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

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

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

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

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

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

Panic buying still isn't the best solution

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

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

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

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

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

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

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

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

An attack that matters much more than its predecessors

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

What is AGI?

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

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

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

Does AGI matter?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Analysis: tread carefully with unofficial mods

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

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

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

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

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

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

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

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

Nvidia confirms Hugging Face acquisition

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

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

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

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

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

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

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

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

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

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

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

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

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

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

2. Continual optimization should improve performance a good deal

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

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

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

3. Experimental results with DLSS 5 are pretty incredible

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

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

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

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

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

4. Tuning options are key for DLSS 5

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Handheld manufacturers could use RTX Spark

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

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

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

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

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

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

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

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

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

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

What makes NVHBM tick?

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

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

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

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

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

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

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

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

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

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

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

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

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

Keeping their cards close to their chest

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

A portable for-local AI offering that holds its own

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

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

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

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

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

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

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

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

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

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

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

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

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

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

AI above everything else

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

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

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

Mini heartbreak

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

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

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

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

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

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

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

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

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

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

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

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

A job cannibalized by changing priorities at the helm?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Performance is a different ball game too

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

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

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

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

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

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

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

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

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

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

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

Apocalyptic decoy

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

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

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

Profit isn't a good yardstick

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

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

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

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

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

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

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

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

Monopolization of our modern lives

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

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

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

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

How does RTX Spark perform inside of laptops for gaming?

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

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

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

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

How does RTX Spark rival other SoCs for gaming?

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

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

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

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

Intergrated hardware you cannot afford

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

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

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

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

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

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

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

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

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

A bigger push into AI infrastructure

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

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

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

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

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

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

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

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

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

Executives point to rising demand

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

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

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

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

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

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

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

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

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

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

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

Nvidia wants its own PAC

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Nuts and bolts

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

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

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

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

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

Change in tone

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

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

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

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

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

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

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

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

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

Skeptics remain

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

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

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

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

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

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

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

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

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

You'll still be secure with an Nvidia GPU

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

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

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

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

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

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

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

Could there yet be a reprieve from Nvidia?

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

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

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

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

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

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

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

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

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

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

Nvidia and Hugging Face

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

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

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

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

Neither Nvidia nor Hugging Face responded to requests for comment.

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

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

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

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

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

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

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

Analysis: Steam Machine dream team

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Analysis: is anything immune to the component crisis?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Foreign parts recovered from missiles and drones

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

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

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

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

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

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

Sanctions evasion continues despite restrictions

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Via United24Media

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Analysis: default paranoia

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Analysis: pricey times

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

• Nvidia reprotedly weighing up $250 billion in financing for OpenAI's giant Ohio AI data center
• Total project costs could surpass $500 billion after infrastructure and hardware investments
• OpenAI seeks greater control over computing infrastructure through dedicated Ohio facilities

Nvidia is reportedly negotiating financing guarantees worth roughly $250 billion for a massive OpenAI data center project.

Reporting from The Wall Street Journal claims the financing would help OpenAI secure a lease for the proposed 10-gigawatt facility in southern Ohio.

The site is being developed by SB Energy, a subsidiary of SoftBank, under a separate infrastructure arrangement.

Financing plan could support one of the world's biggest AI facilities

The guarantee would cover the data center lease and debt financing, though it would exclude the Nvidia chips housed inside the facility.

Nvidia is separately discussing financing arrangements for OpenAI's chip purchases, which could total up to $350 billion.

The combined costs for the entire project, including chips, are expected to exceed $500 billion once construction concludes.

The first phase alone is expected to finish by 2028, delivering around 800 megawatts of the planned 10 gigawatts.

The project's power supply will be controlled by the U.S. government and funded separately through a trade arrangement with Japan. That arrangement ties into Tokyo's pledge to invest $33 billion in a natural gas plant.

US Commerce Secretary Howard Lutnick reportedly plays a role in deciding which companies gain access to that power.

OpenAI's financial position complicates the infrastructure push

OpenAI is currently valued at $852 billion, ranking among the most valuable private companies globally despite remaining unprofitable.

According to shareholder documents, the company posted a net loss of roughly $21.3 billion in the first quarter of 2026 alone.

Stripping out a non-cash charge tied to revaluing investor warrants leaves an operating loss near $9.3 billion for that quarter.

Internal projections reportedly show OpenAI expects to lose around $14 billion across 2026, with cumulative losses reaching $44 billion by 2028.

That lack of profitability raises questions about how the company intends to fund its numerous multi-billion-dollar infrastructure commitments going forward.

Nvidia's backing would reportedly help reassure lenders that the financing vehicles underpinning the project carry sufficient institutional support.

For OpenAI, securing the deal would mark a shift toward owning infrastructure rather than renting capacity from Microsoft, Amazon and Oracle.

On Nvidia’s part, the deal would help guarantee sustained chip demand well into the coming years.

OpenAI has reportedly held talks for weeks about leasing the Ohio site directly, while Anthropic, Microsoft and Google have also shown interest.

The proposed facility would rank among the largest AI data center developments planned anywhere in the world, but its financing structure combines private investment, government-backed infrastructure and long-term computing demand across multiple funding sources.

At the moment, commercial agreements between the companies involved have not been finalized.

• AMD and Cerebras will split inference across two machines, with Helios racks handling prompt processing and the Wafer-Scale Engine generating tokens, available through Cerebras Cloud in H2 2026
• Nvidia is also doing something similar by licensing AI chip startup Groq's SRAM decode technology for $20 billion
• The move sees AMD and Cerebras claim 5x higher tokens per watt versus a standalone Cerebras WSE configuration

AMD and Cerebras Systems have announced a technical partnership which pairs the former's Helios rackscale system with the latter's Wafer-Scale Engine in what both companies call a disaggregated inference solution.

The move has enabled a combined AMD Helios and Cerebras WSE configuration to deliver up to five times the tokens per second per watt (TPS/W) in internal testing by both chip designers.

The move aims to address a Cerebras WSE efficiency challenge by offloading prompt processing to AMD's rackscale offering.

An efficiency gains-centric play?

Both AMD and Cerebras Systems are painting the news as a win, and it very well might be, given the latter's efficiency gains in play and the former's ability to get access to SRAM decode technology without spending the $20 billion Nvidia shelled out at the end of last year for a non-exclusive deal.

It must, however, be noted that the efficiency claims of 5 tokens per second per watt are compared against an existing Cerebras WSE (Wafer-Scale Engine) as the baseline, while running the open-source Kimi 2.6 1T model, making them impressive, but without a direct comparison to figures for an Nvidia rackscale offering, one that lacks context, especially when efficiency is the metric.

The idea itself is sound and well established in the industry, with WSE known to struggle with the 'prefill' part of the equation while handling the 'decode' segment relatively well, essentially substituting AMD's hardware where Cerebras' equipment falls short.

The choice of Kimi 2.6, however, deserves a second look. Moonshot AI's model, released on 20 April 2026, is a mixture-of-experts design with one trillion total parameters but only 32 billion active per token, and it ships natively in INT4. At INT4, the full weight set runs to roughly 500 GB. A single Cerebras wafer holds 44 GB. Even before KV cache, a Cerebras-only deployment needs somewhere north of a dozen wafers just to hold the model, while one Helios rack could hold it around sixty times over.

That asymmetry means the five-times figure is measured on a model that is close to the least favorable for a WSE-only configuration. A dense model small enough to sit resident on a handful of wafers could flatter Cerebras considerably more. None of this makes the number wrong, but it does make the case for additional testing to demonstrate both its strengths and weaknesses for different models.

A partnership without numbers, for now

More importantly, the absence of any financial information might very well be a future story, especially at a time when there are increasing concerns about 'circular financing' in an industry where Nvidia's recent move to backstop OpenAI's data center purchases was seen as a net negative by Wall St, which is already concerned about AI spend and the sustainability of such transactions.

AMD has also, in the past (and more recently with Anthropic), linked purchases of its own hardware to investments or stakes it would take in AI companies, moves that the market welcomed earlier but might view with a bit more hostility lately.

The announcement comes at a time when Cerebras might need it more than AMD: Cerebras listed on Nasdaq in May, priced at $185, opened at $350, and closed its first day at $311.07 before falling back to around $227 by late June 2026.

AMD stock, on the other hand, is up 121.48% year-to-date (YTD) as investors continue to bet heavily on its new Instinct AI processors, and the Cerebras partnership allows it to further consolidate its gains, as this might be seen as another vote of confidence in its current direction by one of its prospective customers.

• Nvidia and Microsoft lead the list of founder members of the Open Secure AI Alliance
• AI, financial services, tech companies, and other organizations with developing AI services have joined the alliance
• Notable exceptions include OpenAI, Anthropic, and Google

Nvidia has confirmed its membership of the new Open Secure AI Alliance, a collective of industry-leading tech, financial, and AI businesses working in collaboration to ensure openness in the AI industry. Curiously, however, while Microsoft and OpenClaw are among the founding members, Google, OpenAI, and Anthropic are not.

Inspired by the open source movement and the Linux Foundation-led Akrites AI cyber threat response initiative, the Open Secure AI Alliance aims to use open technologies to disclose and remediate vulnerabilities.

Citing the recent Hugging Face incident, where closed AI tools prevented full investigation of the event, the Open Secure AI Alliance founding announcement underlines the importance of open models, highlighting how cybersecurity is one of the main beneficiaries of open source software.

A Hugging Face response?

A sizeable group of recognizable companies are involved with the Open Secure AI Alliance, including OpenClaw, Palantir, Palo Alto Networks, Microsoft, Crowdstrike, IBM, Linux Foundation and Hugging Face.

Notable from this list are Google and Palantir, and of course OpenAI, which was responsible for the Hugging Face attack. Given that this event occurred during a cybersecurity benchmark test and OpenAI’s model conducted a wholly automated attack on Hugging Face’s servers, it serves as an important demonstration of what is at risk from closed source models in the AI world.

“When defenders cannot inspect, adapt and run advanced AI on their own infrastructure, their ability to respond is constrained at exactly the moment speed matters most,” states the Open Secure AI Alliance, which emphasizes its mission: “to ensure defenders everywhere have open, frontier tools they can trust and control.”

OpenAI’s lack of openness in dealing with the incident, and Hugging Face’s initial difficulties in analyzing the intrusion due to closed source models, really underline the importance of this new alliance.

Open vs. closed

The argument concerning open and closed software models isn’t likely to end any time soon, but the Open Secure AI Alliance certainly makes a strong point for open models. Yes, they can be a risk that can be modified or misused, but those risks exist within closed systems.

So, the alliance’s view is to give defenders the tools to deal with threats, rather than blocking those tools. Decades of cybersecurity research have shown, after all, that the best option is collaboration and testing – or, as the Open Secure AI Alliance states “the safer path is the one that gives more defenders the ability to test, verify and strengthen the systems on which society relies.”

Given OpenAI’s role in the Hugging Face incident, its absence makes sense – but what about Google and Anthropic? At a time when corporate collaboration is driving development of new technologies within tight standards (for example, the Connectivity Standards Alliance’s work with Matter and Aliro), it seems as though the Open Secure AI Alliance has the right idea.

• AMD's Helios beats Vera Rubin NVL72 on memory at rack level, with 31TB of HBM4 against roughly 20.7TB
• AMD also claims it beats the competition by 15% on FP4 compute on a 'per-GPU' basis even as Nvidia comes out ahead on published 'per-rack' figures
• AMD claims Helios also offers 30% more tokens per dollar spent versus the competition

AMD has launched its Helios rackscale offering, outlining five comparisons in which it claims wins over what it calls "the leading competitive solution."

While the company skipped naming Nvidia, the market leader's Vera Rubin-based NVL72 rack-scale solution is the only real competitor to Helios and the one it continues to compare itself against.

While its memory claims hold, its GPU FP4 claim might fall short when comparing rack to rack, and many of its calculations are based on peak performance rather than Nvidia's published numbers, making Helios an interesting "win" but one that does encourage potential adopters to look more closely.

A numbers game that continues to grow complex even as AMD ekes out some wins

While AMD claims a 15% win versus Nvidia's Rubin on a per-GPU basis for FP4 compute, its 72-GPU rack-scale solution falls short of Nvidia's published rack-level numbers: 2.9 exaflops versus 3.6. AMD does not specify whether it is counting Nvidia's individual dies or its two-die packages, and the distinction matters: against dies the gap runs in AMD's favor by far more than 15%, while against packages AMD trails.

The two figures also use different formats, AMD's MXFP4 against Nvidia's NVFP4, so they are not measuring identical arithmetic.

This might, however, be indicative of a very real situation that hampers both vendors' headline numbers: real-world FP4 workloads rarely reach hardware peak ratings due to memory movement constraints, scheduling overheads, and software kernel efficiency. AMD conceded as much at its own event, putting measured FP4 throughput at roughly half its peak rating.

Nvidia may sustain more of its peak thanks to its custom Vera CPU, a mature NVLink 6 software stack and a larger pool of what it calls fast memory, 75 TB per rack once 54 TB of LPDDR5X is counted alongside 20.7 TB of HBM4. AMD's 31 TB is all HBM, which is better suited to models that must be held entirely in high-bandwidth memory, and Helios offers higher capacity and bandwidth per accelerator at 432 GB and 23.3 TB/s.

On raw scale-up fabric, the two are level, both delivering 3.6 TB/s per accelerator and 260 TB/s per rack.

Despite this, Helios is an exceptionally strong product on paper, and it may be the first time AMD has produced a credible rack-scale answer to Nvidia since the AI race began. Seventy-two MI455X accelerators, 18 EPYC Venice CPUs, 31 TB of HBM4, UALink over Ethernet inside the rack and Ultra Ethernet out, on an OCP Open Rack Wide chassis with merchant Broadcom switch silicon, is a serious response to a company that had a two-year head start on the form factor.

More importantly, its fabric specifications are publicly available, enabling hyperscalers to build customized variants that meet their requirements. Nvidia's platform offers no equivalent latitude.

AMD frames openness as the platform's central advantage, with Vamsi Boppana, senior vice president of AI at AMD, saying that Helios "brings together leadership compute, high-performance networking and open software in a unified rackscale platform."

The more important question for AMD, however, might be memory supply. Nvidia has had Vera Rubin in full production since Q1 with partner availability this half, while AMD's first Helios deployments are not due until Q4. AMD may be further gated by HBM4 supply, much of which is reported to be already committed to hyperscalers, which could keep its deployment volumes well below Nvidia's this year.

• Off-planet mobility company Lunar Outpost with use the Nvidia Jetson platform and Nvidia CUDA-X libraries for future lunar missions
• Nvidia Jetson modules will manage the command and control of the Lunar Voyage 2 rover's LiDAR system, set for launch later in 2026
• This follows Lunar Outpost’s successful bid to design and deliver the Pegasus Lunar Terrain Vehicle for NASA

Nvidia is collaborating with space economy infrastructure company Lunar Outpost to deliver edge AI for future missions to the Moon and beyond. Powering the first generation of AI space computing, Nvidia’s Jetson platform and Nvidia CUDA-X libraries are expected to underpin the Lunar Outpost’s growing mission manifest.

Everything from processing to mapping and communications will be handled by Nvidia’s edge AI provision, which is seen as the solution to Lunar Outpost’s contributions to the NASA Artemis program of modern space exploration.

Later in 2026, the Reiner Gamma exploration – the “lunar swirl” geographical feature on the Moon’s surface – will launch, with the Lunar Voyage 2 expected to be the first Lunar Outpost hardware to be deployed with Nvidia Jetson tech.

Nvidia in space

Lunar Outpost is no stranger to space exploration, having assisted NASA and MIT on the MOXIE project, generating oxygen on the surface of Mars on a project running 2021 to 2023. Its collaboration continues with the Reiner Gamma exploration, working with General Motors and Goodyear on its lunar rover.

“Lunar missions require intelligent systems that can process data and make decisions in real time," noted Chen Su, Head of edge AI product marketing at NVIDIA.

"Lunar Outpost is using NVIDIA Jetson and CUDA-X libraries to give its rovers the onboard AI computing needed for terrain mapping, autonomous navigation, mission data processing and scientific discovery.”

Human-robot exploration

Set for dispatch to the lunar surface at some point in the second half of 2026 via SpaceX Falcon 9, Lunar Outpost’s Lunar Voyage 2 will be overseen from Earth, while the Nvidia AI technology handles advanced autonomy and communications, as well as lunar mapping and onboard data processing.

Justin Cyrus, Founder and CEO of Lunar Outpost, said “NVIDIA has set the standard for advanced AI computing, and we're proud to work alongside them as we bring increasingly intelligent capabilities to the lunar surface and build towards a Moon Base.”

Partnering with Nvidia for edge AI support appears to be the first stage of some pioneering missions. The future of Lunar Outpost’s off-world activities includes Lunar Voyage 3 and Lunar Voyage 5 missions, where the first human-robot exploration of another world is planned. For this, the Nvidia Space-1 Vera Rubin Module will be deployed, managing real-time data, mapping, and real-time video streaming from the Moon to Lunar Outpost’s mission control.

Lunar Outpost is contracted to a further nine lunar and cislunar (the area between the Earth and Moon) missions before 2030, with Nvidia Jetson and the CUDA-X libraries expected to play a part in all 10.

• Several AI companies in the US have published an open letter
• The letter is campaigning for unrestricted open-weight AI
• Meanwhile the US government accuses Moonshot AI of IP theft

Any hopes AI tech reporters may have had about easing quietly into the weekend have been dashed by some of the biggest names in the business sharing an open letter in support of open-weight AI models — while the White House has accused China of 'large scale' theft from US companies in building its open-weight AI.

Nvidia CEO Jensen Huang got the ball rolling with his first-ever tweet, linking to a statement championing open-weight AI models, those models that can be downloaded, viewed, and edited by anyone. Models like this are essential for making AI "more accessible, adaptable, and widely available" the open letter says.

It's co-signed by a slew of well-known tech and AI companies, including Meta, Microsoft, Perplexity, and IBM. While ChatGPT developer OpenAI — which has had more of a closed-model focus, to date — didn't appear on the original post from Huang, it has now also backed the sentiments.

Essentially, these companies are campaigning for openness in AI to drive innovation and access, while also arguing against 'premature restrictions' by governments. It's no coincidence that the US government led by President Trump has recently tried to take a more prominent role in evaluating AI models and deciding when they should be released.

It's also notable that Anthropic and Google are missing from the list of backers to this open letter. We don't have any official comment yet from these companies, so it's hard to judge the reasoning — Google regularly releases open AI models, after all — but it could be something to do with recent accusations aimed at Moonshot AI in China.

Open and closed

Part of the background to this open letter is that Chinese company Moonshot AI stands accused of distilling Claude's (closed) Fable 5 model in order to build its own (open) Kimi K3. In simple terms, distilling works by asking millions of questions of an AI model, and using the responses as training data.

It's something Moonshot AI has denied, while hyping up the potential of the open-weighted, freely downloadable Kimi K3 AI model, which is being released on Monday — another factor in this open letter coming out now. The US government is calling it IP theft, and has issued some heavy-handed responses.

"We support open-source AI and the innovation it unlocks," US Treasury Secretary Scott Bessent posted on X. "But open source is not open season on American IP. When PRC firms conduct covert, industrial-scale distillation attacks that cross the line into IP theft, sanctions and Entity List designations will be on the table."

What Nvidia, Meta, Microsoft, and the other open letter co-signers seem to be doing is trying to get ahead of any potential clampdown by the US government on open-weighted models. While everyone agrees that these AI models are beneficial, it's not clear what the next regulatory steps might be.

However, the open letter is vague and general enough that you can read between the lines in a variety of different ways. It may be intended to allude to the Moonshot AI case, or it may not. What's clear is that we're coming to a crunch point for AI regulation and innovation across the globe, and everyone has a perspective.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Consumer struggles

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

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

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

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

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

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

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

• Nvidia's Rubin-generation AI servers run coolant that is often hotter than a hot tub as it enters a closed loop at up to 45°C and leaves at around 55°C, with no performance penalty in tow
• The principle leveraged here is that of a temperature gradient with server chips still running hotter than the coolant, allowing heat transfer to occur seamlessly
• The approach allows Nvidia to roughly conserve 2.6 million gallons per megawatt annually to near zero and could save a 50MW site over $4 million a year in cost

Nvidia's Rubin generation is the first of its kind in multiple ways, but the one that sticks out possibly is the fact that it is 100% liquid cooled, implementing it as a core design feature at a platform level with every single chip and networking component covered by a closed loop.

The reference design by Nvidia aims to allow for AI factories that effectively consume zero water by implementing a closed loop circuit that does not leverage evaporative water cooling 99% of the time.

The approach goes a step further by potentially eliminating industrial chiller plants from the equation by using liquid cooling where the temperature is as much as 45 degrees Celsius for coolant entering the system and approximately 55 degrees Celsius while exiting it.

A numbers game for a data center that crunches them at an industrial level

Nvidia's approach is not the first of its kind in terms of thermal design or implementation; however, IBM beat it by 16 years, using 60-degree coolant to cool its prototype supercomputer at ETH Zurich.

The premise was the same: a much lower carbon footprint, significantly lower power consumption, and a loop that is filled once and left alone. What Nvidia adds is scale — its Vera Rubin-based DSX design extends the approach across an entire facility rather than a single machine.

"The NVIDIA DSX reference design for AI factories has zero water consumption — we have eliminated massive amounts of power usage and pretty much all water usage," said Ali Heydari, Nvidia's director of data center cooling and infrastructure.

The coolant is a 3:1 water-to-propylene-glycol mix, and it pulls heat away from chips running far hotter than the coolant itself with ease.

The savings are not only water-based: industrial chiller plants cut their energy costs by roughly 4% for every degree they raise their operating temperature, and Nvidia estimates that a 50-megawatt hyperscale facility could save over $4 million annually in cooling-related energy and water costs by adopting its design.

With cooling historically accounting for as much as 40% of a data center's total power bill, Nvidia's approach is ambitious, but it is one that should pay for itself quickly — eliminating server fans altogether while allowing chillers to run only where necessary, and at considerably lower power than a loop demanding colder coolant.

It must be noted that DSX is a reference design: a set of best practices for building an AI factory, not a census of what the industry has actually built, where many designs still consume very large amounts of water even as AI data centers continue to pop up in drought-affected regions of the US.

Nvidia might seem to be championing a "greener" AI data center for the future, at least in its own designs, but that is only part of the story — and the part it leaves out is a little less flattering. Nvidia did not adopt hot-water cooling because it is elegant, or green, or clever, though it is all three. It adopted it because its accelerators became so power-dense that moving air across them was no longer physically viable, and once you are committed to liquid cooling, running it hot is simply the efficient way to do it.

The sustainability win is real, and it is a consequence of a thermal problem Nvidia created for itself by building the most power-dense chips in the industry. Hot water isn't the trick. It's the bill coming due, paid in the most efficient currency available.

Intel is one of the most ubiquitous companies in the world, let alone the technology industry, with its technologies powering many of the computers most people have used for generations. Central to its success was a philosophy from its former CEO Andy Grove, who was a part of the company from day one and ran it from 1987 to 1998.

Inside Intel

Andy Grove was setting out the foundational theme for his management book 'Only the Paranoid Survive', which he wrote toward the latter years as CEO of Intel.

Grove, who is widely recognised as one of the most successful tech executives of the late 20th century, advocated strongly for putting paranoia at the heart of business. Worrying about various risks that can affect your business — from new competitors to macroeconomic shocks — means you can prepare for them.

But what's worse, as he described it, are 'strategic inflection points' that signal a massive shift in fundamentals that could either lead to staggering growth or a grave decline by an order of magnitude. It's fair to say, in the years since he left Intel, that the company has endured its ups and downs, putting it mildly.

Sliding doors

Although Intel enjoyed incredible success in the 20th century and into the early 2000s, the company stuttered in recent years with rival chipmakers including Nvidia and AMD gaining ground and eating into its market share.

Ironically, from the outside, it appears that the company had succumbed to the sort of complacency that its former CEO had been warning against.

That said, the company is in the midst of a remarkable comeback under the leadership of new CEO Lip-Bu Tan, who has overseen a dramatic surge in the company's stock price that underlines its improved overall outlook as well as new ventures to support the AI buildout.

• Nvidia donates one of its most advanced AI supercomputers to US military’s flagship graduate university
• The supercomputer will be used across a range of applications, from simulations and digital twins, to weather prediction and cybersecurity
• "AI ‌will ⁠be a backbone of America’s defense," Nvidia CEO Jensen Huang said at the commissioning of the supercomputer

Nvidia has donated a DGX GB300 AI supercomputer to the Naval Postgraduate School in Monterey, California, to support students, researchers and faculty in weather prediction, cybersecurity, disaster resilience and response planning.

The supercomputer was commissioned with Nvidia CEO Jensen Huang in attendance. “Our nation depends on our men and women who fight on the front lines,” Huang said.

“Nothing is more valuable to you than information and insight, and information and insight in a timely way, and to understand its impact and consequence. I can’t imagine anything more important.”

AI supercomputer to be used for training, oceanic modelling, and digital twins

The Nvidia DGX GB300 is one of Nvidia’s most powerful AI servers, and houses 72 Nvidia Blackwell Ultra GPUs, alongside 36 Nvidia Grace CPUs, that provide 20 TB in GPU Memory at a bandwidth speed of up to 576 TB/s.

This will provide the US military’s flagship graduate university with an enhanced ability to train new AI leaders, and enables more technical applications across in-house foundational models, run simulations, and use digital twins to train students from every branch of the military on real-world navigation and decision-making.

“Leadership is about being in service of something else. You’re in service of your team. You’re in service of your country. You’re in service of your mission,” Huang said at the commissioning. “And all of this technology is just tools to help you.”

Admiral Samuel Paparo, commander of the US Pacific Command, said, “As we modernize our technology, we must also modernize how we educate our leaders. Access to advanced computing capability means NPS students and faculty understand the opportunities and responsibilities that come with these technologies.”

The AI supercomputer will also allow students to better understand sea conditions and predict atmospheric changes, while also equipping students for an increasingly AI-enhanced military.

The US and China are in heavy competition to deploy better and better frontier AI models, with China quickly catching up despite widespread sanctions on the sale of AI chips to the country. Both nations are integrating AI systems into the military, police, and government agencies.

“Many of you will command in AI-enabled environments. Information will move at lightning speeds, compressing response times,” Paparo said. “Our advantage will come from leaders, like you, who can use the technology to see, understand, decide and act faster while exercising the judgment, experience and leadership that machines cannot provide. That education is happening here at NPS.”

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

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

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

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

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

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

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

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

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

Analysis: a step forward, but the doubters remain

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

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

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

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

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

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

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

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

• 13,750 Vera CPUs and 27,500 Rubin GPUs will drive compute at the Nvidia Vera Rubin AI factory
• The project will be the world’s first national AI infrastructure for physical AI
• By partnering with Noetra Corp, Nvidia is providing hardware to drive Japan’s FRONTia Project

Japan’s Ministry of Economy, Trade and Industry (METI) is supporting a partnership between Noetra Corp and Nvidia to provide the foundation for the its national artificial intelligence initiative, the FRONTia Project.

Noetra Corp – which comprises NEC, Sony, Honda, SoftBank Corp, and has backing from 44 other companies and organizations – won a tender on June 30 2026 to run FRONTia until 2030, with as much as ¥1 trillion (around $6.1 billion) expected to be invested in the project over the five years.

Japan sees FRONTia as providing the heart of its sovereign AI ecosystem for industry, providing compute power for the development of advanced multimodal foundation models that can be used to drive physical AI. Robotics across industry and healthcare can potentially benefit from this, along with the development of superior AI agents and digital twins.

What is the FRONTia Project?

Aiming to foster collaboration between global technology leaders and Japan’s manufacturing expertise, and leveraging real-world industrial data, the FRONTia Project (referring to the “Development of Multimodal Foundation Models with a View to AI Robotics and Physical AI” project) is described as “the core of the country’s physical AI ecosystem,” by Ryosei Akazawa, Japan’s Minister of Economy, Trade and Industry.

“By fostering collaboration between Japan and leading global innovators — including NVIDIA — and leveraging Japan’s strengths, such as its onsite expertise and manufacturing technology infrastructure, we will build highly reliable multimodal foundation models and contribute to solving global social challenges.”

When constructed, the Nvidia Vera Rubin AI facility will provide Noetra’s multimodal foundation models to domestic model developers and enterprises. Nvidia’s own AI-related software models and libraries will also be available.

There has been some criticism of the FRONTia Project, however. Concerned parties have highlighted the lack of financial transparency from Noetra and the Japanese government, as well as the presence of Nvidia as partners, with the US company seen as a strategic rival. Meanwhile the massive energy costs associated with AI installations is once again considered an unnecessary problem to create.

“Japan invented modern manufacturing. Now, it is building the AI factories that will power the next industrial revolution,” said Jensen Huang, founder and CEO of Nvidia.

“NVIDIA is honored to partner with Japan and its industrial leaders to build the AI infrastructure that will power the country’s industries, its economy and a new generation of innovation.”

Is the world ready for physical AI?

AI’s impact on creative industries has been widely discussed, but its potential for beyond “generative” processes – such as in the physical space – have avoided scrutiny. To date, physical AI (the integration of AI with physical hardware such as robots, vehicles, or industrial machines) hasn’t been able to scale as quickly as generative AIs with ever-deepening LLMs.

Predicting the next word is a lot easier than, say, predicting shifting terrain for an autonomous machine, and without the delicate motor skills of a human, no process can be completely automated.

This is a problem for physical AI, and investing money in developing solutions such as the NVIDIA Vera Rubin AI factory will surely have a direct impact on the workforce. A successful physical AI project means more automation from robots, and fewer physical jobs.

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

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

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

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

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

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

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

Analysis: Super stumble?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

For as long as there's been software there has been tension between open source and closed source. The creator of the Linux kernel, software engineer Linus Torvalds, has long been an outspoken advocate for open source environments – while putting the boot into closed source corporate entities who have tried to exploit the ecosystem.

Open for business

During a Q&A at Aalto University, an attendee complained that the Nvidia Optimus chip – a processor that switches between integrated graphics and dedicated graphics – was no longer being supported on Linux.

That meant that this expensive chip fitted into her Linux laptop was basically useless unless developers worked on a fix by reverse-engineering the process. Torvalds answered by giving Nvidia both barrels – complaining that the company was profiting from the Linux Foundation ecosystem through products like the Tegra chips for Android devices.

At the time, he complained that competitors like Intel and AMD were happy to co-operate with Linux to build native drivers whereas Nvidia would treat its proprietary code as classified material. This exchange culminated with an angry Torvalds flipping Nvidia off to camera.

Turning the tables

The situation today is almost entirely different from that we found nearly 15 years ago. Nvidia has embraced open source with open arms, and has also fully transitioned to open source Linux GPU kernel modules as being the default options as of 2022, pushed under a dual GPL/MIT licence.

With the AI buildout also fully underway, Nvidia has crafted the Linux-based DGX OS for its personal mini supercomputing DGX Spark products. This Ubuntu-based system is designed to work with the entire Nvidia software stack – with this AI layer (including software like CUDA, cuDNN and NCCL) being closed.

Although the battle between open and closed source remains active, Nvidia's reversal signalled that the future of enterprise and AI-centric computing was to be found in open source environments.

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

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

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

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

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

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

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

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

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

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

• The Nvidia Shield TV (2019) is out of stock pretty much everywhere
• Nvidia says it's down to consumer demand for the streaming device
• It's not yet clear if we'll ever get more stock or a replacement gadget

While there hasn't been any new Nvidia Shield TV hardware for seven years now, the streaming devices continue to be popular — but it looks as though it could finally be the end of the road for the cheapest base model in the series.

As spotted by Android Authority, this $149 / £129 / AU$289.95 model is now out of stock via Nvidia's official channels (there does appear to be some stock at Amazon Australia at the time of writing), and Nvidia has stopped short of promising that those stock levels will be replenished anytime soon.

In a statement to Android Authority, Nvidia said the Shield TV was out of stock "due to demand", and that there was nothing to say "regarding future availability at this time" — so make of that what you will.

Nvidia also took the opportunity to highlight that all Shield TV models continue to get software updates and continue to be supported by Nvidia, even though it's now more than a decade since the first of these gadgets went on sale.

More to come?

Both the base model Shield TV and the more premium Shield TV Pro were given a refresh in 2019, and Nvidia says the Pro version is still on sale. Whether or not Nvidia is planning to let the stock run down on that model too remains to be seen.

I've had several Shield TVs down the years, and have always been impressed with the hardware and the software on offer. They support a wide variety of apps and games, come with local storage, and can be customized in a variety of ways too.

Even something like the recently launched Google TV Streamer only just about catches up to everything that's possible with the Shield TV boxes — and that's saying something considering Nvidia's devices made their debut in 2015.

Nvidia execs are on the record as saying they're open to the idea of new Shield TV hardware in the future, so hopefully this clearing out of stock will be followed by a brand new model in the not too distant future.

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

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

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

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

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

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

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

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

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

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