ 495 as "essentially a factory-overclocked version of the Strix Halo APU," with clocks about 100MHz higher, but it supports up to 192GB of LPDDR5X-8533 at 273GB/s, matching DGX Spark and, by extension, ASUS's own offering. Up to 160GB of that can be allocated to the GPU, according to Framework and GMKtec, which is more than the entire memory pool of any RTX Spark machine currently being sold.

Unlike ASUS's offering, Gorgon Halo boxes are arriving with pricing in tow, and they are not cheap. GMKtec's EVO-X5 Pro sells for $6,599 with 192GB and 2TB, against a listed regular price of $6,799. Peladn's YO2 495 costs CNY 49,999 (about $7,454) in China, and Lenovo's ThinkCentre X Ultra starts at $3,699 when orders open in November, with up to 128GB and support for clustering up to four units. Framework's 192GB Desktop is still listed as coming soon, however.

The silence on pricing is the GR1X's biggest open question, and the memory market is not helping. Nvidia has raised its own 128GB DGX Spark to $6,950, nearly 75% above its launch price, while partner 64GB GB10 systems are expected to sell for around $4,999.

AMD's machines have followed the same curve. According to The Register, a 128GB Strix Halo box cost between $2,000 and $3,000 a year ago, and around $4,000 by the time AMD's own workstation launched this summer.

Asus's ProArt laptops start at $2,599.99, and Asus offers them in memory tiers as low as 24GB. The GR1X, by contrast, targets the 64GB and 128GB tiers, making Microsoft's $5,999 Dev Box, Nvidia's DGX Spark, and GMKtec's $6,599 EVO-X5 Pro its direct competition.

Until Asus publishes a price, confirms a bandwidth figure, and commits to a date, and reviewers can test its thermal and gaming claims, a 20-core chip and 128GB of memory are the least surprising things about the ProArt GR1X.

• A PC maker in the US says 'retail GPU pricing is expected to climb significantly'
• A further rumor suggests AMD just put up the cost of VRAM, which will affect the prices of graphics cards
• Rounding off a trio of fresh rumors is a claim that Nvidia has discontinued the RTX 5090

There have been a few developments in the GPU world over the weekend — and none of them bode well for the prices of graphics cards down the line.

For starters, Wccftech reports that according to the Board Channels — a reliable enough forum for chatter from the supply chain over in China — AMD has increased the cost of the GDDR6 video RAM it sells to board-making partners. This means that those graphics card makers will inevitably pass that cost on to consumers — if this rumor is accurate of course.

Bench Life further observes that more expensive VRAM isn't the only upward pricing pressure here, and that the cost of other graphics card components — including the main circuit board itself — is also rising.

Secondly, as Wccftech also noticed, we have a custom PC maker in the US, StinceBuilt, informing us on X that it is "starting to see increasing supply constraints on graphics cards" and that, "Retail GPU pricing is expected to climb significantly, and these elevated prices could stick around for quite some time."

Finally, VideoCardz highlighted another post on X from regular leaker MegaSizeGPU, who claims Nvidia will not allocate any more GB202 (Blackwell) chips to make the RTX 5090, and that all this silicon is going to be used in RTX Pro (AI) graphics cards.

In theory, that means it's the end of the line for the flagship RTX 5090, with its replacement (the RTX 6090 in theory) still quite some way off in the distance (a more recent rumor indicates that it's probably due to land in 2028).

Analysis: a cacophony of warning bells

Of all those sentiments, the last one needs the most seasoning, although it's believable enough that Nvidia is essentially canning the RTX 5090. By that I mean that if this is true, I don't think this will be an official (announced) discontinuation, but the way it'll play out is that Nvidia will allocate so few chips for the GeForce flagship that stock will effectively dwindle to next to nothing.

Arguably we're already seeing some evidence of this, certainly in the US where at Newegg, for example, you can't buy an RTX 5090 directly from the retailer, but only from third-party sellers (often in Asia). And the cost of the graphics card is tipping up towards $9,000, such is its scarcity — and you'll pay from £6,000 upwards in the UK, although at least stock is available to buy directly from retailers in that country. Still, I wonder how long that will last; and the idea of Nvidia pulling the plug on the RTX 5090 in a stealthy behind-the-scenes manner certainly feels like it's possible.

Meanwhile, the suggestion of AMD's Radeon GPU price rises backs up similar recent rumors, and the warning from the PC maker in the US that price tags are set to 'climb significantly' is another ominous addition to the warning bells that have been sounded of late.

I'm still very much of the opinion that it's a good idea to buy sooner rather than later, even though GPUs are already overpriced, because I fear that the situation could get substantially worse in 2027, as the grapevine indicates.

The key question is how long a recovery from these pricing pressures might take in the case of GPUs. Again, I have a suspicion that it might take a lot longer than we might hope.

• Samsung and five affiliates are putting $1 billion into Helix Digital Infrastructure
• KKR founded Helix in June 2026, with Nvidia and Vistra among investors
• Helix will secure power capacity, which limits how quickly data centers open

Samsung and five affiliated businesses are committing $1 billion to Helix Digital Infrastructure, deepening the group's involvement in the backbones of artificial intelligence.

Samsung Electronics will provide $500 million, while five other affiliates supply the remaining investment through the venture.

The commitment combines semiconductor expertise, cooling equipment, construction, computing services, batteries, and infrastructure financing within one corporate strategy.

Samsung brings multiple infrastructure capabilities together

Helix, established by KKR in June 2026, is intended to develop data centers while securing electricity and network connections

Its founding investors also include Nvidia, Kuwait Investment Authority, and US electricity company Vistra, adding financial and energy capabilities.

Samsung's involvement extends beyond its financial commitment because several affiliated businesses can contribute technologies and services needed to develop and operate data centers.

Samsung Electronics contributes memory technology, and its FläktGroup subsidiary provides cooling systems for data centers.

Samsung C&T can handle major engineering and construction projects, with Samsung SDS developing data centers and providing rented GPU computing capacity.

Samsung SDI adds batteries for uninterrupted power systems, supporting facilities where servers must operate continuously without electricity interruptions.

The group therefore controls several functions that competitors would typically obtain from separate equipment manufacturers, contractors, technology providers, and infrastructure companies.

Samsung says the arrangement could help it move from supplying hardware toward participating directly in building artificial intelligence infrastructure.

Helix also plans to obtain power capacity, making electricity availability an important part of its proposed development model

That approach gives Samsung exposure to infrastructure requirements extending beyond processors, memory products, cooling equipment, and computing systems.

Huawei and SoftBank appear closest among rivals

Samsung's breadth is a major advantage, and few rivals can compete with such an extensive combination of infrastructure capabilities.

Huawei is among the closest comparisons, with capabilities spanning Ascend processors, storage, networking, security, cooling equipment, and electricity infrastructure.

That range gives Huawei significant technical reach, although the company may not have a comparable $1 billion infrastructure commitment.

SoftBank also has semiconductor interests through Arm, Ampere Computing, and Graphcore, as well as plans for an artificial intelligence facility in Ohio.

Its combination of chip businesses and infrastructure ambitions gives it substantial reach, although matching Samsung would still require capabilities from multiple businesses.

Nokia offers networking products for artificial intelligence facilities and has attracted investment from Nvidia, but its role remains concentrated around connectivity technologies.

Ericsson's work with SoftBank focuses on artificial intelligence applications for cellular networks rather than developing complete computing campuses and supporting infrastructure.

The difference is that Samsung can draw on affiliated businesses for capital, components, construction, cooling, computing, and battery systems within the same corporate group.

Huawei may be closest in technological range, while SoftBank has substantial semiconductor resources and growing ambitions around AI infrastructure, but neither company can combine these capabilities through affiliated businesses as extensively as Samsung does.

• DeepSeek open sourced Ascend ports of its production kernel and communication libraries with Huawei's support in tow
• Published benchmarks ran on a proof-of-concept hardware kit DeepSeek says is not a publicly distributed release
• Two repositories are genuinely new and have received hundreds of GitHub stars versus Nvidia's thousands; the other announced components are updates to existing tools

DeepSeek has revealed it is open sourcing a set of infrastructure components for Huawei's Ascend accelerators.

DeepSeek published two completely new repositories, DeepGEMM-Ascend for matrix multiplication and DeepEP-Ascend for the all-to-all communication that mixture-of-experts models depend on.

The other components named in the announcement were upgrades: TileKernels, DeepSelect, and FlashMLA are existing projects that received Ascend-targeted updates, a distinction FourWeekMBA established from the GitHub creation dates.

An open source offering with limited use cases?

Reuters framed it as two Chinese firms deepening ties in search of an alternative to Nvidia, with Bloomberg called the centerpiece a programming language named TileLang, China's answer to CUDA.

The question is whether any of this matters when engineers are still on the fence about using these tools and access to Chinese compute remains, at best, elusive.

The biggest piece, as Bloomberg puts it, TileLang itself is neither new nor DeepSeek's own offering. It was open-sourced in January 2025, and its Huawei Ascend adapters were published in an external repository, tilelang-ascend, on September 29, 2025, a full year before the recent announcement.

While TileLang supports Nvidia's CUDA backend, it also supports Huawei's Ascend as an ecosystem backend, classified and maintained in a separate repository. One could therefore argue that TileLang is less a weapon aimed at Nvidia than a shared abstraction layer that half a dozen Chinese accelerator vendors are quietly standardizing on, with Huawei as the largest of several tenants rather than the landlord.

The new software has limitations: DeepGEMM-Ascend requires the Ascend 950 series and CANN 9.20. DeepEP-Ascend requires Ascend 950 silicon with UBMEM connectivity, so it targets a narrow audience beyond Huawei's own engineers and limited Chinese AI labs with access to that silicon.

Despite this, at a time when Nvidia chips are unlikely to be generously exported by the US or allowed in by China, Huawei's Ascend silicon has a large task on its shoulders: DeepGEMM-Ascend keeps the same Python package name and API shape as its CUDA sibling, and DeepEP-Ascend aligns its public buffer interfaces with the Nvidia version.

That is because the goal isn't a performance claim, but switching-cost reduction, and switching costs are precisely what Nvidia's moat is made of: it costs far too much for most developers to abandon CUDA or code their own alternative currently.

Public signals show this remains an uphill task; at the time of writing, DeepGEMM-Ascend has 515 stars and 39 forks, compared with DeepGEMM, which has a comfortable lead at 8,522 stars and 1,359 forks, for example. The story is similar for DeepEP-Ascend and DeepEP, with the latter surpassing 10,200 stars versus DeepEP-Ascend's 224.

The reputational problem Huawei faces here predates the repositories. ChinaTalk, citing the Financial Times, quotes a Huawei researcher describing CANN as making Ascend chips difficult and unstable to use, and a Chinese developer characterizing work on the 910B as a road full of pitfalls.

Epoch AI has reported that Huawei dispatches engineering teams to major customers such as Baidu and Tencent to help port CUDA training code and keep deployments running.

A frontier lab publishing its own production kernels partially answers that complaint, because it replaces vendor documentation with code that has survived contact with a model and can be optimized further. It is also, however, code that runs on one chip family, under one toolkit version, on firmware the rest of the world cannot download or even potentially access.

• An upcoming data center cited by previous Prime Minister Keir Starmer as part of the UK’s AI Strategy will no longer open as planned
• The Nscale site in Loughton, Essex, which counts Nvidia among its investors, had already raised eyebrows when it was revealed earlier this year to still be in operation as a scaffolding yard
• UK Power Networks has told Nscale that the electricity grid will not be able to produce enough power for the site until the early 2030s

A new AI data center – dubbed the UK’s largest AI supercomputer – is facing delays thanks to challenges supplying power to the facility. Built by Nscale, with backers including Nvidia, the neocloud installation was set to launch in 2027, but power issues have forced UK Power Networks to come clean over the data center’s intended viability.

Oddly, the site – which was highlighted by the UK’s recently replaced Prime Minister, Keir Starmer – was still operating as a scaffolding yard as recently as March 2026.

In order to operate, the Loughton data center in Essex, England, requires 90MW, around the same energy requirement as 315,000 homes. However, the UK’s National Grid has a backlog of over 300 data centers requiring power, with the total demand more than 60% greater than the UK’s current capacity.

The Nscale neocloud

Constructing the neocloud – a GPU-based AI cloud data center – is the first project for Nscale, a UK startup that is preparing for a £26 billion ($35 billion) public listing and has already raised around £2.55 billion ($3.36 billion) in pre-IPO funding.

However, while cited as a key example of the UK’s commitment to AI, the Nscale neocloud cannot operate without the 90MW supply.

National Grid, the company that owns networks in the UK, don’t comment on individual cases. Its representative told the Guardian: “We’re continuing to work closely with Neso, distribution network operators and customers to identify opportunities to accelerate connections where possible and support economic growth.”

But the power supply roadblocks materializing for the Loughton data center are not exclusive to that project.

UK power capacity concerns

Other data center plans around the UK are being held up by power supply problems. Regulator Ofgem revealed in June 2026 that 315 data centers were awaiting connection to the National Grid. They represent a demand of 73GW, exceeding the country’s entire capacity of 45GW by 62%.

The UK’s power generation is a diverse mix of gas, nuclear, wind, solar, and biomass. Some power comes from mainland Europe. Over the past 25 years, the UK has focused more on renewable energy resources, deprioritising nuclear, and using gas as the fall-back. However, the increasing demand for AI has revealed that strategy requires an urgent rethink.

Around 100 data centers are planning to build their own gas-fired power stations, according to reports earlier this year, which noted that 100GW of data center projects were awaiting connection to the UK’s power infrastructure.

• Those buying an RTX 5090 at Micro Center now have to fill out a declaration form
• This includes having a government-issued photo ID scanned by the retailer
• Some approve of the policy as it's an anti-scalping measure, but others view it as an annoyance or a pointless move

If you're buying an Nvidia RTX 5090, or a PC with the graphics card inside, over in the US, you may have to fill out an official form to declare that you don't intend to export and resell the GPU – or at least you will at Micro Center.

PC Gamer spotted the 'advanced computing product purchaser declaration' which a Redditor was forced to complete and sign – including a scan of their driving license for ID – when buying a PowerSpec AI90 workstation PC with an RTX 5090.

The Micro Center store was in Tustin, California, but someone else posting on the Reddit thread claims that the same form has been introduced at the Columbus, Ohio store.

Apparently this is a new policy that only started this week at Micro Center (but seemingly other retailers already follow this practice in the US, including Central Computers).

In case you hadn't guessed, this move is about trying to prevent scalpers from buying up the RTX 5090, or PCs containing it – which can be around the same price as the standalone GPU these days, and are sometimes the only way of getting this graphics card at all – in order to sell it on to China and turn a profit.

Analysis: a pain in the neck, or a valid anti-scalper tactic?

As some of the commenters on Reddit are quick to point out, this form isn't legally binding, and there are question marks over how any of this might be enforced. Somebody breaking the terms of the agreement is likely to just shrug their shoulders about the consequences, perhaps.

However, as PC Gamer notes, the use of the ID check is certainly a deterrent of sorts for GPU exporters. And what this is doing is preventing people who are making a short trip to the US buying an RTX 5090 and selling it on when they get back home for a hefty mark-up. Those people won't be able to provide a US address and government photo ID that can be verified as belonging to them, of course.

The fact remains that it's a bit of a pain for Micro Center's customers to have to go through this form filling and ID check, although that said, some of the folks on the above Reddit thread don't have a problem with the process given that it's an anti-scalping measure. Others are concerned about having to give a retail store personal information for an unnecessary reason as they see it, or they're just annoyed and think it's a waste of time. As one Redditor put it: "This is ridiculous."

Going by the Reddit thread, another measure Micro Center enforces is that there's only one purchase of an RTX 5090 GPU per customer full-stop, forever. Once you've bought one, you can never buy another, we're told.

• The lower-tier Shield TV has been officially discontinued
• The Shield TV Pro will stick around, but is now $100 more expensive
• The list price is now $299 (about £225 / AU$429 before tax)

One of our favorite streaming gadgets, the standard Nvidia Shield TV, is no more. We can't say we're surprised — we reported in the summer that it was unavailable via official channels and there were no indications of any restocking — but we are surprised that the Shield TV Pro is immediately getting a very big price hike now that the cheapest model is gone.

According to WCCFTech, the Pro is increasing in price by 50% from $199 to $299 (about £225 / AU$429). The reason, and the reason for discontinuing the base model, is AI: As Nvidia says in its announcement, "the cost of components, including memory, has increased substantially across the industry".

If only there were someone Nvidia could talk to about the AI industry driving component costs into orbit.

Nvidia Shield TV Pro: latest specifications and availability

Although it's now several years since its last hardware refresh, the Shield TV Pro is still a great TV streamer with a hardcore following. It's powered by the Tegra X1 system on a chip, which features a 256-core GPU teamed up with 3GB of RAM and 16GB of storage. It's ready for 4K HDR, supports Dolby Vision HDR, and its AI upscaler can deliver up to 4K at 60fps.

This is likely to be the last Shield TV Pro model for some time, because all the indications are that the component cost crisis is going to continue through 2027 at the very least, and Nvidia can already reach gamers on multiple platforms with its GeForce Now streaming service, which is available on all the major desktop and mobile platforms as well as smart TVs from the likes of LG and Samsung.

Nvidia also recently revealed its RTX Spark "superchip" for laptops and handhelds, featuring an ARM processor with an integrated RTX GPU. Nvidia says it's "a new beginning" for gaming laptops and small form factor PCs, and with big-name studio support already it looks like it could be a big deal for mobile gaming in the future — right now, it seems to be much more of a pro component.

But still, that makes the Shield Pro an increasingly niche part of Nvidia's world domination plan, so I guess we can be happy it's stick around at all for now.

When Nvidia last updated the Shield Pro we said it remained our favorite TV streamer — but that was at a price of $199 / £179 / AU$379. With a 50% price hike added and a new Apple TV model expected imminently, it might be wise to wait before you decide what your next TV streamer should be.

• Borderless Gaming is a tool that's sold on Steam
• It just got an add-on to easily fudge DLSS 5 to work with various PC games
• Reviews of the tool have turned rather colorful, as those who still hate DLSS 5 and all it stands for are chipping in

You can't have missed all the chatter about experimenting with Nvidia's DLSS 5 in all sorts of different PC games, and if you've felt the urge to join in but have been too intimidated by the idea of fudging the tech to work, a solution is now at hand.

VideoCardz noticed that Borderless Gaming, a tool that's sold via Steam – it's used to facilitate gaming in a borderless window, as the name indicates – now has DLSS 5 support. Or, as the developer put it: "DLSS 5 Neural Rendering for Everyone."

The dev notes that: "DLSS 5 Neural Rendering is now available in Borderless Gaming – without replacing game DLLs, modifying game files, or needing a game-specific mod. Install the effect, select the preset, and apply it to your game."

Borderless Gaming applies DLSS 5 directly through its own BGFX pipeline, the developer explains, rather than forwarding requests to Nvidia's DLLs.

In short, this is DLSS 5 being fudged in about as no-fuss a manner as can be, save for the fact that you have to buy and install the app, of course. This implementation shouldn't be confused with native DLSS 5 support, because it's simply a workaround to bring the neural rendering technique into play (with variable results as you might expect).

Note that the Borderless Gaming app doesn't actually come with this built-in, but after installing the tool itself, you need to also download the appropriate effect (DLSS5-NR v0.2.1 currently) from GitHub here (installation instructions are provided at that link).

Analysis: catching some of the DLSS 5 hate

According to the developer, this implementation can work on a wide range of GPUs – not just the officially supported RTX 5000 models – and an RTX 3090 can hit around 30fps typically with DLSS 5 turned on. Obviously the frame rate will vary massively depending on the game and graphics settings, but the gist is that you can get something playable on the RTX 3090.

It's a neat enough solution by the looks of it, although as ever, if you're curious, I have to issue my usual warning about third-party apps here: download and use them at your own risk.

Borderless Gaming is an app that's been around for a long time, but the Steam reviews have turned distinctly colorful of late – with some of that color revolving around this latest move with DLSS 5. As one reviewer puts it: "Added DLSS 5 for no reason but to make games look worse... Slop." (Hence the above clarification that DLSS 5 isn't actually added to the app itself at all).

Nvidia's DLSS 5 was controversial when it was first announced, although the reception to its neural rendering trickery has warmed up considerably since it went live in a game (NBA 2K27). There are still haters out there, though, as the Steam reviews for Borderless Gaming illustrate.

It's also a fair question whether Nvidia might take action against projects like this, which are going further in terms of tweaking and playing about with DLSS 5 tech. It's likely that for now, with DLSS 5 getting a greater level of positivity from many of the gaming experimenters out there, Team Green might let it slide – at least for the time being.

• 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.

• 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, 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.

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.