It’s been a heck of a time lately for PC gamers willing to get their hands dirty with mods. Hot on the heels of the discovery of the DLSS 5 DLL in a prerelease version of NBA 2K27, modder dashdogy found a way to bring Multi Frame Generation, one of the crown jewels of GeForce RTX 50-series graphics cards, to RTX 40-series (and earlier) products.

As already elevated graphics card prices seem set to continue rising, and hardware upgrades get further and further out of reach of the average PC gamer, more and more folks are going to want to hold on to the RTX 40-series hardware they have for as long as they can, especially if smoothness-boosting features like MFG are just a few clicks away on those older cards.

So we had to see MFG working on Ada for ourselves—assuming it works at all. We grabbed the mod files and got to playing with them in Cyberpunk 2077, since it’s likely in many TH readers’ Steam libraries already and has a healthy modding community. You can find the latest instructions for enabling MFG on Ada through dashdogy’s GitHub page.

Before spending a ton of time testing, we verified that the mod works at all. While spinning the camera at a high, constant speed, we could indeed see that increasing MFG multipliers beyond the officially supported 2X factor on Ada cards does greatly increase perceived smoothness or fluidity of motion in Cyberpunk 2077, as you would expect.

Another tell is that the same visual artifacts are visible in certain regions of the screen on both RTX 40-series and RTX 50-series graphics cards as you add more generated frames. MFG 4X and above, especially, tend to add some visual “junk” at the bottom of the frame that appears regardless of the game, and we could see that artifacting on Ada.

At least in Cyberpunk 2077, then, we’re confident that MFG is really doing its thing on RTX 40-series cards with this mod.

We also didn’t see any perceptible issues with frame pacing or frame delivery, although playing on a high-refresh-rate, G-Sync-Compatible monitor like our test bench’s ROG Strix XG27UCS smooths out all but the worst such issues. If you don’t have a high-resolution or high-refresh-rate monitor to begin with, the utility of MFG will be seriously limited for you anyway.

The latency question

So is this a free lunch? Is Nvidia soft-locking MFG to Blackwell purely for marketing reasons? That’s where performance testing comes in, since it has the potential to reveal whether there’s a catch in running MFG on Ada. The question is not so much whether MFG juices output frame rates, but whether it runs on Ada within acceptable latency thresholds.

As we’ve long emphasized, when you have essentially arbitrary control over output frame rates like MFG allows, input lag becomes the final barrier to a playable experience. So in the performance results that follow, we’ll certainly present output frame rates as you would expect. But you should view those in the context of your own monitor’s refresh rate. As long as the delivered frame rates we recorded exceed your display’s peak refresh rate, you have headroom to play with to keep your monitor at or near that number during gameplay.

The real issue, then, is whether both RTX 40-series and 50-series cards deliver an acceptable input latency under our test conditions. In our past testing, we’ve determined that a roughly 60ms average latency threshold, as indicated by Nvidia’s FrameView app, is the point at which player inputs and displayed frames start to become noticeably decoupled in AAA single-player experiences like Cyberpunk.

If you’re only slightly on the wrong side of this threshold, a game might still be playable, but if you totally blow past it, you’re likely to notice laggy inputs and increasingly distracting visual artifacts as the MFG model struggles to fill in the gaps between sparser and sparser input data.

Testing methods and notes

As one of the biggest technical showcases of the current PC gaming era, Cyberpunk 2077 lets us enable all the modern rendering features we’d want for Nvidia cards. It implements not only ray tracing and path tracing, but DLSS Super Resolution, Ray Reconstruction, and Multi Frame Generation. The number of AI-generated pixels per frame can be quite high in this title.

We enabled all those features to expose the full potential complexity of running all of their associated AI models in a modern rendering pipeline. If RTX 40-series cards are going to stumble for some reason with modded MFG enabled, we want to put as many obstacles in their way as possible.

And because benchmarking the performance of an unofficial mod is venturing into the Wild West anyway, we also added a DLSS 5 mod to the mix to see whether Blackwell GPUs have a distinct edge in the neural rendering future that the arrival of that feature promises to usher in. DLSS 5 is supposed to come to RTX 40-series cards later this year, so we think it’s good to understand where performance sits today, even if it’s subject to the same disclaimers as this MFG mod.

For reference, then, we tested Cyberpunk with maxed-out raster settings, path tracing, and MFG 4X at three resolutions: 1080p with DLSS Balanced, 2560x1440 with DLSS Performance, and 4K with DLSS Ultra Performance upscaling enabled.

We only tested cards ranging from the RTX 4090 down to the RTX 4070 for these experiments, both because of time and Cyberpunk’s VRAM requirements. We didn’t want to deal with potential performance pitfalls due to running out of VRAM, so the 12GB RTX 4070 is where we’re drawing the line for now.

Modded Cyberpunk 2077 1080p performance

At 1080p, Cyberpunk 2077 with MFG 4X scales fine on Ada, but it’s clearly scaling better on Blackwell. The RTX 4090 lands between the RTX 5070 Ti and RTX 5089. Introducing DLSS 5 to the mix doesn’t change any relative standings, although it does create some larger gaps between average frame rates and 1% lows than we might like on the RTX 4070 Ti Super, RTX 4070 Ti, RTX 4070 Super, and RTX 4070. The RTX 5070 has no such trouble.

But again, the real story is in our latency results. Here, we can see that every card we tested falls under our 60ms threshold with MFG 4X alone. But Blackwell hardware has a clear advantage in the standings, as even the RTX 5070 delivers a lower input latency than the RTX 4090 with our DLSS 5 mod off and a comparable input latency with it enabled. All the other Ada cards shake out as you would expect from there. But in absolute terms, even with DLSS 5 enabled, only the RTX 4070 Super and RTX 4070 are far beyond our acceptable latency thresholds.

Blackwell might have a latency edge with MFG enabled and a smoothness edge with a modded version of DLSS 5 on top, but at least with the RTX 4070 on up, there’s certainly enough headroom to use the feature on Ada.

And as we went to press, a version of the RTX 40-series MFG mod came out that removes the need for ReShade. That more streamlined approach might cut down latency, but we couldn’t test it because it currently crashes Cyberpunk 2077. Again, this is the Wild West, not a validated, bulletproof solution from Nvidia like you get with RTX 50-series products.

Modded Cyberpunk 2077 2560x1440 performance

At 2560x1440, generational performance standings and scaling between cards remains much the same as we saw at 1080p. The RTX 4090 still falls short of the RTX 5080, and the RTX 4080 duo falls behind the RTX 5070 Ti.

We also still see the wide gap between average frame rates and 1% lows rear its head on more Ada cards with our DLSS 5 mod enabled, though to be fair, these lows are still being smoothed over by MFG to the point that you’re unlikely to notice them with a high-refresh-rate, variable-refresh-rate monitor like we’re using.

On the latency side, all of the Ada cards except the RTX 4070 still run our modded MFG 4X with acceptable input latency. But enable the DLSS 5 mod we’re using, and input latency climbs past 60ms for all Ada cards except the RTX 4090. The RTX 4080 Super and RTX 4080 still provide acceptable performance under this full load, as they’re only slightly over our latency threshold. But for any less powerful RTX 40-series cards, you’d need to start choosing between DLSS 5 and other eye candy, like lighter RT settings instead of path tracing or no RT or PT at all.

Modded Cyberpunk 2077 4K performance

Our modded performance results at 4K with DLSS Ultra Performance demonstrate why it’s so important to discuss MFG-boosted frame rates in the context of input latency. If you’re not mentally dividing by four, everything on our output frame rate might look playable.

At this high output resolution, the RTX 4090 finally takes the lead over the RTX 5080, both with plain MFG 4X and with our DLSS 5 mod on top. The 16GB of VRAM of the RTX 4070 Ti Super would seem to be giving it an edge over the RTX 4070 Ti and RTX 5070, and the RTX 4080 duo would seem to beat out the RTX 5070 Ti.

Our latency chart for MFG 4X shows a weird, but entirely reproducible result: input latencies at 4K with DLSS Ultra Performance are actually much lower than they are at 2560x1440 for most Ada cards, despite the fact that both of these output resolution targets share the same input resolution.

We’re not sure why Ada cards run into such a latency hump with this MFG mod at 2560x1440 with DLSS Performance, but we double-checked our results, and this behavior is reproducible. Blackwell cards experience the more linear rise in input latency as output resolutions rise that you would expect.

Again, this is the Wild West of modded performance, and we have nobody to blame but ourselves here, but it’s an unfortunate result given the prevalence of 2560x1440 monitors. Perhaps the maintainers of this mod can track down the root cause and fix it, but nothing is guaranteed.

In any event, input latency with MFG 4X alone at 4K with DLSS Ultra Performance isn’t an issue for any card here. You might be pushing your luck with the RTX 4070, but both Blackwell and Ada cards are delivering on the promise of MFG here: smoother output with responsive input.

Add DLSS 5 to the latency picture, though, and as we’ve come to expect, you really want an RTX 5090, RTX 4090, or RTX 5080 for acceptable responsiveness. And you’re pushing it with the RTX 5080. No other cards in this bunch need apply.

Bottom line

Our experience with the purportedly Blackwell-exclusive Multi Frame Generation on RTX 40-series cards through modding suggests that there isn’t any glaring reason why Nvidia couldn’t enable the feature for Ada Lovelace cards, and that’s kind of wild given how heavily it was touted as a Blackwell-exclusive feature back when those cards launched.

At least as long as Nvidia doesn’t find a persistent way to lock it out, our experience is that MFG generally just works on Ada. Even if input latencies aren’t quite as low on those older cards as they are on comparable Blackwell hardware, all else equal, they’re still perfectly acceptable, even under the combined load of path tracing, DLSS Super Resolution, DLSS Ray Reconstruction, and MFG 4X in Cyberpunk 2077.

We only had time to test cards ranging down to the RTX 4070 for this quick look, but for folks looking to extend the useful life of their Ada hardware, the availability of MFG could certainly stretch those cards’ lifespans.

But our experience also shows that MFG on Ada isn’t perfect. It’s still a mod in active development, and the unusual and reproducible input latency behavior we charted at 1440p on RTX 40-series cards is the sort of unexpected pitfall you might expect from an unofficial implementation of the feature. Cross your fingers that it’s an issue that can be fixed by the community.

The fact that MFG works as well as it does on Ada, even in this modded form, also makes us wonder whether Nvidia might just enable official support for it at some point, given the apparently bleak prospects for gaming graphics card pricing and future hardware generations as the AI boom shows no signs of abating. And such a move would provide much broader and more immediate performance relief for gamers than re-introducing ancient silicon like the RTX 3060.

Given that Nvidia is already working on bringing DLSS 5 to RTX 40-series GPUs, maybe this mod will convince it to throw in official MFG support, too. Fingers crossed.

Discrete graphics card shipments for desktop PCs in Q2 2026 totaled 12.5 million units, the highest number since Q1 2022 despite record-high prices and cratering shipments of desktop CPUs, according to findings from Jon Peddie Research. The result highlights a broader trend that shows that unit sales of standalone GPUs for gaming have so far remained immune to rising prices, perhaps because gamers are expecting even higher prices in the coming quarters.

The industry shipped 12.5 million standalone graphics cards for desktop PCs in the second quarter of 2026, up around 5.9% sequentially and 7.8% year-over-year. 12.5 million add-in boards (AIBs) is the highest number of graphics cards sold in one quarter since the first quarter of 2022, when the industry shipped 13.38 million AIBs.

It is particularly noteworthy that 2026 is shaping up to be better for unit sales of desktop graphics boards than 2025 despite raising prices. For the first half of 2026, 24.3 million desktop AIBs were shipped, up significantly from 20.8 million graphics cards supplied in the first half of 2026. JPR analysts also note that only around 14 million desktop PCs were sold during the quarter, which — given an unusually high 89% attach rate — largely means that the majority of AIBs shipped during the quarter were aimed at gamers buying in retail and not at PC makers.

"Defying common wisdom, high-end AIB sales spiked as prices increased," said Jon Peddie, president of JPR. "Our theory is consumers rushed to buy AIBs before the prices went any higher, as the war in Iran is driving prices up in all segments."

Having shipped about 11.25 million discrete GPUs for desktop computers in Q2 2026, Nvidia remained the undisputed leader of the market with around 90% market share. AMD controlled roughly 8% of the market, shipping about one million discrete desktop GPUs, while Intel's share increased to 2% on shipments of several hundred thousand units. Meanwhile, Jon Peddie Research notes that market share changes were negligible during the quarter: AMD’s overall AIB market share decreased by -0.16% from the previous quarter, Intel's market share increased by 0.3%, and Nvidia's market share decreased by -0.1%.

For Nvidia, the quarter was particularly good as it sold the highest quantity of discrete GPUs for desktop PCs in a single quarter since Q3 2017, when it sold approximately 11.72 million units. By contrast, sales of AMD's standalone graphics cards have been floating below or around one million units per quarter for nearly four years now, with only three quarters being exceptions (Q3 2023, Q4 2023, Q4 2024). Still, one million is higher than the around 700 thousand discrete desktop GPUs the company sold in Q2 2025.

In total, Nvidia shipped approximately 17.365 million discrete graphics processors in the second quarter: roughly 11.25 million units went to desktops, and around 6.115 million units were installed into notebook and compact PCs. Since both AMD and Intel have quietly quit the market for standalone GPUs for mobile PCs, their shipments to this market segment were essentially zero.

Biren Technology, a leading supplier of AI accelerators from China, posted massive nearly 2,000% revenue growth in the first half of 2026 amid skyrocketing sales of non-Nvidia AI processors in the country, according to Jon Peddie Research. Sales of the company's products began to climb rapidly in the second half of 2025 after American companies led by Nvidia stopped supplying their AI GPUs to the People's Republic due to export control measures.

Biren reported first-half revenue of $183.9 million, up 1,998% year-over-year from around $8.665 million in the first half of 2025. The company's gross profit rose to $78.552 million, and gross margin increased to 42.7%, but it still lost $56.2 million primarily because it continued to invest in new products, including AI accelerators, optically-interconnected rack-scale solutions, and software. Biren's revenues started to climb in the second half of 2025, so for the whole year its sales reached $154.17 million as its market share of AI accelerators in the country was below 3%, according to TrendForce.

For those who follow China's AI and GPU markets, Biren Technology is certainly a familiar name as the company's products are well documented and appear to be competitive with those developed by AMD and Nvidia on paper. The company has developed at least three high-end AI GPUs — the BR106, BR110, and BR166 — and is currently working on BR20X, BR30X, and BR31X accelerators, according to JPR. Biren has also built its own Birensupa software stack meant to compete against Nvidia's CUDA and is working on a rack-scale solution.

In reality, demand for domestic AI accelerators has always been relatively low in China, as even cut-down versions of Nvidia's leading AI GPUs provided better performance and software stack than solutions developed in China. While Nvidia charged $12,000 - $15,000 per H20 AI GPU when it sold these products in the PRC, it still supplied some 2.2 million AI accelerators to the country in the first half of 2025, when it could still ship them until the Trump administration's export controls kicked off in May, according to TrendForce. By contrast, Biren shipped thousands, maybe tens of thousands of AI accelerators throughout the whole 2025. Even today, Biren's shipments are minuscule compared to Nvidia's in 2025.

Without a doubt, Biren's financial improvement is real and impressive, but it is coming from an extremely small base in the first half of 2025, so the 1,998% 1H 2026 growth figure makes Biren sound much larger than it actually is. While Biren is growing at an enormous rate, with $183.9 million in revenue, it is still a relatively small accelerator supplier in absolute terms.

What remains to be seen is whether Biren can secure enough manufacturing capacity from SMIC or other suppliers to compete with larger Chinese AI accelerator vendors, such as Huawei, Kunlunxin, and Cambricon. The company certainly has more financial resources than it did a year ago and faces less formidable competition from AMD and Nvidia amid U.S. export restrictions and China's own bans on American AI hardware. But having competitive designs is only part of the equation: Biren now must manufacture enough accelerators to satisfy customer demand and substantially increase its market share.

Not that long ago, we reported that Nvidia was dusting off the blueprints for the RTX 3060, in its 12 GB form. At the time, we'd spotted it in stores for about $339.99, but just like with ever-climbing memory, hard drive, and SSD prices, two months passed is an eternity. The same cards are now selling for $489 new, and one particular specimen is the SRhonyra RTX 3060 12 GB Low Profile card, for $495.59.

• SRhonyra RTX 3060 12 GB Low Profile card - $495.59

This card and its price may raise more than a few eyebrows, but there are reasons why it exists. First off, it's a one-slot model, making it easy to put many of them to work in the same machine with relatively little concern for airflow. They have no power inputs and rely on 70 W delivered by the PCIe slot alone, eschewing the need for a high-end PSU and lots of cables. Third, the low-profile form factor makes it possible to place them in potent puny personal computers.

Attentive readers might surmise that one (or more) of these would be good candidates for an entry-level local LLM rig. That's precisely how SRhonyra is pitching the card, calling it "capable of local AI" and "running 7B [to] 13B LLMs." A standard-dimensioned, fully powered RTX 3060 is capable of drawing a maximum of 175 W, so it's fair to assume the performance of the diminutive variant will sit below that of its full-sized brethren, given it ought to only draw 70 W of juice from the PCIe slot.

Even then, it's likely that people interested in these cards are looking to use them either as secondary GPUs, or use more than one in the same box to be able to virtually pool their VRAM and use larger models than you'd otherwise be able to. The low power draw also means they're a simple, thoughtless drop-in to an existing system, whereas larger, more power-hungry cards require careful consideration with physical spacing (or lack thereof), power supply sizing, and ever-annoying cables.

Nvidia's DLSS 5 Neural Rendering technology has taken the PC gaming world by storm since its recent leak and then official rollout in recent days. In what might be one of the most impressive technical applications and mods of the feature yet, one developer has showcased DLSS 5 running on a second GPU to share the processing load, rendering the game on the first GPU before applying Neural rendering at the end of the frame, thus boosting the performance of neural-rendered frames.

Marcelo Guibout shared the demonstration online, with videos showing the process running on a cinematic video from The Blood of Dawnwalker, as well as Cyberpunk 2077. Guibout was quick to clarify that the videos are technical showcases, not benchmarks. However, they did share some performance figures. More exciting still, you can download the project from GitHub and try it for yourself. However, the technique does require a second display and doubles the display latency.

The above demonstration features a Ryzen 7 7800X3D, 32GB DDR5 setup with two Nvidia RTX 5060 Ti 16GB GPUs, with both cards using PCIe 5.0 x8, and a display attached to each card. The ReShade add-on, dubbed MGPU Bridge, reads each finished frame before applying Nvidia's Neural rendering. "Neural rendering happens at the end of the frame: it takes a finished frame and hands a finished frame back," they explain. "That is what makes it possible to pick it up and run it somewhere else. The add-on creates its own D3D12 device on your second GPU, sends each finished frame across to it, runs DLSS-NR there, and displays the result on that card's own monitor — so nothing has to come back. The render GPU does no neural work at all and runs cooler for the same reason."

In the TBOD demo at 1080p, they shared the following performance numbers:

Guibout clarified that DLSS super-resolution is running in every column of the test, with the rows showing each mode tested. The game rendered at the same internal resolution in all three columns, with the first column giving figures with DLSS 5's neural rendering switched off, representing the ceiling for performance. As you can see, running DLSS 5 on the second card in this dual-GPU setup drastically increases performance in every mode.

"The frame rates are not the finding. The slope is: what you gain by going from DLAA down to Ultra Performance, and how much of that available gain each arm keeps," Guibout explains. "Neural post-processing saturates whatever device it runs on, and it always runs at output resolution. Its cost barely falls as you drop the DLSS mode, while the render work collapses. On the render card it therefore eats a larger and larger share of every frame, and upscaling stops paying for itself: you keep about a third of what the machine actually had to give. Move it to the second card and you keep 86% of it."

Other benefits include temperature reductions on the rendering card, with the first GPU running 21 degrees cooler without the neural load.

Guibout is cautious to note this isn't a return to Nvidia's SLI technology, which would split the workload of frame rendering between two GPUs. They also noted plainly this is not Nvidia's vision for DLSS 5 and lacks the native integration of official DLSS 5 support.

You can see the original demo for TBOD below:

As commenters online have noted, the method seems more akin to using Nvidia's dedicated PhysX cards, with plenty of potential for gamers who have the capacity to run a dual GPU setup. The second demo features a Ryzen 5 5600 on a DDR4 system, so top-of-the-line hardware isn't required to make it happen.

Nvidia launched the GeForce RTX 3060 (12GB) in early 2021 as an affordable mainstream gaming GPU with a 170W TDP, before re-releasing two months ago to offer relief during the ongoing component crisis. Throughout this time, not one person thought that the 3060 consumed too much power... except someone in China who decided to make a 75W version of the card. This blower-style variant has no 6- or 8-pin power connectors and runs entirely off the PCIe slot it'll be connected to, resulting in some expectedly underwhelming performance.

The RTX 3060 takes very well to undervolting and, therefore, can already be taken all the way down to just 100W at the cost of modestly reduced performance. A single PCIe x16 slot can provide up to 75W of power, so forcing a 3060 down to that number requires a shunt mod. This practice is usually associated with unlocking power limits on a GPU to chase overclocking feats where you decrease resistance, but in this case, you'd be increasing it.

This is a single-slot, low-profile card with a blower-style cooler about the size of a modern smartphone. You get just 1x HDMI and 1x DisplayPort in terms of connectivity. There is no fin stack present either, the PCB lacks a backplate, and the shroud is a thin metal sheet responsible for all the heat dissipation with a rudimentary heatsink in the middle.

A teardown of the GPU on BiliBili shows it's under-equipped from the inside, too. The memory chips have no thermal pads or paste on them. Instead, the metal shroud just touches the core directly to keep the entire thing cool. Remember that there's just one fan at the far end to blow hot air; there is no intake or proper airflow with positive pressure. So far, everything about this card, except its compact size, is looking subpar.

Once we get to testing, any skepticism is validated as the 75W RTX 3060 GPU is barely able to edge past 900 MHz, despite being rated for 1,770 MHz boost clocks. This results in a Time Spy score of just 4,821 points whereas a regular RTX 3060 easily scores upwards of 9,000 points in the same benchmark. Even older budget GPUs like AMD's iconic RX 580 and Nvidia's equally-popular GTX 1060 score more.

During the Time Spy run, the GPU hotspot also went past 90 degrees Celsius, proving that the cooler is barely performing if it's struggling to tame even a 75W card. Clearly, this GPU was suffering from severe thermal throttling on top of already having its power budget more than halved. One could make an argument that it's still an Ampere GPU, so it could make sense in low-power, compact systems. But there are now mini PCs with similarly performing or far more potent integrated graphics in 2026.

PAX West is underway at the Seattle Convention Center in Seattle, Washington, and Nvidia is offering a selection of its Founder's Edition GPUs at MSRP. Nvidia has RTX 5070, RTX 5080, and RTX 5090 models available while supplies last, along with packs of GeForce Trading Cards Series 1. Jacob Freeman, GeForce Evangelist at Nvidia, shared the announcement on X, telling interested gamers to "come find me" if they want a GPU.

Nvidia's Verified Priority Access (VPA) is a lottery program for Founder's Edition cards that the company launched in 2022 for RTX 40-series GPUs. It returned in 2025 for RTX 50-series GPUs, and although you can still sign up for the program online, Nvidia has seemingly shifted to offering MSRP GPUs during live events. Last month, the company did something similar at QuakeCon in Austin, Texas.

Over the past month, we've seen a sharp rise in the price of Nvidia's highest-end graphics cards in our GPU price tracker, with the $1,999 RTX 5090 now regularly listed for above $5,000. The RTX 5090 has been a particular flashpoint due to its plentiful 32GB of GDDR7 memory, making it ideal not only for flagship gaming performance but also (relatively) low-cost local AI inference.

Earlier this month, however, we saw increases as large as 39% in median list price for RTX 50-series GPUs, following a series of reports about regional price increases outside of the U.S. The increases hit the middle of Nvidia's Blackwell stack the hardest, with the RTX 5060 Ti 16GB jumping by 29% and RTX 5070 jumping by 36%.

The RTX 5090 has continued to rise in price, even after the hike we saw early last month. At the time, the median price was $4,699.99, but now, you'll spend at least $5,000 on a GPU online. Deals, if you can call them that, are available on the RTX 5090 if you have a Micro Center nearby, with models going down as low as $4,200.

This week, Nvidia launched DLSS 5 for RTX 50-series GPUs in NBA 2K27, following a leaked DLL that allowed modders to get Neural Rendering operational in just about any game. Within days, the community got DLSS 5 operational on RTX 40-series GPUs, as well as older RTX 30-series GPUs, though performance was unplayable on the latter. Nvidia says it plans to bring DLSS 5 support to RTX 40-series GPUs at a later date.

Although Nvidia doesn't have a booth at PAX West 2026, many of its partners do, including Starforge Systems, Razer, and Lenovo. Freeman says he'll be posting updates on X on where and when attendees can find him.

Nvidia's DLSS 5 has arrived in NBA 2K27, and we've been up since midnight testing it across every RTX 50-series graphics card to see what the first official implementation of this tech can do and how it compares to the community-implemented mods that have been circulating over the past couple weeks.

Before we discuss the performance cost of DLSS 5, many will ask whether this tech is even worth getting excited about, given the intense controversy that it's sparked ever since Nvidia revealed it earlier this year.

In short: yes, absolutely.

This first-party implementation of DLSS 5, tuned under the full control of 2K Games' art directors and artists, looks incredible, full stop. If you see it running, you will want to leave it on. It doesn't look like "slop" or a cheap filter. It just looks correct, or at least more correct.

For just a couple of examples, without DLSS 5, even at ultra settings and with RT, the game has a slightly "plastic" or "flat" look. Faces can have a waxy uniformity that immediately indicates that you're looking at a video game, not a live broadcast. Hair looks totally and unnaturally flat. The whites of eyes can be unnaturally bright, giving a sort of googly-eye or doll-like effect that immediately broke my sense of immersion. I didn't think any of this would be a big deal for a sports game, but it all stands out.

DLSS 5 breathes incredible life into NBA 2K27. Skin looks like real flesh and blood. Eyes are rendered with the proper tones, depth, and sparkle. Hair looks a billion times more realistic. Every human on screen just looks more alive. You won't entirely forget that you're looking at a game, but it becomes much easier to suspend disbelief and get lost in the action.

My experience slapping DLSS 5 mods into games has produced promising but sometimes mixed results. If the polished experience that NBA 2K25 delivers is any indication, I can't wait for more studios to get their hands on it and integrate it into their games under the care of the same artists that created them.

Our testing methods

We did our best to deliver clean test numbers in the short time we've had with NBA 2K27. Performance in this can be tricky to measure. Benchmarking in the hub worlds or cutscenes will deliver lower performance than on the court, and even then, in-game events like fouls and time-outs will also result in skewed numbers if you're not careful. We picked a repeatable match-up from the game's career mode and sampled 60 seconds of continuous gameplay with DLSS 5 on and off, making sure to restart if we ran into any of the scripted events above.

We used NBA 2K27's maximum graphics settings as our baseline, including RT. We tested at native resolutions without upscaling, and we didn't test with Multi Frame Generation enabled. We view MFG as a cherry on top of an already solid baseline experience, not a baseline in itself.

Our test system is built with the following components:

All of our performance results are captured using Nvidia's FrameView 2.0 utility. We measure graphics card power consumption directly with Nvidia's PCAT hardware power logging tool.

If you have any questions about our testing methods, let us know in the comments and we'll do our best to answer them. On to the numbers.

NBA 2K27 1080p performance

At 1080p, everything from the RTX 5070 on up is CPU-bound without DLSS 5 enabled, even with maxed-out settings and RT on. Turn on DLSS 5, though, and the differences in Tensor Core compute across the cards quickly becomes obvious. But even the RTX 5060 can manage nearly 60 FPS on average with DLSS 5 enabled at 1080p, so almost anybody with a Blackwell card can at least try out the feature.

Given these results, you might think to enable DLSS Super Resolution (aka upscaling). But because the amount of time needed to run the DLSS 5 model has a fixed cost per output frame that scales with your target resolution and largely dominates the total frame time, especially on lower-end hardware, you may find that enabling DLSS SR doesn't have as much of an effect on performance as we've come to expect. The game still has to wait for that final generative step to occur, even if DLSS SR cuts down some of the total frame time.

As noted, we didn't use MFG for these tests, simply because there's no need for us to make the numbers on these charts artifically large. If you do want to enable it, the multiplier you want will be determined by your monitor's refresh rate and your own personal tastes.

We did chart PC latency as estimated by FrameView, so you can get a sense of whether there's enough of a latency budget to enable MFG at all. At 1080p, every card is a solid candidate for enabling MFG without adding unreasonable amounts of input latency, although the RTX 5050 is in a borderline position. And all three of the 8GB cards might have trouble fitting the MFG model into their VRAM with these maxed-out settings and DLSS 5 enabled, so you might need to turn off RT at a minimum to use MFG on these lower-end GPUs.

As we already saw in early testing of the leaked DLSS 5 builds circulating in the community, enabling the model has a large impact on power consumption, likely due to the intense Tensor Core load required to run the DLSS 5 model.

NBA 2K27 1440p performance

Moving up to 1440p separates our cards some more. The RTX 5080 and RTX 5090 are still CPU-limited without DLSS 5. The RTX 5060 Ti 8GB, RTX 5060, and RTX 5050 all get a "Low VRAM" warning with these settings, so if you're trying to push this higher resolution with those cards, you might want to start tuning your DLSS upscaling settings to relieve VRAM pressure, even if it doesn't result in additional performance with DLSS 5.

But you really want an RTX 5060 Ti at a minimum here to get acceptable input latency with DLSS 5 enabled, and the RTX 5070 is the true baseline for a good experience. At the higher end, the RTX 5070 Ti and RTX 5080 both provide fluid frame rates even without MFG, and you have the latency budget to enable framegen without worry on anything from the RTX 5070 on up.

DLSS 5's power consumption on all these cards (except the chugging RTX 5050) rises as expected with the higher-resolution output frame we're asking it to generate. Everything from the RTX 5060 up to the RTX 5070 Ti ends up running at its power limit, while the RTX 5080 and RTX 5090 still have room to stretch out. The unconstrained MSI RTX 5090 Lighting Z sucks down a ton of power to deliver its scorching performance.

NBA 2K27 4K performance

Running NBA 2K27 at 4K with DLSS 5 is extremely demanding, since the model has to generate an output frame with more than twice as many pixels than at 1440p. Even the RTX 5070 Ti is straining here, as its input latency with DLSS 5 is potentially too high to enable MFG while still delivering a responsive gameplay experience.

You can see why Nvidia only recommends the RTX 5080 and RTX 5090 for a 4K DLSS 5 experience in this title, as they're the only two cards that deliver high enough baseline performance with low enough input latencies to make MFG practical. And even then, the RTX 5080 is on the edge of what we'd consider an acceptable input latency before enabling MFG.

Our power consumption chart at 4K is a bit of a mess at the low end since the RTX 5050 and RTX 5060 are crushed by the demands of DLSS 5 and end up chugging. Really, though, we're here to see how the RTX 5080 and RTX 5090s handle this incredibly Tensor Core-intensive work. Both cards end up running at their power limits.

The MSI RTX 5090 Lightning Z shows how much power scaling the GB202 GPU has left in it once you remove the constraint of a single 12V-2x6 connector. We were wondering whether the power numbers we saw from a modded version of Control were a fluke, but DLSS 5 in NBA 2K27 goes even harder and pushes the RTX 5090 Lightning Z to nearly 850W on average in this test. For 48% more power than the RTX 5090 Founders Edition, you get 22% higher performance.

On a big 4K screen like an OLED TV, a fluid 90 FPS at a native 4K resolution with the level of detail and realism that DLSS 5 adds is an astounding gaming experience. You really haven't seen anything like it. But mere mortals with single-plug 5090s will probably want to enable MFG 2X at a minimum.

Bottom line

It's early days for DLSS 5 performance, and in its first official showing in NBA 2K27, it certainly has a large performance cost—sometimes well over 50% on lower-end hardware. But the generational leap in realism it provides for every human on screen is well worth it to my eye, at least. Now that I've seen a first-party, developer-driven implementation of DLSS 5, I want it in every game where its photorealistic enhancements would make sense, without question.

And as it's implemented in NBA 2K27, our performance results show that the tech is accessible enough that almost anybody with an RTX 50-series graphics card can try it out and still enjoy a fluid and responsive experience, even without Multi Frame Generation. Most of us aren't playing on 4K monitors, and at 1080p and 1440p, the Blackwell card you may already have is probably up to the task of running DLSS 5 with acceptable baseline performance.

DLSS 5 does upend some intuitions we've developed around the interactions of upscaling and frame rate. The large fixed frame-time cost that's required to generate the DLSS 5 output frame is entirely dependent on your target output resolution, making the lowered input resolution of DLSS 4.5 upscaling far less of a performance multiplier than it might otherwise be.

Given those realities, DLSS MFG can still make for a more fluid experience alongside DLSS 5 with a couple of clicks in a menu—at least assuming you have enough VRAM to hold the game assets, the DLSS 5 model, and the MFG model all at once. On 8GB graphics cards, this may require some tweaking to get working.

Nvidia has promised to continue improving the fundamental performance of the DLSS 5 model with time, and the fact that it's moved from requiring a dedicated RTX 5090 to run in the demos we saw at GTC to running on an RTX 5060 today is a sign that such promises do bear fruit.

Boosting the fundamental performance of the DLSS 5 model is vital, because as we all well know, prices for most Blackwell cards have spiked across the board to the point that former midrange options like the RTX 5060 Ti 16GB and RTX 5070 are many hundreds of dollars more expensive than their MSRPs and well out of whack with their former performance-per-dollar propositions. A hardware upgrade to get better performance is no longer a no-brainer for many PC builders, and that problem is going to get even worse as the AI boom continues unabated.

Those elevated prices are also why Nvidia's post-launch pledge to bring the DLSS 5 model to RTX 40-series cards later this year is a welcome development. In our limited experience with community mods, Ada GPUs run the current DLSS 5 model about as well as Blackwell cards do, and gamers who haven't already upgraded from their 40-series cards certainly won't be raring to move off that hardware any time soon. Locking DLSS 5 to new GPUs that are prohibitively expensive to buy for non-technical reasons isn't a winning strategy for goodwill or broad adoption, and it's good to see Nvidia acknowledge that reality.

For all that, we shouldn't lose sight of the fact that DLSS 5 is a huge, exciting leap forward in the never-ending pursuit of photorealism in real-time graphics. When you toggle it on and off in NBA 2K27, it feels like a generational improvement in rendering technology at the press of a button. Now that I've seen it in action, I don't want to play NBA 2K27 without it, and I hope to see more first-party integrations of it soon.

Sales of discrete graphics processors for consumer PCs were up both sequentially and year-over-year in the second quarter despite soaring prices caused by component shortages, according to a newly released report by Jon Peddie Research. Although PC CPU shipments dropped in Q2 2026 year-over-year amid seasonality and shortages, sales of standalone graphics processors for consumer computers were up 12.2% sequentially and 14.1% YoY, the best market dynamics in some time.

Sales of graphics processing units for consumer PCs — which include integrated and standalone GPUs for desktops and laptops — totaled 75.5 million in the second quarter of 2026, up 10.4% quarter-over-quarter and 1.1% year-over-year, primarily driven by notebooks. This happened as the consumer CPU market contracted by 1.1% YoY amid a massive sequential drop in desktop CPU shipments and a significant rise in mobile CPUs shipments. Desktop GPU shipments declined by 4% quarter-over-quarter, while notebook GPU shipments surged by 16.8%, JPR claims.

But despite declining desktop PC unit shipments and modest growth in notebooks, unit shipments of discrete GPUs increased by 12.2% sequentially and 14.1% year-over-year in Q2 2026, according to JPR data. Jon Peddie Research does not publish absolute numbers of standalone graphics processors shipped in the second quarter, but our estimate is that around 20 million discrete GPUs were sold by AMD, Intel, and Nvidia in Q2, based on attach rates and Nvidia's market share and dynamics.

The results indicate that demand for PCs with discrete graphics remained remarkably resilient despite soaring component prices and slowing demand for desktop PCs. However, JPR's shipment data does not reveal whether the increase was primarily driven by gamers buying graphics cards, stronger demand for gaming notebooks, or other factors.

"The second quarter is typically down compared to the previous quarter," said Dr. Jon Peddie, president of Jon Peddie Research. "This quarter, discrete GPUs increased by 12.2%, even while a global memory crisis sent component prices soaring, driven by a mix of supply-side positioning, artificial demand shocks, and localized market dynamics."

Jon Peddie Research has yet to publish its complete desktop AIB report, which is expected later this month and will include market shares for AMD, Intel, and Nvidia; yet it is safe to say that the latter has maintained its undisputed leadership.

As for the overall consumer PC GPU market, Intel retained its leadership with a 56% market share as it increased shipments of consumer CPUs in Q2 2026. Nvidia came second with 23%, which is not bad at all considering that it only ships discrete GPUs. AMD came third with 21% share, up significantly from 14% in the same quarter a year ago, as it managed to gain seven percentage points of the consumer GPU market YoY amid growing sales of its consumer CPUs.

Nvidia started its AI business with humble AI accelerators, then moved to AI servers, and later to rack-scale and data center-scale platforms. With its multi-faceted AI strategy in place, the company is now looking beyond hardware. On Thursday, Nvidia said it had agreed to acquire Hugging Face, one of the world's largest platforms for distributing and developing open AI models, for $12.93 billion. Hugging Face will retain its brand and remain open to models, frameworks, clouds, inference providers, and computing platforms.

Nvidia positions the deal as an expansion of its commitment to open-weight AI models and as a way to popularize the use of artificial intelligence in general by enabling different types of developers to use appropriate open models for their products. The move is strategically important for Nvidia as it commands the lion's share of the AI hardware market and wants demand for its hardware to grow. Yet, Nvidia promises not to force participants of the platform into its hardware ecosystem.

Hugging Face currently serves more than 18 million developers, researchers, and creators, who have uploaded over 3 million models, 500,000 datasets, and 1 million applications, according to Nvidia. Furthermore, more than 200,000 companies use the service to find, assess, modify, and deploy AI models. Nvidia claims this business model will remain intact after the acquisition: Hugging Face will continue to host open-source and open-weight models from different developers and support multiple clouds and accelerator architectures.

Meanwhile, Nvidia says that its infrastructure, engineering resources, and global presence can improve Hugging Face's platform reliability, safety, model evaluation, inference, and deployment capabilities, which means that it will increase the portion of Hugging Face that relies not only on its hardware but also on its resources and global presence.

It is noteworthy that Nvidia itself already has a considerable footprint on Hugging Face. The company claims to have published more than 500 models and 250 open datasets, making it one of the platform's largest contributors. Nvidia also develops some of its models, software libraries, and tools openly so that third-party developers can modify and build upon them.

Interestingly, the deal appears to have originated with Hugging Face's founder. Nvidia's Jensen Huang says Clément Delangue approached him while evaluating the company's next stage and concluded that Nvidia could provide an appropriate home for Hugging Face, its community, and its open-model ambitions. As it turns out, Nvidia agreed to buy Hugging Face and keep developing it. The Hugging Face team will join the Nvidia organization and continue working on the project.

If you're a token-hungry AI enthusiast, and if you or your family happen to have PCs with idle GPU cycles to spare in this economy, Nvidia wants to make it possible to harness those cycles so you can save cash on cloud tokens and keep your work private. At IFA 2026, the company is introducing a local distributed AI clustering tool called the Personal AI Router (PAIR) that dispatches agentic AI sub-tasks from your main PC to systems on your home network that have suitable GPU cycles to spare.

As Nvidia tells it, when a user runs a local AI agent and gives it a goal to complete, that central agent might then spawn several sub-tasks carved out of that larger goal. If those sub-tasks or sub-agents are all running on the same GPU, the contention they create might cause the task to finish more slowly than it could if each sub-agent had a dedicated compute node to work with.

PAIR is a tool that can make that distributed AI work happen on a home network. Assuming that a family or shared household is sufficiently flush with idle GPU resources, PAIR canYEa assign each participating system one of those sub-tasks to perform and return the results to the main node, potentially resulting in faster completion of the larger agentic task.

Of course, systems on your local network won't always be idle. Their owners will frequently use the GPUs in their systems for gaming, creative work, or AI tasks of their own. If a user needs their GPU back, PAIR purports to gracefully deal with those changing conditions. It doesn't reserve dedicated capacity from other PCs; it's elastic by design and will make the best of the resources available to it at any given moment.

This unpredictable availability of spare cycles does, of course, mean that quality of service is not assured from a PAIR cluster. But for long-running tasks that don't need to be done on a strict deadline, being able to put spare compute to work could still be more effective than running an agent swarm on a single node.

PAIR sounds relatively simple to set up. It creates a proxy for popular AI front-ends like LM Studio and Ollama to connect to. PAIR then orchestrates work across available nodes on the network and returns the results of that work to the originating application on the head node.

In turn, participating PAIR nodes also need to be running Ollama or LM Studio and have a PAIR installation of their own. Nvidia says that enrolling systems in a PAIR cluster is straightforward and relies on mDNS or an IP address fallback for discovery. PAIR will also help initiate model downloads on participating systems, but Nvidia says that nodes don’t need to have identical models or sets of models downloaded to participate.

If more systems do have a given model available, though, it broadens the pool of potential nodes that can handle a request if the orchestrator agent needs a particular model’s capabilities.

PAIR will run on any DGX Spark (or other GB10) box, as well as GeForce RTX 20-series graphics cards or newer. It also supports Macs with M4-series processors or newer for inference. Accordingly, the PAIR client will be available for Windows, macOS, and Linux.

Nvidia is finally getting more specific about the launch of the RTX Spark. The chip, which it is now officially calling the "N1X," will launch in laptops in October and come in two configurations.

The more powerful option will mix a 20-core Grace CPU with a 6,144-core Blackwell GPU, and come with between 24 and 128GB of unified memory. The other variation uses an 18-core CPU and a cut-down 5,120-core Blackwell GPU with between 24 and 32GB of unified memory. That version will only be in laptops.

There's a lot that Nvidia hasn't specified outside of core counts and memory support, but with the chip launching next month, we're sure to find out more, either through Nvidia or OEMs, soon enough.

Nvidia made the announcement ahead of IFA in Berlin, where Lenovo and Acer announced new systems with Nvidia's new chips. Lenovo debuted the Yoga 9n 2-in-1 laptop, while Acer is debuting the SFF RTX Spark, a mini desktop. As of this writing, neither has pricing.

Further systems will come from Asus, Dell, MSI, HP, and Microsoft, the latter of which debuted the flagship Surface Laptop Ultra at Computex this year. These systems are being pushed as laptops and mini PCs meant primarily for agentic AI, though Nvidia has also demonstrated its gaming prowess in demos. We'll need to get our hands on these to test both scenarios. The company is also promising "all-day battery life," another claim we'll need to test.

The use of the N1X name has occurred in leaks, but this is the first time Nvidia has used it publicly. This suggests a separate, lower-end N1 chip may exist and be released down the line.

It's unclear exactly which devices will launch in October and which might come out later in the year. It's likely that that information will come soon, now that Nvidia has chosen a month. We'll also have to see if Microsoft's Agent Framework, which lets agents run in the background under control of the operating system, is ready in Windows by then.

Nvidia's unified memory approach is similar to the one popularized by Apple in its M-series chips, including the recently announced M6 and M5 Ultra, which also uses an Arm instruction set. On the x86 side, AMD's Strix Halo (and upcoming Gorgon Halo) also use unified memory.

The launch of these chips will also break Qualcomm's exclusivity on Windows on Arm, bringing a major new silicon vendor into the ecosystem, and possibly more partnerships from application developers eager to use Nvidia's tools.

Developers at RPCS3, the open-source PlayStation 3 emulator, have criticized Nvidia’s DLSS 5 technology, calling it an “AI-slop generator.” A number of modders managed to port DLSS 5 to various games last week after the latest neural-rendering tech leaked before the official launch. A few people also went ahead and apparently tested the leaked DLSS 5 DLL on the RPCS3 renderer. According to a post by the official RPCS3 account on X, “Whilst the gaming industry pushes more upscalers and frame generation to hallucinate games and hide their lack of optimisation, we do not ship any of that slop.”

The post has drawn criticism from certain users, with one calling RPCS3’s response “possibly the dumbest” they could have expected. They argued that DLSS and FSR frame generation can complement game-engine optimizations rather than simply mask poor performance, while Nvidia’s Reflex can help mitigate the latency penalty associated with frame generation.

In its response, RPCS3 said that emulators have never-ending goals, and they do not simply target high-end hardware to achieve playable status. The developers also added that modern temporal upscalers are difficult to implement because PS3-era games do not expose the motion-vector data they require. At the same time, RPCS3’s low-level RSX emulation makes reconstructing that data impractical. As a result, the emulator currently relies on spatial upscalers such as FSR 1, bilinear, and nearest-neighbor scaling.

DLSS 5 is expected to officially roll out starting today, with NBA2K27 being the first game to support the latest tech. Focusing on 3D-guided neural rendering, DLSS 5 aims to deliver movie-grade visual realism and photorealistic lighting in games. The AI model is trained to deduce how complex features of game scenes like characters, skin, hair, and environmental lighting should look in the real world given certain inputs from the game engine, like color buffer and motion vectors, in combination with an input frame.

According to Nvidia, DLSS 5 will also provide different AI models and controls, allowing artists to decide what gets enhanced, where the effect appears, and how strongly it is applied. For players, however, Nvidia wants to keep things simple as DLSS 5 essentially becomes an on/off graphics option, while the detailed decisions about how the AI-generated visual effects are used are handled by the game developer.

Since DLSS 5 is resource-intensive, gamers should expect a noticeable drop in frame rates, especially with the initial release. Nvidia has also confirmed that DLSS 5 will only be compatible with RTX 50-series desktop and mobile GPUs, as well as GeForce Now. Notably, the recently leaked DLL has allowed some modders to successfully port DLSS 5 to older RTX 40-series cards.

Nvidia and MediaTek this week announced a major expansion of their partnership under which Nvidia is investing $3.5 billion in convertible bonds issued by MediaTek, while the latter adopts NVLink Fusion platform for its custom AI accelerators, local AI systems, and automotive platforms. On the one hand, MediaTek's adoption of NVLink Fusion enables it to design accelerators for Nvidia's fully developed rack-scale platforms. On the other hand, Nvidia gets a slice of the growing market of custom AI accelerators.

Having become the world's largest supplier of AI accelerators, Nvidia does not have direct rivals of comparable size. However, in a world where custom AI accelerators are becoming more widespread as more companies see benefits in bespoke solutions, Nvidia must hedge against their rise and ensure that its addressable market expands even if it does not win every accelerator design. One of the ways to achieve this is to spread its NVLink Fusion platform beyond its own products and to popularize it among users of custom hardware. The deal with MediaTek is aimed at exactly that.

AWS, Google, Meta, Microsoft, and now OpenAI are developing their own AI accelerators partly to reduce dependence on expensive merchant GPUs. Nvidia cannot necessarily prevent this trend, so NVLink Fusion gives it another strategy: if customers replace some Nvidia GPUs with their own XPUs, Nvidia wants those XPUs connected using NVLink, paired with Nvidia CPUs where appropriate, and deployed within Nvidia networking and rack architectures. Under the new arrangement, customers can bring an XPU architecture to MediaTek, then MediaTek and Nvidia will supply much of the technology surrounding the actual compute engine.

MediaTek will use NVLink Fusion as the foundation for custom accelerators that can evolve alongside future Nvidia architectures. The platform includes the NVLink Fusion chiplet, which connects custom XPUs to Nvidia's NVLink scale-up fabric using electrical or photonic interconnects; NVLink-C2C, which provides high-bandwidth, energy-efficient links between XPUs, Nvidia Rosa CPUs, and other compatible processors; and Nvidia NVHBM, which enables customized memory configurations and reserves more silicon area for compute.

Using Nvidia's NVLink Fusion platform for custom AI accelerators enables potential MediaTek customers to concentrate on their differentiated compute architecture while Nvidia and MediaTek provide connectivity, memory architecture, packaging, manufacturing, and rack-level technologies. Essentially, MediaTek's customers will get a pre-developed rack-scale platform for their custom AI accelerators, something they cannot get elsewhere. Since Nvidia tends to supply AI infrastructure platforms, not just AI accelerators, the deal with MediaTek fits perfectly into its strategy.

It should be noted that while hyperscalers like AWS, Google, or Microsoft can develop their own rack-scale solutions for AI and other workloads, smaller companies barely have enough resources to develop the whole rack-scale machine using off-the-shelf components.

"MediaTek is one of the world's great semiconductor companies, with exceptional expertise in system-on-chip design, connectivity, leading performance and power efficiency," said Jensen Huang, founder and CEO of Nvidia. "Together, we are building platforms that bring Nvidia accelerated computing to new markets and give customers the freedom to create differentiated AI systems at enormous scale."

The companies are also expanding their work on local AI computing. MediaTek previously collaborated with Nvidia on the GB10 Grace Blackwell Superchip powering DGX Spark. The collaboration was considered positive, so Nvidia and MediaTek now plan to cooperate on multiple generations of RTX Spark and DGX Spark processors for client systems, AI developer supercomputers, and enterprise workstations.

Finally, Nvidia and MediaTek will continue their multi-generation automotive collaboration. MediaTek's Dimensity Auto platforms integrate Nvidia AI technologies and RTX graphics for intelligent vehicle cockpits and can operate alongside Nvidia Drive AGX. Future generations will continue to wed MediaTek's automotive SoC expertise with Nvidia's accelerated computing, AI, graphics, and software technologies to build more advanced software-defined and AI-powered vehicles.

Following its announcement earlier this year, and just days after the DLL leaked inside NBA2K27, Nvidia has today confirmed that its controversial DLSS 5 will launch on September 3. Starting at 9 pm PT, the aforementioned NBA title will support DLSS 5 for all RTX 50 Series GPUs, laptops, and GeForce NOW. The company also shared its first in-house benchmarks of DLSS 5, which is expected to impact GPU performance.

As a series not primarily renowned for its graphical fidelity, NBA2K27 marks a fairly strange launch title for the tech, but Nvidia says that it has closely partnered with Visual Concepts and 2K to achieve "a new level of authentic sports experience for its players." The technology, which Nvidia officially dubs as 3D-guided Neural Rendering," will reportedly show players the natural passing of light through players' ears, light catching hair, and the natural lighting of skin "all while preserving the facial geometry scanned from real-world athletes."

DLSS 5 is not without controversy. As we explained during our first look at DLSS 5 back in March, DLSS 5's AI model "is trained to infer how certain complex features of game scenes like characters, skin, hair, and environmental lighting “should” look in the real world given certain inputs from the game engine (including, but not limited to, the color buffer and motion vectors) in combination with an input frame." While deep integration with a game's engine and assets should make it more consistent and predictable than run-of-the-mill generative AI imagery, that promise hasn't stopped some critics from raising severe objections to its premise since its initial reveal.

CEO Jensen Huang described DLSS 5 as "the GPT moment for graphics — blending hand-crafted rendering with generative AI to deliver a dramatic leap in visual realism while preserving the control artists need for creative expression.”

Its launch in NBA2K27 will mark the first time the technology debuts in a playable title, marking our first "fair" assessment of its capabilities.

DLSS 5 is very resource-intensive, with early testing and indications hinting at quite severe drops in frame rate when using the tech. As expected, Nvidia has confirmed DLSS 5 will only work with RTX 50-series GPUs and mobile GPUs, as well as through GeForce Now. Other older generation cards are left out in the cold for now. Along with the announcement, Nvidia has shared its first in-house benchmarks of DLSS 5 RTX Desktop performance.

While these are difficult to contextualize (they only show one game) and should be taken with the usual grain of salt for marketing material, they are interesting in one clear sense. Nvidia has pointedly only shared 4K results for its RTX 5090 and 5080, possibly hinting at the company's expectations of diminished results on anything less powerful, though cards lower in the stack do appear below 4K resolutions.

The recent DLL leak has led to some modders porting it to older RTX 40-series cards, or even older Ampere or Turing GPUs, with predictably varying results.

TIME published its 2026 TIME100 AI list last week, with one notable omission: Jensen Huang, one of eight TIME-designated “Architects of AI,” whose GPUs power the training infrastructure for all frontier AI models and whose company has just reported one of the largest sets of quarterly earnings in American corporate history. AMD CEO Lisa Su is missing too, while Ben Affleck, Senator Bernie Sanders, and Paris Hilton all managed to clear a bar that the heads of the industry’s two dominant GPU makers apparently couldn’t. Aside from TIME’s Architect of AI and joint Person of the Year designation for 2025, Huang has featured on every previous edition of the TIME100 AI. This year, however, Nvidia’s sole entry is Josh Parker, the company’s head of sustainability.

Broadcom CEO Hock Tan, Micron CEO Sanjay Mehrotra, SMIC co-CEO Liang Mong Song, and Huawei HiSilicon president He Tingbo all appear on the 2026 list, joined by Cerebras’ Andrew Feldman, CoreWeave’s Michael Intrator, and Nscale’s Josh Payne. Together, the roster includes Nvidia’s HBM supplier, two of its biggest GPU cloud customers, its custom-accelerator rivals, and China, which is trying — and so far struggling — to replace Nvidia altogether.

Parker’s profile, which appears under the Thinkers category, focuses on data center energy and water consumption, alongside Nvidia’s tripling of Scope 3 emissions in two years, and the Rubin platform’s shift to 100% liquid cooling. “Nvidia is at the center of the AI revolution,” Parker told TIME.

TIME editor-in-chief Sam Jacobs wrote in the list’s accompanying article that selection was led by editor Ayesha Javed, and that the 100 honorees represent the year’s “key storylines and who, in our opinion, are having the most influence in driving these developments.” Now in its fourth year, this edition of the TIME100 AI appears to have focused especially on featuring the new additions to TIME’s community of AI leaders, with Mark Zuckerberg, Demis Hassabis, Sundar Pichai, and Satya Nadella representing four other honorees, arguably deserving of a space on this year’s list, churned out.

Su’s strong TIME back catalog makes her omission all the stranger still. The magazine named her its 2024 CEO of the Year, put her on the 2024 AI list and the main TIME100 in 2025, and included her among December’s Architects of AI.

Nvidia’s second-quarter results, reported August 26, totaled $96.2 billion in revenue, including $89 billion from data centers, up 117% year-over-year. The company became the first to cross a $5 trillion market cap last October, and posted a $57 billion record quarter three weeks later.

Nvidia's current-gen Blackwell family includes some of the best graphics cards on the market with exceptional performance, but pushing silicon to its limit can always unlock more power. This is where enthusiast software, such as mVolt+, comes in, allowing users to overclock their GPUs beyond what's usually permissible. Unfortunately, it seems like a new driver update has shut down mVolt+ across all RTX 50-series cards.

Unlike mainstream GPU utilities like MSI Afterburner, mVolt+ bypasses standard driver restrictions and gives you more granular control over a few hardware blocks. The app can tweak separate power targets for the core and memory channels. It can also provide access to voltage and clock offsets for Core, XBAR, SYS, and VRAM. You can manually tune the power across the interconnect and video encoders, for instance, instead of relying on a global slider.

As such, users have been able to nudge their hardware into territory usually only charted by relying on a specialized VBIOS or physically modding the PCB. Last week, an Asus ROG Astra RTX 5080 reached 680W using mVolt+ despite even the official OC BIOS only permitting a 450W TDP. Someone with an RTX 5090 saw their GPU draw 650-700W of power while gaming, compared to 450W it used to suck up previously.

Even though the performance gains were marginal in real-world usage, mVolt+ was still a useful tool for overclocking. It allowed enthusiasts to chart on 3DMark's worldwide rankings just through software, so the fact it has suddenly stopped working is quite disappointing. The latest driver, version 615.56, seems to be blocking the app from making changes, hard crashing anytime you try to adjust the core power limit.

It's unclear if this is an intentional move from Nvidia, or just a compatibility issue that's preventing mVolt+ from properly working on the latest drivers. Some users on the Overclock.net RTX 5090 thread say their GPUs are still able to accept power limit modifications through Hydra 2.3B Pro — another extreme overclocking utility. That suggests mVolt+ wasn't explicitly blocked, and that its creator b00nz can push an update to patch this driver conflict soon.

The Trump administration is considering a new AI export control that would close a loophole in current U.S. trade policy toward China, that being the PRC's access to advanced AI compute through remote servers in nearby countries, reports The Information. The rule, should it go into effect, would target remote access through Thailand and Singapore, which aren't subject to the same export controls as China. The Department of Commerce could share the rule with trade groups to gather feedback as early as September, according to the report.

The U.S. government has struggled to reckon with AI export controls since overturning the Biden-era AI Diffusion Rule in early 2025. The AI Diffusion Rule is still in effect, though the Commerce Department has said it won't enforce the rule in the interim. In March, the Commerce Department issued a statement about a tiered licensing structure for advanced AI chip exports. The rule was revoked a little over a week later due to pushback from the U.S. AI industry.

This mismatch of Biden-era rules and Trump-era proposals has created uncertainty about the administration's plans to curb exports to China. Meanwhile, Taiwan has taken action against alleged smugglers of Nvidia's most advanced AI chips to China. In July, one Nvidia employee was detained in Taiwan on suspicion of falsifying documents, and just days ago, the Taiwanese government indicted nine people in relation to Supermicro servers being smuggled to China.

Export controls are mainly governed by 2022-era rules, which restrict Chinese access not just to advanced AI chips, but also advanced semiconductor manufacturing tools, such as EUV lithography machines. Current U.S. House members have called on the Trump administration to enforce the export controls that Joe Biden introduced as the end of his term in early 2025.

According to The Information, one of the driving forces behind the proposed rule is Moonshot's Kimi K3 model. Director of the White House Office of Science and Technology Policy Michael Kratsios posted on X in July, alleging that Moonshot used distilled U.S. models to train Kimi K3 through Nvidia-equipped servers in Thailand.

If the Commerce Department moves forward with the rule, it's sure to face legal challenges. An attorney at the firm Baker McKenzie told The Information that it's "widely acknowledged" that the Commerce Department can't enforce a regulation on remote access. The department instead has, traditionally, regulated the transportation of physical goods.

Still, the Commerce Department could crack down on remote access through other avenues. Namely, through know-your-customer checks. The Biden administration put these checks into effect with the Foundry Due Diligence Rule in early 2025, shortly before Joe Biden left office, but the Trump administration has said it won't enforce the rule.

When you serve a barely regulated emerging market worth trillions of dollars, you have to gear up your presence in politics beyond what usual lobbying or government affairs can do. This is exactly what Nvidia is doing by establishing its employees' federal political action committee (PAC), which will fund politicians whose positions are favorable to Nvidia's interests, reports Bloomberg.

The Nvidia employees PAC — which was registered with the Federal Election Commission on Thursday — will receive voluntary contributions from eligible Nvidia employees, who can provide up to $5,000 per year. The PAC will be permitted to contribute to federal candidates from both parties as well as party committees that express positions which align well with Nvidia's own goals, particularly in the fields of AI regulations, export controls, education, infrastructure spending, and workforce policy, just to name a few.

Nvidia reportedly told employees eligible to participate that decisions Congress makes over the coming years could have substantial consequences for the AI industry and everyday use of the technology. The company also noted that policymakers have increasingly focused on its industry and that decisions made in Washington affect both Nvidia's business and its customers' ability to obtain and deploy its technologies.

Nvidia has already substantially expanded its political operation. The company has spent more than $2.5 million on federal lobbying this year, an increase compared with the same period last year, according to lobbying disclosures seen by Bloomberg. In June, Nvidia hired Bruce Andrews, who previously headed government affairs at Intel and served as deputy secretary at the U.S. Commerce Department in Obama's government, as chief external affairs officer. In addition, Nvidia has hired five external Washington lobbying and government-relations firms to advocate for the company on a range of issues, including AI and trade policy, among others. The company also donated $1 million to the Trump Vance Inaugural Committee in January 2025.

Nvidia needs a PAC because lobbying and campaign contributions serve different purposes. The company can spend corporate money to lobby lawmakers, but it generally cannot use corporate treasury funds to contribute directly to federal candidates. Meanwhile, a company-sponsored PAC has a legal mechanism to collect voluntary contributions from eligible employees and direct that money to candidates and party committees from either party.

Meanwhile, unlike individual campaign contributions, donations made by the PAC will be publicly disclosed and subject to spending limits that do not increase with inflation. Furthermore, corporate PACs face growing resistance from lawmakers, according to Bloomberg. More than 270 House and Senate candidates have pledged to reject corporate PAC contributions this election cycle, the highest number since End Citizens United began promoting the commitment in 2018, Bloomberg claims. However, it is unclear whether these 270 are serious major-party nominees or current members of Congress, or some of the thousands of declared congressional candidates.

Nvidia is hardly alone in increasing spending on its influence in Washington, as most high-tech companies, now joined by AI giants, tend to spend millions on government relations and lobbying. The establishment of the PAC just highlights Nvidia's growing dependence on policies set in Washington.

Nvidia on Friday denied a report by the Wall Street Journal claiming that the company had put some transactions under its recently introduced 'take or pay' AI Compute Partnership on hold, less than two months after unveiling the initiative in early July and days before detailing the effort in its earnings call. The transactions were reportedly paused as some partners were irritated with Nvidia's attempts to influence their operations and because it raised internal concerns about potential antitrust scrutiny.

"The new business model we introduced in July that opens up compute access to the fast-growing AI ecosystem is still in place and continues to evolve due to high demand," a spokesperson for Nvidia told Tom's Hardware.

The report itself does not establish that Nvidia has abandoned the AI Compute Partnership program under which the company committed to rent capacity of newly built AI data centers as well as their minimum revenue, but claims that it put some deals on hold. Meanwhile, Nvidia's denial indicates that the program continues to exist, but is evolving, which means changing.

Per the report, it looks like Nvidia attempted to control how its 'AI Compute Partners' rented their capacity. The company told some cloud providers participating in the program that they could lease its GPUs only to customers approved by Nvidia, according to the WSJ report. The company also preferred to spread available capacity across multiple smaller AI companies instead of allowing a single large customer to take most or all of it. Some cloud operators reportedly pushed back against these restrictions, arguing that they should retain control over which customers they serve. Perhaps, in turn, Nvidia put some of the deals on hold.

While Nvidia does not lend any money or directly finance AI data center buildouts (which essentially means circular financing), it provides demand commitments and guaranteed revenue levels, which perhaps raised internal concerns about potential antitrust scrutiny. As a result, Nvidia could be revising the terms of the deals it inks with partners.

$36 billion of commitments

Modern AI data centers cost billions of dollars that must be spent on the premises, infrastructure, and compute hardware well before an operator has secured enough customer contracts to finance the buildout. Meanwhile, banks or infrastructure investors want confidence that enough of the future facility capacity will actually be rented. Under the program, Nvidia intends to use its own demand commitment on a portion of the facility's capacity in exchange for a percentage of the facility's revenue if demand is strong. This makes financing AI data centers easier as from the lender's perspective, part of the project's revenue stream is effectively supported by Nvidia rather than depending entirely on the operator's ability to find customers.

"Nvidia provides a take-or-pay commitment on a portion of the facility's capacity, a minimum revenue guarantee that gives lenders the confidence to underwrite the project, and in exchange, we share in a portion of the NeoCloud's revenue earned above that floor," explained Colette Kress, chief financial officer of Nvidia, during the company's earnings call. "In this model, we get paid twice, once on the hardware sale, and again through the share of rental revenue, a highly recurring stream layered on top of a one-time equipment purchase."

While actual percentages and economic terms have not been disclosed, it should work pretty straightforwardly. Nvidia provides a take-or-pay commitment that it rents, say, 30% of the capacity of a newly built facility and a minimum revenue guarantee over a period of six years. If the demand is strong and the facility rents 80% of its capacity, well exceeding the minimum revenue guarantee, Nvidia does not need to absorb the guaranteed capacity, and because revenue exceeds the agreed floor, Nvidia receives a percentage of the excess revenue. If the demand is weak and the facility can only rent 20% of its capacity, running well below the guaranteed revenue level, Nvidia's take-or-pay obligation would require it to cover the difference between actual revenue and the contracted minimum according to the specific agreement. Alternatively, Nvidia could rent back unused compute capacity for its own needs and cover the difference between the actual and guaranteed revenue level.

While at least some participants were reportedly irritated with Nvidia's alleged control of tenants, the program has proven to be quite a success so far. As of late July, just weeks after formally announcing the program, Nvidia had committed $36 billion in these new agreements that run for six years.

"Our commitments, which are typically six years in duration, totaled $36 billion as of July 26, 2026," an Nvidia filing with the Securities and Exchange Commission reads.

Nvidia has not disclosed which portions of monetizable capacities it typically commits, so it is impossible to figure out the value of the hardware it intends to supply under the $36 billion commitments.

DLSS 5 has apparently leaked, originating from inside a new game. This week, Renan Maniero discovered that NBA 2K27, which has entered early access, contains a new Nvidia library called nvngx_dlssnr.dll. The file, named Nvidia DLSSNR, appears to be associated with Nvidia’s DLSS 5 neural rendering technology, suggesting that NBA 2K27 will be among the first games to support it. Developers were quickly able to get DLSS 5 running in Control.

DLSS 5 is Nvidia’s upcoming neural rendering technology, first introduced in March of this year, which received a mixed reception from the gaming community. Nvidia announced that the technology would launch this fall, meaning its release is now fast approaching.

One of the developers behind RenoDX, a popular toolset for modding games, has been working to get DLSS 5 running in an actual game using the aforementioned DLSS 5 library. The developer successfully got the technology working in Control and applied it to the game's character models.

You can expand the tweet above to see DLSS 5 in action, with seven sliders controlling parameters such as style, intensity, tone, and structure strength.

According to the developer on the RenoDX Discord server, DLSS 5 currently runs only on Blackwell GPUs. On an RTX 5070 Ti at 4K, enabling the technology reportedly reduced performance from 71 FPS to 35 FPS. However, the developer did not specify the DLSS 5 settings or the internal rendering resolution used for the test, so it is not yet clear how representative this performance impact is.

It is important to remember that this is an early implementation of DLSS 5 injected into a game by modders. Therefore, it should not be taken as confirmation that Blackwell GPUs will be the only hardware supported at launch, nor should the performance shown here be considered representative of the final release.

That said, the performance impact is not entirely unexpected. When Nvidia first demonstrated DLSS 5 in March, the demo was running on two RTX 5090s. Since then, the model has been distilled into a smaller and more efficient version capable of running on a single GPU, according to Nvidia’s presentation at SIGGRAPH 2026. Nvidia may still have additional optimizations to implement ahead of the official launch, but we still expect DLSS 5 to be demanding to run in practice.

In terms of image quality, a user provided a couple of comparisons in the RenoDX Discord server we linked to previously.

In Nvidia’s initial reveal in March, many of the comparison shots demonstrated substantial improvements to effects such as subsurface scattering, contact shadows, light transmission through hair and foliage, and overall lighting. However, there were occasional instances where the neural rendering model deviated significantly from the intended artistic direction. At SIGGRAPH 2026, this no longer appeared to be an issue. The same appears to be true in the Control screenshots, where improved shadows and light transmission through the characters’ hair and skin make the scenes appear more photorealistic without altering the underlying geometry or the game's overall look.

DLSS 5 is set to launch soon, and while what we have seen today is only an early glimpse of the technology, it has certainly left us intrigued. Even in this early implementation, the improvements in lighting, shadows, and material rendering are impressive, particularly given that they can enhance a scene's visual fidelity without altering its underlying geometry or artistic style. Of course, the real test will come with the official release, where we will get a better idea of its image quality, performance requirements, and hardware compatibility. For now, however, DLSS 5 is shaping up to be a fascinating addition to Nvidia’s suite of neural rendering technologies.

Nvidia expects sales of the Vera Rubin platform to account for around 20% of its data center revenue in the third quarter of its fiscal year 2027, which will make it the company's fastest-ramping data center product in history. As Nvidia projects its revenue to be around $108 billion in Q3 FY2027, sales of Vera Rubin hardware alone will total around $20 billion in just one quarter.

"We see Vera Rubin accounting for about 20% of data center revenue in Q3," said Colette Kress, chief financial officer at Nvidia, during the company's earnings conference call with financial analysts and investors. "Having already received purchase orders from every major hyperscaler, AI cloud, and system OEM, we expect Vera Rubin to mark the fastest product ramp in Nvidia's history."

Data center revenue has accounted for around 92% of Nvidia's quarterly sales in recent quarters, so as long as Nvidia meets its $108 billion earnings projection in Q3, its data center revenue will reach around $99.36 billion, meaning that sales of its Vera Rubin platform hardware will be around $19.872 billion.

Nvidia began to ramp up production of Vera Rubin components — that include Vera CPUs, Rubin GPUs, BlueField 4 DPUs, and other units — this spring and commenced first revenue shipments of actual VR200 NVL72 racks in August, with Microsoft being the first to deploy commercial systems (at least according to Satya Nadella).

Normally, companies ramp up production and sales of their data center platforms for several quarters. Ramping up a data center platform from effectively no production revenue in Q2 FY2027 to 20% of revenue ($20 billion in this case) in just one quarter is a record not only for Nvidia, but perhaps for the whole industry, given Nvidia's scale. However, there is a catch: actual unit shipments do not look truly breakthrough.

Exact volumes of Vera Rubin units that Nvidia plans to ship in the third quarter of its fiscal 2027 are unknown. However, if Nvidia ships only VR200 NVL72 rack-scale systems, depending on their actual price and configuration (estimated from $5 million to $7.8 million per unit), Nvidia will supply from roughly 2,550 to approximately 3,975 machines containing 91,700 – 143,100 Vera CPUs as well as 183,500 – 286,100 Rubin GPUs.

These numbers are, of course, very rough since the company will sell a boatload of MGX servers and even Vera CPUs and Rubin GPUs separately, meaning that actual unit shipment volumes of these key components will be higher. Nonetheless, we are still talking about hundreds of thousands, rather than millions, of CPUs and GPUs, which is not particularly many. Still, given the stellar prices of leading-edge AI hardware, even relatively limited volumes of these components can generate tens of billions of dollars in revenue.

Nvidia has agreed to acquire Hugging Face for $12.9 billion, according to The Information, citing a person familiar with the deal. If the report is accurate and Nvidia indeed buys Hugging Face, the purchase could strengthen Nvidia's open-model strategy, provide another route to sell AI hardware, and help defend its hardware business as Anthropic, Google, OpenAI, and other major hyperscalers develop their own accelerators.

Nvidia sells hundreds of billions worth of AI hardware every year. Although the ubiquity of its CUDA software stack and leading performance of its hardware are the primary reasons why Nvidia's AI platforms are sold like hot cakes, another important factor is that many AI models were trained on Nvidia hardware and are optimized to run on it. Therefore, the more models trained on Nvidia hardware, the more products the company is going to sell eventually.

Hugging Face is an AI development platform best known for the Hugging Face Hub, a GitHub-like repository where researchers and developers publish, discover, download, and collaborate on AI models, datasets, and applications. Hugging Face also develops widely used software such as the Transformers library and provides tools and cloud services for training, optimizing, and deploying models on different types of AI hardware.

In addition to hosting models, datasets, and applications, Hugging Face provides software that helps developers optimize and deploy AI models on different CPUs, GPUs, and AI accelerators, while its Inference Endpoints service lets customers run models on managed infrastructure hosted by AWS, Google Cloud, and Microsoft Azure.

To make things simple, Inference Endpoints allows customers to select the provider, region, hardware type, and instance. What is important here is that the hardware used by Amazon, Google, and Microsoft is not all Nvidia. Hugging Face currently offers, depending on the provider, AWS Inferentia, AMD Instinct, Google TPU, Intel CPUs, and Nvidia accelerators, among other configurations.

OpenAI models, datasets, popular applications, and abilities to optimize and deploy AI models on different hardware make Hugging Face strategically important to Nvidia. On the one hand, the company can make the platform exclusively rely on its hardware, though this may face a backlash from the community, so this is something unlikely to happen in the short term (even assuming Nvidia is indeed set to buy Hugging Face). On the other hand, Nvidia wants open models to remain competitive with proprietary offerings from companies like Anthropic and OpenAI that may eventually get optimized for proprietary non-Nvidia hardware. Nvidia has been building its own Nemotron open models and has committed tens of billions of dollars to the effort. Furthermore, as Hugging Face grows, so is adoption of AI hardware in general and Nvidia hardware in particular.

Hugging Face is growing rapidly, but its revenue remains modest compared with the purchase price, according to The Information. The 10-year-old company recently reached approximately $150 million in annualized revenue, compared with about $100 million several months earlier, which puts Nvidia's price at roughly 80 times forward revenue, something that clearly highlights the strategic nature of the acquisition. Negotiations reportedly began after Hugging Face received acquisition interest elsewhere.

CEO and co-founder Clem Delangue said in June that paying subscribers doubled during the first half of 2026 and recently said the company was close to profitability. Demand has benefited from improving Chinese open models from Z.ai, Moonshot, and DeepSeek.

If Nvidia proceeds with the takeover, the transaction will be a part of Nvidia's increasingly aggressive investments across the AI ecosystem that spans from hardware to models to software. Last week, Nvidia agreed to pay $6 billion to license development technology from open-model developer Poolside and offered jobs to more than 100 employees. Nvidia also acquired Groq, Enfabrica, Essential AI, Illumex, and Kumo AI, just to name some.

It has become a tradition that every single quarter Nvidia reports record results that outpace all of its quarterly results before that. This Wednesday was no exception as the company posted revenue of $96.2 billion, which was up 106% year-over-year, due to rising demand for its AI hardware. But such results come at a cost, as the company has to invest massively in its future. In the second quarter of its fiscal 2027, Nvidia had to commit to procuring memory worth up to $160 billion, which includes its memory supply pact with SK hynix.

Nearly $100 billion revenue per quarter

For the second quarter of Nvidia's FY2027, which ended on July 26, 2026, the company's GAAP revenue hit a record $96.221 billion, up 18% quarter-over-quarter (QoQ) and 106% compared to the same quarter a year ago. Nvidia's net income totaled $59.688 billion, up 126% year-over-year (YoY), as its gross margin reached 75.0%. Sales of Nvidia's Compute & Networking hardware reached $88.299 billion, up 18% sequentially and 114% YoY, whereas sales of its graphics hardware hit $7.922 billion, up 12% sequentially and 46% year-over-year.

"AI has reached its inflection point," said Jensen Huang, founder and CEO of Nvidia. "AI is doing useful work. Its tokens are productive and profitable. Now, compute is revenue. And demand is accelerating. […] We have a golden age of new AI labs and startups, multiple frontier labs scaling in parallel, a thriving open-model ecosystem and physical AI coming online […]. The AI infrastructure buildout is at full steam. Vera Rubin, now in full production, was built to power exactly this moment."

Nvidia's results were driven by sales of its data center-grade AI hardware as various customers bought $89.023 billion worth of equipment, an increase of 18% sequentially and a rise of 117% compared to the same quarter a year ago. Hyperscalers purchased $48.710 billion worth of hardware from Nvidia (up 102% YoY and 13% QoQ), while revenue from AI Clouds, Industrial and Enterprise climbed to $40.313 billion (up 138% YoY and 25% QoQ), an indicator that while hyperscalers still purchase more equipment from Nvidia, the ACIE segment is growing faster. Sales of Nvidia's Edge Computing products were $7.198 billion (up 27% YoY and 13% QoQ), which means that sales of graphics products for PCs were strong despite shortages of GPUs and memory.

Commitments total $279 billion

Nvidia expects demand for its products to remain strong in the coming years. To meet that demand, the company increased its long-term purchase commitments from $119 billion in Q1 FY2027 to $279 billion in the second quarter. Typically, Nvidia's long-term supply commitments included pre-payments and commitments for wafer processing and advanced packaging at TSMC, as well as for HBM memory made by DRAM makers. This time around, Nvidia explicitly says that the bulk of the commitments are 'primarily related to the procurement of memory.'

Such huge commitments indicate that the company projects massive demand for its data center AI products in the coming years. During the conference call with financial analysts and investors, it indicated that its customer forecasts point to doubling demand next year, but Nvidia currently believes its supply chain can support about 70% growth.

"Even though our demand is much greater than 70%, our supply allows us to confidently deliver 70%," Huang said. "The unconstrained would be a lot, a lot higher. […] We have secured a lot of supply, but we just need a lot more."

To that end, the $279 billion supply commitment should be interpreted as not a precautionary inventory-building, but a strategic move to ensure shipment growth. Nvidia is effectively reserving memory and other capacity because it expects demand to exceed what the supply chain can deliver through at least the end of FY2028, as its management explicitly says supply will remain a bottleneck at least through FY2028.

$108 billion per quarter envisioned in Q3

For the third quarter of FY2027, Nvidia expects revenue of approximately $108 billion, ± 2%, with no data center compute revenue from China included in its outlook due to uncertainties with export and import licenses. The company projects a GAAP gross margin of around 74% and expects GAAP operating expenses of approximately $9.2 billion.

Nvidia's NVLink Fusion program gives the company's partners the building blocks necessary to connect custom chips with the NVLink scale-up domain used to join many separate processors into a single coherent system like the Vera Rubin NVL72 rack-scale accelerator. Today, Nvidia is adding a new building block to that toolkit: NVHBM, a custom implementation of the high-bandwidth memory that underpins practically every AI accelerator in use today.

As Nvidia describes it, NVHBM is a custom HBM base die that promises higher bandwidth, lower power usage, and a smaller on-die footprint than traditional HBM4e. Nvidia says it's designed and validated with "leading memory vendors," so it promises custom silicon developers faster time-to-market than implementing commodity HBM from the ground up. But it's worth re-emphasizing that this isn't an HBM replacement. Instead, it's a new building block that Nvidia is only offering to its custom silicon partners.

Memory bandwidth is everything for AI accelerators, and NVHBM promises up to 30% higher bandwidth per stack than standard HBM4e. For memory-bandwidth-bound AI workloads, that higher bandwidth translates into higher throughput, such as a higher tokens-per-second rate for AI inference.

The custom NVHBM base die also reduces the footprint of memory-related circuitry on the main custom accelerator die. Traditionally, the HBM memory controller has been incorporated into the primary silicon die on the package. NVHBM instead moves the memory controller into the base die of the HBM stack and provides a smaller custom PHY that NVLink Fusion customers can then integrate into their designs.

Nvidia says this approach frees up precious package real estate that can then be used for additional compute die area — up to 30% more compute on the primary silicon die. NVHBM further promises to simplify the interposer routing used to join multiple chips together for designs using advanced packaging techniques.

NVHBM also provides power savings versus off-the-shelf HBM4e stacks. As Nvidia has continuously hammered home in the Vera Rubin roll-out, every watt that isn't going into token production is a watt wasted. Nvidia says NVHBM uses 15% less power than commodity HBM4e, and that power savings can be banked for performance-per-watt improvements, reallocated into more functional units for a custom accelerator design, or translated into higher sustained performance within the same power budget.

Higher bandwidth at lower power is a huge win for AI accelerators that are moving massive data structures like model weights and KV caches around, especially when those savings are multiplied across many thousands of chips. The energy saved on data movement can be plowed back into higher performance from the accelerator itself or reallocated to support larger numbers of accelerators within the same fixed power envelope.

But these are, as of now, reasons for Nvidia's prospective partners to consider incorporating NVLink Fusion and NVHBM into their custom designs, not benefits that will materialize in the Rubin rack-scale systems already in production.

Along with NVHBM itself, Nvidia announced that Amazon's Annapurna Labs will be its first partner on NVHBM. , and Annapurna VP Nafea Bshara says: “We look forward to this technology collaboration to benefit future AWS infrastructure designs.” Annapurna's next-generation Trainium 4 AI chips will already support the NVLink Fusion scale-up interface, so it seems likely that follow-on chips will support NVHBM, too.

Groq's former chief architect stood on stage at Hot Chips 2026 and presented his former company's inference chip as Nvidia silicon. Igor Arsovski, now Nvidia's VP of hardware, presented the Groq 3 LPX rack's architecture and published the first third-party benchmark of the hardware: Artificial Analysis measured it at 3,431 output tokens per second on a 100K-context Gemma 4 31B reasoning workload, roughly four times the 870 tokens per second of the next-fastest public endpoint. Arsovski said the rack is already in production, built on the LP30 chip Nvidia obtained through its $20 billion Groq deal in December 2025, the same deal that pushed the Rubin CPX it replaced off Nvidia's roadmap.

SRAM without HBM

Artificial Analysis ran the comparison on a private, pre-release Gemma 4 31B endpoint served through Google Cloud, taking the median of 50 sequential client requests at a concurrency of one, while the public providers it measured against ran shared production serverless endpoints. Serving one request at a time produces the highest per-user token rate the hardware can post, and it's not directly comparable to the multi-tenant conditions the other endpoints run under.

Nvidia's on-stage demo showed a higher figure still, 10,996 tokens per second on the same 31B model, which Igor Arsovski, Nvidia's VP of hardware, flagged on stage as "self-reported" before telling the audience the aim was "third-party verified independent benchmarks that you guys can trust." Gemma 4 31B is also a dense model small enough to sit inside a single LPX rack, and the picture at trillion-parameter mixture-of-experts scale, where memory capacity becomes the main constraint, went unaddressed.

Each LP30 carries roughly 500MB of on-die SRAM and no HBM, so a full LPX rack of 256 chips holds 128GB of memory delivering 40 PB/s of aggregate bandwidth against 315 PFLOPS of FP8 compute, with 350 ns of chip-to-chip latency in a Vera Rubin-compatible, MGX liquid-cooled rack that scales past 1,000 LPUs.

Keeping model weights resident in SRAM rather than streaming them from HBM removes the memory-access latency that dominates single-token decode, and the design drops caches, branch prediction, and out-of-order execution in favor of a fully deterministic pipeline that the compiler schedules at clock-cycle granularity. The architecture descends directly from the Tensor Streaming Processor that Groq, founded by ex-Google TPU engineer Jonathan Ross, described in a 2020 ISCA paper titled Think Fast, the same title Arsovski and Raghavan reused at Hot Chips.

A Rubin GPU carries 288GB of HBM4, roughly 576 times the memory of a single LP30, so a 31-billion-parameter model at FP8 needs on the order of 62 LPUs to hold its weights, and a large mixture-of-experts model runs into four figures of chips across several racks. Capacity is the cost of the SRAM-only design, and it's why Nvidia is describing the LPU as for decode rather than as a general-purpose replacement for its GPUs.

Determinism lets the compiler predict power draw cycle by cycle, which Nvidia uses to pre-order current from the rack's regulators ahead of demand, cutting voltage droop by more than 60% and overshoot by more than 70% against an uncompensated load. The same per-block scheduling lets the hardware equalize heat instead of throttling to the hottest tile, which Arsovski put at roughly 10% to 11% additional performance under a fixed thermal limit. "By doing this, we can actually get more utilization of the chip under the same thermal limit, basically. So we can actually get, again, about 10 to 11% more performance under the same thermal limit. So this is another benefit of deterministic execution."

Across racks, Nvidia synchronizes chips to a single virtual clock in what it calls a plesiosynchronous network, with each chip acting as both processor and router so the fabric needs no adaptive routing or congestion sensing, and clock drift between chips is compensated at the chip-to-chip links. Asked during Q&A about the blast radius of a chip that fails mid-workload, Arsovski said users "would experience the exact same as any other hardware in the industry" and would "just checkpoint it or reconfigure the hardware."

Splitting inference with Rubin

Nvidia is pitching the LPX rack as a decode co-processor bolted onto Vera Rubin NVL72, with Rubin GPUs handling the compute-heavy prefill phase and building the KV cache while the LPUs generate output tokens. Nvidia showed three ways to divide the work: disaggregated prefill and decode; attention-FFN disaggregation, which keeps attention and its cache on GPU HBM while the LPU runs the feed-forward layers; and external-draft speculative decoding, where a small model on the LPU proposes tokens that the GPU verifies in parallel, with only draft tokens crossing the link.

An FPGA bridges the synchronous LPU domain and the asynchronous world of host I/O and GPU hand-offs, and Nvidia's Dynamo runtime, together with an LPU extension to CUDA, orchestrates the split. The company put the gains from these modes at roughly three-to-five-times over Rubin alone on a two-trillion-parameter workload with a 400K-token cached context, all Nvidia-measured.

Cerebras CS4

Cerebras used the same Hot Chips session to present its CS4 wafer-scale system, which chief system architect Jean-Philippe Fricker said runs up to 30 times faster than GPUs and doubles the token rate of the CS3 while carrying 10 times the token capacity. Each CS4 rack packs three wafer-scale engines into a new modular platform Cerebras calls Nexus, built around pluggable compute "backpacks" that separate power, compute, and I/O, and Fricker put its memory bandwidth at 43 PB/s, which he told the audience was "2,000 times higher memory bandwidth than Nvidia's next-generation Rubin chip." Cerebras also has a partner for the prefill side of the same problem: it agreed in July to pair AMD Helios GPUs for prefill with its wafer-scale engines for decode, the same division of labor Nvidia now builds in-house with Groq.

Nvidia pulled the Rubin CPX, its own GDDR7-based long-context accelerator, to focus on shipping the LPU this year, a decision VP Ian Buck laid out at GTC 2026. The $20 billion deal that produced the LP30 was structured as a non-exclusive IP license plus the hiring of Ross, president Sunny Madra, and most of Groq's engineers, a form that avoided a formal merger review. Arsovski opened the Hot Chips talk by calling it "a pinch me moment for the Groq team that's now integrated into the Nvidia group."

Senators Elizabeth Warren and Richard Blumenthal wrote to the FTC and to Nvidia in early 2026, arguing the arrangement acquired Groq "in all but name," and no formal, deal-specific investigation has been confirmed as of late August.

Full Nvidia Groq Hot Chips 2026 presentation

For as much as we might discuss the performance of an individual CPU, GPU, or other chip in a rack-scale AI system, the ultimate constraint on the performance of those chips is the amount of power one can get to the building and into each of the racks that contain them. The management and allocation of that power is a major concern for maximum productivity from a data center installation going forward.

During Nvidia's Hot Chips presentation on the Rubin GPU, the company emphasized this hard limit on data center capacity and touted the amount of compute that Vera Rubin NVL72 systems can deliver within an example fixed facility power budget of 100MW.

Nvidia says that the use of all of Vera Rubin’s power management technologies, in tandem with its DSX MaxLPS (Land, Power, Shell) suite of design and site-level dynamic power management resources, will allow operators to provision installations of 40,000 of those next-gen chips GPUs (or about 40 Rubin DGX SuperPODs) within that 100MW budget, and expects that hardware to deliver up to 2 zettaFLOPS (ZFLOPS) for NVFP4 inference and up to 1.4 ZFLOPS for NVFP4 training.

Doing some back-of-the-napkin math for ourselves from publicly available Rubin specs, we feel safe in assuming that those performance figures are estimated, not measured. The maximum number of achievable FLOPS from real-life workloads is likely to be significantly lower for a host of reasons.

But the overall point still stands: getting the most compute out of precious power budgets when planning the AI data centers of the future is going to require more refined planning, monitoring, and facility management than simply applying the coarse measure of estimated peak power draw for every electrical component in the facility. And Nvidia has those building blocks ready for data center constructors in the form of its DSX toolkit.

According to a companion blog post that Nvidia shared, as data center operators provisioned their facilities in the past, many of the assumptions they made around power usage focused on those fixed, worst-case power peaks per rack, potentially leading to inflated power budgets that end up stranding power allocation in racks that will rarely, if ever, use all of it.

In just one example, if there was an application load differential between racks in a cluster such that one system would benefit from having more power sent its way in that moment, it couldn’t be re-routed under a static provisioning scheme. The less-utilized rack would use less of its allocated power budget, and the more heavily loaded one might still run into the limits of an overly conservative guard band.

The DSX MaxLPS approach is meant to overcome the limitations of static power provisioning by instead applying an intelligent, dynamic scheme that is continuously aware of power usage at the chip level, rack level, and groups-of-racks level. Where unused power is available due to workload characteristics or idle capacity, Nvidia's Dynamic Power Software control loop can find and redistribute that energy to systems where it's most needed in the moment, maximizing the number of systems that can be installed and performance per watt from the facility over time.

In Nvidia's measured example of current GB300 racks, within a 540kW power budget and with static provisioning, an operator might be able to install four 135kW systems by statically provisioning for peaks rather than measured values from workloads. But in practice, as much as 170kW of that power budget might sit unused due to differences in rack utilization.

For modern rack-scale systems like Nvidia’s NVL72s, that’s an entire rack and change that could safely be installed within the same power budget, and indeed, that’s just what the company’s example shows. And across those five systems, the amount of reserve power allocated for peaks can be much lower.

At the rack level, DSX MaxLPS offers further flexibility through workload-specific power profiles. Much like the quiet, balanced, and high-performance power modes that client PC users are familiar with, Nvidia has produced rack-level power profiles that can be assigned to systems performing example workloads like inference, training, and more general memory-bound or compute-bound tasks.

As we noted, Nvidia didn’t share measured Rubin power or performance-per-watt results, but it has characterized the benefits of MaxLPS for prior-generation systems running inference workloads to prove the concept.

For a Grace Blackwell GB300 system running DeepSeek-R1, Nvidia says the past fixed-peak regime would have assumed a 1400W GPU TGP and an estimated rack power of 136kW. Applying MaxLPS, however, the typical GPU TGP under this workload falls to 1000W, and the total rack power falls to 101kW, all without affecting delivered performance.

That less conservative envelope translates directly into higher performance per watt, larger numbers of racks that can be installed within the same facility, and ultimately more tokens that can produce revenue for the data center operator or its tenants.

Nvidia further notes that designing a data center with MaxLPS from the start grants an operator greater flexibility over the life of the installation. For example, if a site starts as a training-focused facility outfitted with cutting-edge hardware, each installed system is likely to need a greater share of the available site power for that more intense workload, and so an operator might not want to populate every available floor space for those racks from the get-go.

But later in the life cycle, as training shifts to new generations of hardware and older systems transition into inference roles, the power demands of each GPU and rack will fall, and so a facility with dynamic power provisioning would be able to free up capacity that can then be used to install more hardware within the same facility and to generate more profitable tokens.

Another major component of MaxLPS in data centers deploying Vera Rubin hardware is the use of higher liquid coolant temperatures for the exclusively liquid-cooled Rubin NVL72 racks. Those systems are designed to work with 45 °C inlet coolant temperatures, much higher than for past liquid-cooled systems. We learned more about this “dry cooling” approach during our visit to Nvidia’s Vera Rubin proving grounds earlier this year.

The use of this higher coolant temperature for Rubin installations is important because the mechanical chillers used to shed waste heat in non-evaporative systems also consume a large portion of the site power budget – as much as 40% for past installations, Nvidia says. As with static provisioning for servers, the company notes that those chillers have traditionally been sized for the worst-case scenario that a facility might face, even if they’re operating well below that capacity for much of the year.

Again, this approach strands power that could be dynamically reallocated to compute given the proper operating conditions and site-level monitoring and management. Those chillers might still need to run during the hottest parts of the year, but outside of those conditions, the higher coolant temperature generally enables more power to be put to productive use, improving a site’s power usage effectiveness (PUE) figure, all else equal.

Power for AI data centers, whether generated by public utilities or behind the meter using alternative power sources, is expected to remain one of the most critical constraints for those facilities for the foreseeable future, and we heard that concern from multiple presenters during Hot Chips.

Nvidia’s DSX MaxLPS approach looks ready to provide the building blocks needed for dynamic allocation of that resource to extract the maximum possible performance per watt from Rubin facilities, and it reflects a comprehensive concern for the interplay of power and achievable performance that only seems likely to grow in importance going forward.

Just over one week after Nvidia agreed to backstop up to $105 billion in financing for its data centers, OpenAI arrived at Hot Chips on Tuesday with benchmarks claiming its first in-house chip beats Nvidia's GB300. Jalapeño, the inference ASIC OpenAI co-developed with Broadcom, delivered 1.5 times to 1.9 times more throughput per kilowatt and 1.7 times to 3.6 times lower end-to-end latency than Nvidia's GB200 and GB300 rack systems on SemiAnalysis's public InferenceX suite, with a 700W part going up against accelerators rated at 1,200W and 1,400W. OpenAI plans to begin deploying the chip in its own data centers later this year.

The tests covered three open models: GPT-OSS 120B, DeepSeek R1 670B, and Moonshot AI's 1-trillion-parameter Kimi K2.5, with OpenAI reporting its widest leads at low-latency operating points, where it claims 8.6 times to 104.3 times more throughput per kilowatt at the GB300's fastest previous time-between-tokens settings.

OpenAI normalized the results to each accelerator's published package TDP, though it said Jalapeño's measured sustained power stayed at or below 550W in testing. An appendix comparison using all-in utility power per accelerator, 1.18kW for Jalapeño against 2.55kW for the GB300, produces narrower gaps, as does pitting Jalapeño against a GB300 running multi-token prediction, where the peak efficiency lead shrinks to roughly 1.5 times.

Jalapeño wasn't tested against Vera Rubin, the Nvidia platform that's slated to power the first gigawatt of Nvidia systems OpenAI agreed to deploy in the second half of 2026. The chip also doesn't train models, the workload where Nvidia's hardware remains unchallenged. In addition, the major comparisons ran Jalapeño's single-token prediction against GB300 configurations doing the same, even though Nvidia deployments commonly use multi-token prediction in production. SemiAnalysis, which said it ran InferenceX with OpenAI engineers in the company's lab, described the part as "beating every Nvidia, AMD, and Google chip we have been able to test."

Each Jalapeño package, unveiled in June after a nine-month RTL-to-tapeout cycle, pairs its compute die with six HBM4 stacks, totaling 216 GiB at 15.4 TB/s. The GB300 carries 288GB of HBM3E at a 1,400W rating, so per watt of rated power, OpenAI's chip packs roughly 50% more memory. The company's Hot Chips presentation states that the main bottleneck its architecture targets is exposing aggregate HBM bandwidth, not adding more of it.

That memory is of course the tightest commodity in the semiconductor industry. Samsung, SK hynix, and Micron have sold their HBM capacity through 2027, a shortage so severe that Nvidia is reportedly testing cut-down Rubin Ultra configurations with as little as 192GB, and SK hynix CEO Kwak Noh-jung has warned that 2027 will be the worst year of the crunch. Micron told the same Hot Chips conference on August 23 that HBM consumes roughly three times the wafer area of DDR5 for equivalent capacity, a penalty that widens with each generation. Scaling Jalapeño across the 10GW deployment agreement that OpenAI signed with Broadcom last October would make the company a substantial new claimant to HBM4 supply, which Nvidia currently dominates through multi-year allocation deals with SK hynix.

A second-generation chip is approaching tapeout, expected within months, according to Bloomberg, and concept work on a third generation is underway. The first part reportedly uses a TSMC 3nm-class process, keeping OpenAI in the same wafer, memory, and advanced packaging queues as Blackwell and Rubin for the foreseeable future.

OpenAI is procuring those inputs while deepening its financial dependence on the company it just benchmarked. On August 17, Nvidia agreed to provide up to $105 billion in financing for an OpenAI-leased data center campus in Ohio. "Nvidia is a really good partner, and we continue to need a lot of Nvidia," Richard Ho, OpenAI's vice president of hardware, told Bloomberg in an interview following the announcement.

A Russian Molniya drone carrying an Nvidia Jetson Orin module crashed and killed three civilians at a gas station in Zaporizhzhia last month after choosing its final target without a human pilot, according to a New York Times investigation that cites Ukrainian air defense commanders, drone experts, and the forensic team that examined the wreckage.

Kateryna Bondar, a senior fellow at the Center for Strategic and International Studies, told the paper it's the first documented case of civilian deaths caused by a Russian drone using fully autonomous targeting. The dead were Tetiana Bubynets, a 19-year-old accounting student, and two men aged 41 and 48.

Human operators launched the drone toward the gas station, per the report, but the final aim point was selected onboard by software trained to recognize objects such as propane tanks. The aircraft failed to clear an apartment building on its approach, struck a wall, and detonated near people sheltering below, rather than striking its chosen target.

The strike drone flew in a group of roughly half a dozen aircraft, none of which emitted radio traffic, Ukrainian air defense officials said. Modules recovered from this attack and from intact test drones carried no encryption, so investigators could examine both the terrain imagery loaded for visual navigation and the code specifying which object classes the drone had been trained to attack. "Machines are making decisions to strike," Col. Serhiy Minaiev, Zaporizhzhia's air defense commander, told the NYT. Russia is understood to have begun AI-guided test flights of the mass-produced, fixed-wing Molniya in May, after months of self-targeting trials with the V2U.

Nvidia confirmed to the New York Times that modules photographed in the recovered drones were Jetson Orin minicomputers, hardware that sells for a few hundred dollars. "Our Jetson Orin modules are consumer-grade products sold to students, developers, and startups for a wide range of beneficial applications," an Nvidia spokesperson told Tom's Hardware. "They are not available in Russia and are not designed for military purposes. Pre-owned Jetsons are available through many reseller channels. Although we cannot track products after they are sold, if we determine that any customer is violating U.S. export controls, we will take appropriate action."

Ukrainian investigators have now linked the Jetson Orin to four Russian weapon families. A teardown by Ukraine's GUR intelligence agency last June found one seated on a Chinese Leetop A603 carrier board inside the V2U loitering munition, in combat use since February 2025. Weeks later, a Ukrainian general said the module powered object recognition in the upgraded Shahed MS001, and earlier this month Ukraine reported the same silicon in the S-71M Monochrome cruise missile. Data-center AI accelerators fall under U.S. export controls; edge modules like the Jetson Orin, with variants starting at $249, don't.

Mykhailo Fedorov, Ukraine's recently ousted defense minister, told the NYT that Kyiv tested its own fully autonomous system against fuel storage and military equipment in occupied Crimea in recent months, and Ukrainian quadcopters killed Russian soldiers in autonomous mode as far back as 2024. In Zaporizhzhia, defenses now include roughly 240 miles of anti-drone netting and plastic sheeting propped at odd angles around propane tanks to throw off image recognition.

Nvidia's 88-core Vera chip will be getting put to work for Elon Musk twice over, first and foremost as the standalone CPU orchestrating Grok's AI agents in SpaceXAI's data centers and, from the fourth quarter of 2027, inside the company's first Starmind satellite. Nvidia announced the deployment yesterday, making SpaceXAI the second hyperscaler after Meta to commit to Vera outside full Vera Rubin racks, with Musk supplying the launch window in a post on X. Nvidia claims the chip completes agentic, reinforcement learning, and data processing tasks up to 1.8 times faster than x86 processors, but that figure hasn't been independently verified.

Agents spend much of their runtime off the GPU. A model that writes code, for example, calls tools, queries databases, and parses results by leaning on the host CPU between every inference pass; idle GPUs waiting on that work are wasted capital. "Vera gives us the CPU performance and memory bandwidth to run enormous amounts of orchestration, code, and data processing while keeping GPUs doing what they do best," Mike Nicolls, president of SpaceXAI, said in the release.

Meta was the first company to announce a large-scale standalone Vera deployment, confirming in February that it would run Grace-only servers in production with Vera to follow as soon as 2027. Nvidia has since disclosed shipping hundreds of thousands of standalone Grace servers and more than 2.5 million Grace CPUs in total, and the SpaceXAI deal extends that dominance directly into territory held by AMD's EPYC and Intel's Xeon lines.

Vera pairs 88 custom Olympus cores on a monolithic die with spatial multithreading and LPDDR5X memory delivering up to 1.2 TB/s of bandwidth. Nvidia's performance claims for the chip remain contested, and AMD has countered that its 256-core Zen 6 Venice processors beat Vera by 3.3 times in rack-level performance. Neither company disclosed how many Vera CPUs SpaceXAI will deploy, the value of the deal, or when the data center rollout begins; the only date attached to the announcement is the satellite launch window.

Musk said earlier this month that Starmind launches would begin in 2027, and his X post following the press release narrowed that to the fourth quarter, with what he described as significant scale-up following in 2028. The terrestrial NVL72 combines 72 Rubin GPUs and 36 Vera CPUs in a fully liquid-cooled rack, and an orbital version has to be reworked for radiation exposure, heat rejection through radiators rather than facility water loops, launch vibration, and the absence of any hands-on servicing.

SpaceXAI's first-generation AI1 satellite design carries a 120 kW compute payload, peaking at 150 kW, on a craft wider than a Boeing 747. The company has also acknowledged that orbital compute at the scale it's targeting requires significantly more chips than it currently has access to, a constraint the Vera Rubin commitment doesn't itself address.

Nvidia has told some of its largest customers that the prices of servers containing its AI chips will rise by more than 15% in many cases, Bloomberg reported on Saturday. The increases will take effect on Grace Blackwell and Vera Rubin systems shipping early next year, according to people familiar with the matter, who commented on communications that have not yet been made public. The size of each increase will depend on the chip generation and the memory configuration involved. Companies that build servers under contract for large data center operators, including Microsoft, Google, and Oracle, have recently notified their customers of the forthcoming increases, the people told Bloomberg.

This is another example of the so-called "RAMageddon" that's gripping the DRAM market, with contract prices having risen at record rates this year. Analysts projected that conventional DRAM contract prices would climb 58% to 63% quarter-over-quarter in Q2 2026, following a Q1 surge of 90% to 95%, as suppliers reallocated capacity toward HBM and server products. SK hynix said in October last year that it had already sold out its entire 2026 memory production capacity, and Samsung and SK hynix raised 2026 HBM3E supply prices by close to 20% before the year began.

AI systems carry enormous memory loadouts, with Nvidia's Rubin GPU shipping with up to 288GB of HBM4 per package, and the NVL72 rack-scale system combines 72 of those GPUs, putting more than 20TB of HBM in a single rack before accounting for the LPDDR attached to its Vera CPUs. With HBM production consuming roughly four times the wafer area of equivalent conventional DRAM, memory has become one of the largest line items in an AI server's bill of materials, and it's continuing to rise at a stratospheric pace.

Ironically, the supply crunch that's now inflating Nvidia's systems is the same one its demand helped to create. The three major memory makers spent this and last year shifting advanced nodes and new capacity toward HBM and high-capacity server DRAM, starving commodity markets in the process. Consumer DDR5 pricing has more than doubled since late 2025 as a result, with a mainstream 32GB DDR5-6000 kit selling for around $392 in August against $110 to $140 a year earlier, according to our RAM price tracker.

Nvidia has already passed rising costs through to consumers, raising prices on GeForce graphics cards earlier this month. The Bloomberg report indicates the same unrelenting pressure has now reached the top of the Nvidia stack, where hyperscalers as well as PC builders will be absorbing the increase. A 15% rise on rack-scale systems that sell for several million dollars each adds hundreds of thousands of dollars per rack across deployments that run to thousands of racks.

Nvidia runs a gross margin of roughly 75% non-GAAP, among the highest in the semiconductor industry, and the reported hikes indicate the company intends to pass memory cost inflation on to customers rather than absorb it, which it can more than afford to do. Meanwhile, supply of its accelerators from TSMC still can't meet demand, which limits buyers' immediate leverage.

Whether the increases push hyperscalers further toward AMD's accelerators or their own custom silicon will depend on how quickly those alternatives can absorb displaced demand, and all of them draw HBM from the same three constrained suppliers.

ByteDance and Tencent each took delivery of roughly 10,000 Nvidia H200 accelerators in recent weeks, and a handful of other Chinese tech groups may soon receive approvals of similar size. The deliveries are the first meaningful movement of the chips into mainland China since President Trump cleared their export in December, but they arrive under strict oversight from China’s National Development and Reform Commission, which approves each purchase individually.

Most of each company's U.S.-licensed allowance, understood to be up to 100,000 units apiece, must stay outside the mainland, largely in Hong Kong. Measured against the 400,000-plus units that ByteDance, Alibaba, and Tencent were collectively approved to buy in January, the chips now on the mainland amount to roughly 2.5% of the order book.

Two licensing regimes

Trump approved H200 exports in December last year, in exchange for a 25% cut of every sale to the U.S. Treasury, and terms formalized in January require each chip to pass through US territory for third-party inspection before re-export. The Commerce Department moved license applications to case-by-case review on January 16 and had cleared roughly 10 firms by mid-May, including Alibaba, ByteDance, Tencent, and JD.com, with Lenovo and Foxconn approved as distributors.

In response, China built the NDRC’s per-purchase approval process from scratch to mirror the Commerce Department’s case-by-case license review. The 10,000-unit mainland allocations function as quantity caps, the very instrument that U.S. export rules have used since the first Hopper restrictions in 2022. The requirement to route imports via Hong Kong operates as an end-location condition, identical to Washington's demand that every chip transit U.S. soil for inspection.

The Cyberspace Administration of China summoned Nvidia last July over alleged backdoors in the H20. State media outlets subsequently ran a campaign calling the chip unsafe and outdated, and state-funded data centers were barred from foreign accelerators. Eight months of NDRC silence on H200 orders left Jensen Huang telling investors Nvidia's China market share had gone from 95% to zero.

DeepSeek’s training bottleneck

A transcript of DeepSeek founder Liang Wenfeng's May 20 closed-door investor meeting, leaked online in July, arguably explains why Beijing is letting any Nvidia silicon in at all. According to the document, whose authenticity DeepSeek hasn't confirmed, Liang told investors he wanted 200,000 Huawei accelerators to train a frontier model and received an allocation of 16,000, against total Huawei capacity of roughly 750,000 chips this year split across every Chinese AI company, a constraint he reportedly expected to persist for around three years. The remarks circulated widely enough that DeepSeek paused a fundraising round targeting a roughly $71 billion valuation days after they appeared.

If we look at DeepSeek’s production history, it appears to match the numbers Liang cited during the meeting. lab's attempts to train its R2 model on Huawei Ascend hardware failed repeatedly, and training moved back to Nvidia chips while Ascend accelerators handle inference. Per the Financial Times’ unnamed source, which broke the story of resuming H200 imports, domestic silicon increasingly serves inference, while Nvidia hardware still carries training.

The H200 obviously fills that gap nicely, with each unit carrying 141GB of HBM3e at 4.8 TB/s, delivering roughly six times the performance of the H20, and approaching the banned H100. A 10,000-GPU cluster is genuine frontier-training capacity, comparable to the builds behind the GPT-4 generation, though it represents a fraction of the 100,000-GPU-plus systems U.S. labs now run. That ratio seems to have been precisely calibrated by Beijing officials, large enough to keep flagship labs training their models, but small enough that inference stays a captive market for domestic chipmakers.

Domestic supply gaps

TrendForce's August 10 supply chain survey projects that domestic chips will take nearly 90% of China's high-end AI chip market this year, with domestic high-end shipments growing 83% year over year, a projection that TrendForce itself revised up from roughly 50% in its December outlook. Bernstein has recorded the same displacement from the other direction, with Nvidia's China share falling from 66% in 2024 to 40% in 2025 and a projected 8% this year.

Huawei planned to roughly double output of its 910C Ascend chip to about 600,000 units in 2026, against a total Chinese accelerator market that ran to roughly 4 million units in 2025, 2.36 million of them supplied by Nvidia and AMD. So, while domestic chips can cover the volume, they can't yet cover frontier training, making the 90% projection and H200 easing two halves of the same policy.

Washington's case for export controls rests on exactly the dependence these deliveries demonstrate: Four years into the restrictions, China's leading labs still can't train frontier models without American silicon, and Beijing has now conceded as much through its licensing decision.

The leaked transcript has Liang arguing that open access to Nvidia would make domestic substitution a much harder commercial proposition, meaning the controls themselves built the market Huawei and Cambricon now hold, and TrendForce's numbers show that market approaching 90% share three years after the first Hopper bans. This month's deliveries disprove neither side's theory, but Nvidia does bear the cost of both, with 500,000 chips reportedly in inventory, a 25% fee on anything that sells, and a Chinese market rationed to 10,000 units per buyer — admittedly, that’s better than zero.

Nvidia has rejected a report claiming that it plans to begin small-batch shipments of a language processing unit tailored for Chinese customers by the end of 2026, with several Chinese orders already placed. "The reporting in The Information on NVIDIA's LPU is incorrect. We have no LPU sales in the China market today, and no China-specific LPU product in our roadmap," an Nvidia spokesperson told Tom's Hardware on Thursday. The Information's story, which cited two Nvidia employees, said the chip is a variant of the Groq 3 LPU Nvidia announced at GTC in March, and that its silicon is unchanged because it already falls within U.S. export rules.

The LPU was designed as a decode co-processor for the Vera Rubin platform, and Vera Rubin can't be sold in China. The Information's sources said Nvidia rewrote the software that splits work between the GPU and the LPU so the accelerator can run alongside processors that are available in the country.

The publication said Nvidia didn't respond to requests for comment over several days before publishing, and that it's unclear whether Beijing would allow the orders to proceed. Chinese officials blocked purchases of the H20 last year and only recently told companies they'd permit some H200 imports, so U.S. compliance alone doesn't guarantee the chips can be delivered.

Back in March, it was reported that Nvidia was preparing LPUs for China, with Jensen Huang saying two days later that the story was "totally false.” Thursday's statement is narrower than Huang's, addressing current sales and a China-specific product. Nvidia hasn’t clarified whether the standard LPU will ship to Chinese buyers. Huang told CNBC in May that Nvidia had "largely conceded" China's AI chip market to Huawei.

The Groq 3 LPU is built on Samsung's 4nm process with 512MB of SRAM per die and no HBM, and Nvidia said at GTC that it would ship in Q3 2026 to customers including OpenAI. U.S. export thresholds for China are set on compute density and bandwidth, and an SRAM-only decode accelerator with no HBM stack is the kind of part that can still be exported under them without a cut-down SKU, which is the mechanism The Information's sources described.

Huawei's Ascend 950DT, which the outlet named as the LPU's direct competitor, is optimized for decode and training and is due in Q4 2026, with the prefill-focused 950PR already in production since April. ByteDance and Tencent each took delivery of roughly 10,000 H200s in recent weeks, according to a Financial Times report this week, the first meaningful Nvidia accelerator volume to enter mainland China since December's U.S. approval.

An AI-themed bar in Beijing's Zhongguancun tech hub hands out free, unlimited DeepSeek tokens with its drinks, running inference locally on two Nvidia DGX Spark mini-PCs kept on display. The AGI Bar, opened last year on Haidian district's Inno Way startup street by Song De, an independent AI developer in his thirties who runs it in his spare time, sells a signature 9.9 yuan ($1.50) glass of foam, also named AGI, and lets anyone on the WiFi code use its house AI agent at no charge. Song has told Reuters the bar is "completely losing money," with roughly 10 times more drinks given away than sold.

The venue sits a short walk from Tsinghua and Peking universities and the Beijing offices of DeepSeek and Microsoft, has hosted parties for Chinese AI labs including Z.ai, and keeps the gong Z.ai struck for its January Hong Kong listing outside the front door. Its registered Chinese name translates to "knowledge distillation," a pun that works equally well for liquor and LLMs.

According to the report, much of the bar's operations have been automated, with AI agents taking care of inventory, reservations, and memberships. The owner is set to introduce humanoid robots later this year.

Each Spark pairs a 20-core Arm CPU with a Blackwell GPU on Nvidia's GB10 and carries 128GB of unified LPDDR5X, enough, per Nvidia, for models up to 200 billion parameters at FP4. Linking two units over their ConnectX-7 NICs pools 256GB. DeepSeek's V3 and R1 weigh in at 671 billion parameters, and V4 runs to 1.6 trillion, so whatever flows over the bar's WiFi is a distilled or aggressively quantized cut of the model rather than the real thing. The showpiece hardware isn't cheap either: after Nvidia raised the Founders Edition price 18% to $4,699 in response to memory shortages, a matched pair costs about $9,400 before a single free token is poured.

DeepSeek suspended its second fundraising round in late July, days after remarks attributed to founder Liang Wenfeng went viral on Chinese social media. The round had targeted a pre-money valuation of roughly 480 billion yuan, or about $71 billion.

Reports citing Chinese outlet Yicai say the leaked meeting minutes had Liang discussing DeepSeek's continued reliance on Nvidia chips and estimating that China trails leading U.S. labs by 12 to 18 months on around one-twentieth of their compute; Bloomberg, which originally covered the leaked transcript, said it hasn't verified its authenticity.

Those admissions reflect badly on Beijing's push to wean its AI sector off American silicon, an effort that last year saw DeepSeek reportedly urged to train R2 on Huawei's Ascend hardware before repeated failures sent training back to Nvidia GPUs. Two American Blackwell boxes displayed as a Beijing bar's main attraction make for a slightly off-message shrine.

ByteDance and Tencent have each taken delivery of roughly 10,000 Nvidia H200 accelerators in recent weeks, the Financial Times has reported, citing two people with knowledge of the matter, a development that would mark the first meaningful movement of the chips into mainland China since President Trump approved their export last December.

Beijing wants most of each company's U.S.-licensed allowance, which the FT puts at up to 100,000 units apiece, kept off the mainland, with regulators directing buyers toward Hong Kong instead. Nvidia is reportedly holding around 500,000 H200s built largely for Chinese customers. Trump cleared H200 exports to approved Chinese customers last December, in exchange for a 25% cut of every sale to the U.S. Treasury, and Washington had licensed roughly 10 firms, including Alibaba, ByteDance, Tencent, and JD.com, by May.

However, Beijing never let orders flow: every purchase requires case-by-case approval from the National Development and Reform Commission, and the resulting blockade left Jensen Huang telling investors that Nvidia's China market share had fallen from 95% to zero. As recently as mid-July, a U.S. trade official told a congressional hearing that only a very small quantity of chips had shipped against the licenses. Lenovo and other Nvidia partners told Chinese customers last week that they could resume orders for H200-based AI servers, per the FT, though each purchase still needs separate NDRC sign-off.

And just because chips are moving doesn’t mean the infrastructure is there to support them. The H200 draws up to 700W, with a fully loaded eight-GPU HGX H200 server pulling roughly 10 kW. At that rate, a single company's 100,000-unit allowance works out to about 12,500 servers and 125 MW of IT load, and Nvidia's reported 500,000-unit stockpile translates to roughly 625 MW.

Hong Kong's entire installed base runs to 47 data centers totaling about 581 MW, per Hong Kong Free Press, and the city's average PUE of 1.62 pushes actual grid draw well above the IT figure. "It's a dilemma," a person familiar with the situation told the FT, with companies unable to find anywhere in the territory to deploy the chips. The Northern Metropolis data center cluster meant to fix the shortfall, awarded to Range Intelligent Computing in March, isn't slated to come online until 2029.

Chinese labs increasingly run inference on domestic accelerators but are still thought to train frontier models on Nvidia hardware, and DeepSeek's failed attempts to train its R2 model on Huawei Ascend chips show why.

The 10,000-unit deliveries amount to roughly 2.5% of the 400,000-plus H200s that ByteDance, Alibaba, and Tencent were collectively approved to buy in January, but the NDRC still gates every order, and steering the bulk of licensed volumes to a territory that can't house them keeps the mainland market captive for domestic chipmakers.

Computer enthusiast 1usmus has developed a new update for their Hydra overclocking application that introduces VRAM overclocking and power limit control for some of the best graphics cards on the market, Nvidia's RTX 50-series. The new update, 2.3B, adds support for a maximum memory offset of +3000MHz for memory overclocking and a static power limit adjustment that peaks at 125% regardless of which RTX 50-series model is used.

The new update allows RTX 50-series graphics card owners to overclock their GPU’s GDDR7 memory up to 36 Gbps. 1usmus showed off their personal Asus ROG Astral LC RTX 5090 OC overclocked to 36 Gbps on the memory to confirm that the application works as advertised. Hydra’s aforementioned limits are reportedly aimed at this first release specifically to keep users safe. But 1usmus hinted that future iterations of the app could have higher limits, noting that the RTX Pro 6000 has experimental support for the “complete feature set” of the program.

Beyond VRAM and power limit adjustments, Hydra version 2.3B also features direct XBAR offset control and support for second-generation “Rail Offsets.” The voltage frequency curve has also been changed and is now unrestricted compared to prior versions.

Compared to mainstream overclocking tools, Hydra supports more in-depth overclocking features that are geared for extreme overclocking. This is particularly true of the XBAR offset control, which is a method of overclocking an Nvidia GPU’s interconnect, helping improve performance beyond just GPU core and memory overclocking, similar to overclocking the Infinity Fabric on AMD CPUs. For pure core and memory overclocking, Hydra’s latest update matches what existing overclocking tools can achieve, such as MSI Afterburner, for now.

For the uninitiated, Hydra is a tool 1usmus released several years ago to better tune Ryzen CPUs. Since then, the app has evolved from just being an advanced AMD overclocking utility to also featuring overclocking support for Nvidia GPUs, overclocking support for Radeon GPUs, DRAM overclocking, and memory stability testing.

Nvidia’s $5 billion purchase of Intel stock last year, made as part of the companies’ strategic AI infrastructure partnership announced in September, has become a highly lucrative investment, generating nearly $25 billion, the company revealed in an SEC filing this week. In addition, Nvidia owns nearly $21 billion worth of SpaceX stock and holds stakes valued at more than $10 billion in various customers, partners, and suppliers.

With quarterly revenue exceeding $80 billion and net income approaching $60 billion, Nvidia has plenty of unspent cash to invest. Traditionally, the company invests in stocks poised to grow and makes strategic investments.

Nvidia's investment in Intel was both strategic and financial, helping Intel survive hard times and generating $24.989 billion for the company. Interestingly, after investing in Intel, Nvidia has sold its 1.1 million Arm shares (worth $178.1 million last August). Without any doubts, Nvidia will continue developing Arm-based CPUs, though for now it does not own any Arm stock.

The SpaceX investment — valued at $20.975 billion — seems entirely strategic at present, since SpaceX's xAI has committed to exclusively using Nvidia hardware in its AI data centers both on Earth and in orbit. Once SpaceX's stock regains its lost value, Nvidia may well earn on it, though it remains to be seen when this happens.

Other notable investments that Nvidia has made over the past year include Coherent, a major maker of lasers, optical materials, and semiconductors; Nokia, a telecommunications giant; and Synopsys, an electronic design automation (EDA) provider.

Coherent is expected to make Ultra-High-Power Continuous-Wave (UHP CW) lasers for Nvidia's next-generation data center platforms relying on co-packaged optical (CPO) interconnects, so Nvidia invested $2 billion in the company earlier this year. Since then, the stock has almost skyrocketed.

Something similar happened to the Nokia investment. Last October, the company announced plans to invest $1 billion in Nokia to accelerate AI-RAN innovation and lead the transition from 5G to 6G. By now, the shares that Nvidia owns are worth $2.2 billion.

Synopsys has been aggressively adding artificial intelligence capabilities for its tools, so to support the company, Nvidia acquired $2 billion worth of Synopsys stock last December. Right now, the stake is valued at $2.15 billion, making it a profitable investment for the AI hardware giant.

In addition, Nvidia continues to own stock of its clients, but the picture is different for CoreWeave and Nebius. Last year, the company owned 24.277 million CoreWeave shares worth $3.959 billion. Nvidia now owns 47.213 million shares of CoreWeave valued at $4.699 billion, which essentially means that the company substantially increased its position as CoreWeave's stock price declined. As for Nebius, Nvidia's position remained at 1.19 million shares, but while the stake was worth $65.869 million in 2025, its value has since surged nearly fivefold to $328.77 million.

Elon Musk told employees of SpaceX that the power capacity of the company's xAI data centers will increase by 7x to 10GW by late 2027. If this happens, the company's data centers will bring the company some $300 billion – $500 billion in revenue per year, according to Musk. The claim comes as SpaceX's market capitalization dropped by nearly $570 billion in less than two months. Meanwhile, the combined performance of the cluster will by far outpace not only all supercomputers in the Top 500, but also all AI clusters running today.

"We have already built the most powerful AI training clusters in the world," Musk told SpaceX employees at a meeting. "What we expect to do by the end of next year is about 10 times more than what we have done thus far. […] So, we are aiming to get to 10 GW [of compute] by the end of next year. […] If the value per watt is probably going to be $30 to $50, which means if we bring 10 GW of AI compute online by the end of next year, it will be $300 to $500 billion a year in revenue. Big numbers."

A lot of power

At present, SpaceX's xAI data centers in Memphis and Southaven have a rated power draw of 1.4 GW. The company plans to increase the electrical capacity of its data centers to 10 GW by the end of 2027, or by around sevenfold in roughly 1.5 years. It should be noted that AI infrastructure with a 'nameplate power draw' of 1.4 GW by far does not offer compute capacity of 1.4 GW.

A large AI data center with a power usage effectiveness (PUE) of roughly 1.2 would have around 1.17 GW available to IT equipment (i.e., 230 MW is used by cooling, pumps, fans, humidification/dehumidification, lighting, power distribution losses, UPS losses, and other facility systems). Not all of that 1.17 GW goes to AI accelerators: CPUs, memory, networking, and storage consume a meaningful share. If perhaps 70% – 80% of IT power ultimately corresponds to accelerators, we might be looking at roughly 0.8 GW – 0.95 GW of accelerator power in the case of a 1.4 GW data center.

Loads of FLOPS

Compute capacity is not measured in Watts; it is measured in floating-point operations per second (FLOPS). Keeping in mind that currently xAI uses a mix of Hopper- and Blackwell-based accelerators, it is hard to determine how much compute xAI has today. Since xAI seems to be betting primarily at Nvidia's Vera Rubin systems from now on, we can make a more or less educated guess about the company's Rubin-based compute capability the company will have by the end of 2027.

Assuming that all of the new 8.6 GW nameplate power draw will be based on Nvidia's NVL72 VR200 rack-scale systems and the PUE of around 1.2, the IT power budget of the new capacity will be 6.88 GW. Actual Rubin AI accelerators will get between 4.816 GW and 5.504 GW of power depending on how much of the IT power will correspond to these GPUs. Each Rubin GPU is expected to consume 2.3 kW of power in Max-P configuration. As a result, xAI's clusters will house between 2.094 million and 2.393 million Rubin GPUs in Max-P mode, or 29,083 and 33,236 NVL72 VR200 systems.

The performance of the NVL72 VR200 system is well known, so depending on the number of these machines that xAI will deploy by the end of 2027, we are looking at rather formidable numbers. NVFP4 inference performance of the cluster will be between 105 and 120 ExaFLOPS; NVFP4 training performance will range from 73 to 84 ExaFLOPS; FP6/FP8 training capability is projected between 37 and 42 EFLOPS, whereas native FP64 compute will total 70 – 80 EFLOPS. Of course, we are dealing with very rough numbers here as some systems may not work in Max-P configuration.

To put the numbers into context. The total combined FP64 performance of all systems on the Top 500 list is 18.73 EFLOPS. xAI will have 3.7X – 4.3X more than that if the cluster is deployed. As for AI performance, 105 – 120 NVFP4 EFLOPS inference and 73 – 84 NVFP4 EFLOPS training put this cluster in a whole different league from anything publicly operating right now, meaning that we are talking about dramatically more sophisticated AI models coming. Whether or not the combined xAI compute capability will enable the company to earn $300 billion – $500 billion per year is something that remains to be seen, as SpaceX is not the only company selling compute capacity to AI companies, and the competition will likely be rough.

Yet, it is about time for Musk to make comments like this, as after topping $2.44 trillion in market capitalization on June 20, SpaceX dropped to $1.43 trillion on August 1, but rebounded to $1.87 trillion on August 12.

Nvidia late on Monday announced that it had signed memorandums of understanding with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR to establish independent financing platforms that could mobilize more than $500 billion in third-party capital to invest in AI infrastructure. Nvidia's goal is to ensure that its clients building AI data centers (which Nvidia calls AI factories) can get enough money from powerful financial companies. As a result, Nvidia will reinforce its position on the AI hardware market as the funds will exclusively finance Nvidia-based AI data centers.

The proposed funds (or platforms, as Nvidia calls them) are intended to provide dedicated pools of capital for customers — such as AI labs, cloud service providers, or enterprises — that deploy Nvidia-based infrastructure. Rather than financing projects itself, Nvidia intends to work with six investment firms to enable access to long-term funding at attractive rates. The company believes that AI infrastructure should not be viewed as conventional IT equipment, but as tools that make sustained economic returns, which is why it must be financed appropriately.

"We are in a pivotal moment of a historic AI investment cycle," said David Solomon, Chairman and CEO of Goldman Sachs. "Nvidia's full-stack platform is in high demand and uniquely positioned at the center of that global buildout. Our investment and distribution roles reflect our confidence in Nvidia's leadership, and we are excited for the new opportunity to create a market for credit backed by NVIDIA compute."

The financial companies believe that AI data centers can be treated as long-duration infrastructure assets rather than conventional IT equipment, in part because Nvidia compute can generate revenue over an extended period and retain value across different workloads and operators. As a result, they appear to believe that AI infrastructure can support long-term financing at attractive rates, although the companies do not explicitly claim that financing AI data centers carries lower credit risk than financing conventional IT deployments. Furthermore, it should be noted that Nvidia and financial companies will inevitably finance companies that would otherwise struggle to obtain capital to finance their AI data centers. This will ultimately help Nvidia sell more hardware and software while allowing its financial partners to capitalize on the rapid expansion of Nvidia's AI ecosystem.

Without any doubt, the arrangement will help to rapidly build AI infrastructure, which will increase adoption of AI technologies. However, this arrangement increases the risk of an AI infrastructure bubble as it potentially weakens one of the natural brakes on overbuilding: the availability and price of capital. Furthermore, Nvidia's help with arranging financing for its own customers introduces an element of circular financing into the AI boom, something that the industry faced during the dot-com bubble era in the late 1990s – early 2000s. However, this does not necessarily prove there is a bubble, as there is genuine, enormous demand for AI hardware and Nvidia sells plenty of such hardware.

Perhaps the biggest concern about the arrangement is that while Nvidia and its partners state that AI infrastructure can provide long-term value, AI accelerators, such as Nvidia's GPUs, have short and uncertain economic lives as the company and its industry peers introduce new and better-performing AI hardware every year, which devalues the previous generation.

"Nvidia has reached an important milestone: we began by building chips; today, we are helping create a new class of productive, investable infrastructure: AI factories," said Jensen Huang, founder and CEO of Nvidia. "In AI, compute is revenue. Nvidia compute is uniquely suited for this role. It is broadly adopted, flexible across models and workloads, fungible and transferable across customers and operators, and continuously improved through CUDA software — extending its useful life and improving its economics over time. It is supported by a deep global ecosystem of developers, customers, and offtakers. That is why we are bringing the world's leading long-term capital providers together to independently underwrite AI infrastructure. These financing platforms will help customers access scarce compute at scale and build the DSX AI factories that will power every industry and country in the age of AI."

News of regional price increases for Nvidia graphics cards has been rolling in over the past little while, and those hikes have now arrived in the United States. We nearly spit out our coffee this morning while checking Newegg prices for Blackwell products. After months of painful but still relatively reasonable e-tail prices versus skyrocketing RAM and SSD costs, popular Blackwell GPUs are now eye-wateringly expensive. And those increases appear to be rolling out across other e-tailers, too.

As part of our ongoing research for the best graphics cards, we track the prices of every e-tail listing we can find for consumer graphics cards and calculate the median price of those products. We specifically track this figure as we feel it represents the price of a given graphics card model that you're most likely to find in stock, not stripped-down models that might be produced in limited volume to hit an artificially low MSRP.

With that, the theoretically entry-level RTX 5060 is now $469.99 at the midpoint of current prices, which is now two rungs up the MSRP ladder compared to its $299.99 launch MSRP. Just a couple of months ago, 5060s were selling for a median $369.99, or just below the RTX 5060 Ti 8GB’s $379.99 launch MSRP. Now, the cheapest GDDR7 Blackwell card costs more than the RTX 5060 Ti 16GB’s $429.99 launch price.

RTX 5060 Ti 8GB cards used to be among the least marked-up Blackwell parts thanks to the fact that their performance and VRAM capacity was out of line with their high $379.99 launch MSRP, but they’re now headed up the escalator like their stablemates. The median 5060 Ti 8GB now costs $529.99, which is about 13-15% more expensive than a couple of months ago.

The RTX 5060 Ti 16GB now commands an astounding $799.99 median price, a jaw-dropping 38% more expensive than the $579.99 midpoint we last calculated. The 5060 Ti 16GB was already far too expensive to recommend for gaming at that price, and the new markup suggests that it’ll only be of interest to local AI explorers trying to get the most VRAM they can on a Blackwell card for under $1000. Pour one out for what used to be the best entry-level enthusiast GPU we recommended.

The RTX 5070 was another one of the last gaming holdouts near its MSRP thanks to strong competition from the RX 9070 16GB, but the midpoint of prices for 5070s has now leaped an incredible 29% over our last survey, to about $850-$900. Even comparing lows to lows, prices for the cheapest 5070s have jumped about 20%. That takes this card entirely out of the midrange running and positions it closer to the much faster RTX 5070 Ti, which also has 16GB of VRAM to play with.

The hikes appear to have hit the middle of the Blackwell lineup the hardest, as the midpoint of RTX 5070 Ti prices is the same as it was during our last check-in. RTX 5080s haven’t gotten substantially more expensive than they have been, either, as prices for those cards have always been highly elevated compared to their $999 MSRP. And the RTX 5050’s price has barely moved today, either, hovering near the $300 it’s maintained since around the beginning of the year.

These increases haven’t been matched by hikes on the AMD side—yet. Heavy emphasis on yet. We’re only seeing single-digit percentage increases in RDNA 4 card prices compared to our last survey, although Radeon RX 9000-series cards appear to be affected by the silicon supply crunch in other ways.

The assortment of available RX 9070 16GB cards is perhaps a bit smaller than it’s been in the past, while the cut-down RX 9070 GRE is available in abundance around its $549 MSRP, suggesting that card has taken over the true midrange role the plain 9070 could never quite manage at the same MSRP.

We felt that the GRE’s price was high at launch, but AMD likely has a better crystal ball for silicon supply chain trends than we do, as the GRE now offers incredible bang for the buck compared to the RTX 5070’s new sticker.

The RX 9060 XT 8GB isn’t completely dead yet, but only one XFX 8GB model remains readily available at e-tail for $399. The RX 9060 XT 16GB’s median price has slightly risen to $474.99, and the RX 9070 XT now sits 5% higher than our last check-in at a median of $799.99.

All told, these Blackwell price hikes are another body blow for a DIY PC component market that’s already reeling from sky-high RAM and NAND prices. Graphics cards had until recently been one of the less hiked-up component categories in a DIY PC’s bill of materials compared to the pre-AI times, but that period of relative solace is well and truly over if you want access to Nvidia’s hardware and software stack.

If you’re a PC gamer, there’s no good news here. We’ll have to see whether there’s a similar price spike waiting in the wings for Radeon cards in the coming days, or whether this is the sad, sorry new normal.

Nvidia is reportedly testing variations of its upcoming Rubin Ultra accelerator with less memory due to concerns it won't be able to source enough HBM. Some versions include just 192 GB of memory and use HBM4 instead of HBM4E, as originally announced, according to The Information. The report confirms an earlier comment from firm SemiAnalysis about a potential Rubin Ultra memory downgrade.

We first saw Rubin Ultra in the flesh earlier this year at GTC, where Nvidia showed off a compute tray housing four compute chiplets alongside 1 TB of HBM4E memory. The accelerator is part of Nvidia's Kyber NVL144 design, which is set to roll out in 2027. SemiAnaylsis reported that the rack was delayed to 2028. "Our roadmap is intact," said Nvidia to Tom's Hardware in response, though the company made no clarification on if the delay was real or not. We've reached out to Nvidia regarding this latest report.

According to The Information, Nvidia is testing versions of Rubin Ultra with 192 GB or 256 GB of memory, as well as versions that use fewer than the 16 announced memory stacks. Perhaps most importantly, Nvidia is reportedly testing with HBM4, not HBM4E as originally announced. Along with the traditional improvements we see in each new HBM generation, HBM4E is unique in that it offers a customizable base logic die. Last year, Micron announced a partnership with TSMC to manufacture the base die and allow customers to tweak the logic die based on their needs.

The complexity of HBM4E has reportedly caused a strain on supply, with memory manufacturers unable to keep pace with Rubin Ultra's rollout. At least three lower-memory designs have been tested by Nvidia, according to the report, though we don't have a full picture of details on those prototypes. The report claims testing with HBM4, as well as 192 GB and 256 GB configurations, though it makes no mention of the number of compute dies, nor the memory type for each tested capacity.

The number of dies is important. In June, reports circulated that Nvidia cancelled its quad-die Rubin Ultra design due to manufacturing complexities. Although Nvidia has yet to comment, reports at the time suggested Nvidia would move ahead with a dual-GPU Rubin Ultra. In such a case, less memory would make more sense. Even with a dual-die Rubin Ultra, the quoted capacities are lower than expected. Each base Rubin GPU currently ships with 288 GB of HBM4.

It's clear Nvidia is trying to get ahead with memory in a world where agreements have been signed multiple years into the future. Nvidia has several of its own agreements. In June, the company announced a partnership with SK hynix to develop next-generation memory technology, which includes HBM, but also LPDDR5X and DDR5. In July, Nvidia expanded that partnership with a $500 billion strategic relationship that includes a long-term memory supply agreement with SK.

Although Nvidia is considering lower-memory configurations, one Nvidia customer told The Information that per-GPU memory isn't a top concern, valuing the relationship with Nvidia over the long term.

Memory shortages are touching nearly every design currently on the market, though enterprise systems packing HBM are particularly vulnerable. Last week, Digitimes reported that Samsung, SK hynix, and Micron have sold through their HBM capacity through 2027. Last month, SK Hynix CEO Kwak Noh-jung said 2027 will be the "worst year" for the memory shortage, with supply constraints lasting through 2030.

Nvidia has a booth at the annual QuakeCon, which is happening from August 6 to 9 at the Gaylord Texan Resort & Convention Center in Grapevine, Texas, where the company is selling RTX 50-series GPUs at their original launch price. The company said in its blog post that several Founders Edition graphics cards are available at MSRP while supplies last, and that there will also be several prizes and freebies, including the GeForce Trading Cards Series 1.

“If you’re attending this year’s event between August 6th and 9th, head to the GeForce booth ASAP to get in on the action,” Nvidia said in its blog update. “And if you want a GPU upgrade, our team is bringing Verified Priority Access IRL to QuakeCon, enabling you to purchase Founders Edition GeForce RTX 5090, 5080, and 5070s at MSRP while supplies last.”

The firm officially announced the RTX 50-series in Las Vegas at CES 2025, with the RTX 5090 priced at $1,999, the RTX 5080 at $999, the RTX 5070 Ti at $749, and the RTX 5070 at $549. However, the supplies of these GPUs were limited — the 5090s and 5080s went out of stock on the same day that they arrived on store shelves. It took several months for the RTX 50-series to finally dip below MSRP, but this only lasted a few months as the memory shortage took hold and caused VRAM prices to skyrocket.

This means that the best graphics cards for gaming are quite expensive at the moment, with Tom’s Hardware’s GPU price tracker showing the best price for the RTX 5090 sitting at $4,381 — more than double the $1,999 MSRP that Nvidia set for the GPU. Even the RTX 5080, 5070 Ti, and 5070 aren’t immune to these price increases, with the most affordable options for the graphics cards sitting at $1,289 (29% over MSRP), $989 (32% over MSRP), and $629 (more than 14% over MSRP), respectively.

Unfortunately, this isn’t likely the worst that we will see when it comes to GPU pricing. There has been some disturbing news that prices for RTX 50-series GPUs jumped by 30% in South Korea as wafer costs from TSMC have increased, and GDDR7 modules push past $20. While this hasn’t reached the U.S. at the moment, there is fear that retailers in the country will follow suit soon.

Such is the state of the PC building industry that RTX 50-series GPUs at MSRP have now become a come-on to desperate gamers who just want to upgrade their gaming PCs for a reasonable price. Unfortunately, “Verified Priority Access IRL” is only available during the convention and only until supplies last — once there’s no more stock on site, you’d have no choice but to troll the interwebs for a deal or a secondhand unit if you refuse to pay for more than the MSRP. Alternatively, if you believe that you’ve got the skills, Nvidia is also hosting Quake III Arena RTX Remix and DOOM: The Dark Ages | Revelations challenges, where the best players will each receive an RTX 5070 Founders Edition GPU.

The AI boom means that no matrix math FLOPS are disposable, and that means older Nvidia GPUs with Tensor Cores are getting a new lease on life. Services are popping up that will outfit your RTX 2080 Ti with 22GB of VRAM, doubling its original memory pool and making it more useful for modern LLM and diffusion workloads. If you’re hard up for compute and you don't have an RTX 2080 Ti to modify, however, eBay has just the thing. A Hong Kong-based seller will send you a pre-modded 22GB 2080 Ti in exchange for $499.

The listing only promises that you'll receive a "Turbo," i.e., blower-style, RTX 2080 Ti with the 22GB mod. Don't get too picky about the particular brand of card you might receive, as the seller says: "The GPU brand could be Gigabyte, MSI, ASUS, Leadtek or others. It depends on what we have in hand."

The eBay listing photos show a Gigabyte blower-style card along with a large box of presumably modified cards ready to ship and a GPU-Z screenshot confirming the availability of 22528 MB of memory from a running card.

The seller only has 99.6% lifetime feedback on eBay, which is relatively low, but feedback for this specific listing shows that at least 38 happy buyers have received these cards and that they’re working as described.

Assuming you can count yourself as one of those happy buyers, a $500 22GB RTX 2080 Ti might be the best VRAM bang for your buck that you can find these days. That relatively large memory pool, combined with the RTX 2080 Ti's 616 GB/s of memory bandwidth and Tensor Cores, means that it's still useful for local LLM tasks, although its raw compute capacity and limited reduced-precision data type support compared to more modern products might make demanding diffusion workloads leisurely.

Among Turing cards, the 24GB Titan RTX still commands about $800 on eBay at today's prices, and a Quadro RTX 6000 with the same amount of VRAM is about $900. The local AI enthusiast's favorite RTX 3090, which has 24GB of faster GDDR6X offering 936 GB/s of memory bandwidth, looks to be selling for about $1200. Ampere RTX Pro cards with even more VRAM rapidly get more expensive from there.

The fact that all of these GPUs are still commanding such high prices many years after their introduction is a testament to the continuing utility and universal availability of Nvidia’s Tensor Core architecture across both consumer and data center products since 2018.

AMD has had matrix math accelerators in its IP arsenal since CDNA 1 in 2020, but their availability has been limited to Instinct data center products until RDNA 4 arrived early last year. Intel included XMX matrix engines in the Alchemist architecture from the start in late 2022, but Arc graphics products have faced considerable challenges beyond the presence or absence of that capability. And Apple only just introduced Neural Accelerators to its GPUs with the M5 family.

All that means that if you’re hard up for VRAM capacity and still need a decent amount of compute to go with it, a 22GB RTX 2080 Ti could be a compelling and relatively budget-friendly way to get there, and you get access to the entirety of the CUDA software ecosystem in the bargain. Not bad for an eight-year-old GPU that might have previously ended up in the e-waste bin by now.

Nvidia has reportedly begun to cancel orders for RTX 5090 graphics cards that were placed prior to raising their price, according to a Redditor’s own report. The claim, made in the Asus ROG subreddit, suggests that an order for an Asus ROG Astral RTX 5090 BTF graphics card was cancelled and a refund issued.

The purchaser, who made their order through Nvidia’s marketplace, was reportedly given the choice to pay the “new price” for the GPU or to lose out. Meanwhile, contact with Nvidia’s customer support team, shared in a later post, placed the blame at Asus’ door instead, suggesting that it’s actually Asus’ fault for failing to notify them of the GPU’s new pricing.

IndependentOk1031 shared their story on Reddit over the last two days. In their original post, the Redditor explains that the order was for Asus to fulfil, even at the “stupid high” price of $4,429 (or $4,607 after taxes), but it was later cancelled. An updated post shared since then goes on to explain that, after reaching out to customer support, an Nvidia customer care agent suggested that Asus wasn’t happy to fulfil the order at the price that it was listed for at the point of order, which the Redditor suggests was Friday, July 31.

According to Nvidia, that’s because the “price change update” was provided a “little late,” resulting in the cancellation. Nvidia suggests that it did try to get Asus to honor the order but, according to its agent, Asus was “unable to fulfil it at the previous price rate.” As a result, Nvidia wasn’t able to make any further steps forward. The same GPU is now listed on Nvidia’s website for $4,929.99 before tax, an increase of $500 (or 11.28%). The Redditor is still waiting on contact from an Asus supervisor to discuss the situation but notes that “the odds of anything happening are slim.”

This follows reports last week of Nvidia price rises at major tech retailers in the United States, including at Best Buy and Amazon. While the RTX 5090 Founders Edition continues to hold an MSRP of $1,999, a price still listed prominently on Nvidia’s website, pricing for this top-spec GPU means consumers have to pay thousands of dollars more. Third-party pricing data from CamelCamelCamel shows that the lowest ever pricing for this high-end Asus ROG card on Amazon to date has been $3,289.09, with current pricing set at $4,849.99 from a seller.

As our GPU price index shows, this phenomenon isn’t restricted to the RTX 5090. Current and last-gen Nvidia and AMD graphics cards across the board have all seen significant price rises in a market being badly affected by the AI boom. The cost of memory has pushed the manufacturing costs up, while the demand for the cards themselves caused by AI has caused the retail price to soar further.

With no location mentioned, it’s unclear if the Redditor has any other remedies to pursue his case further. Either way, a near-$600 price hike for a GPU already costing over two times its MSRP demonstrates the significant strain facing buyers globally in the current PC hardware market. With no sign that the market is likely to cool any time soon, buyers looking for the best GPU on sale right now could see further price hikes in the months ahead.

Elon Musk on Tuesday said in an X post that SpaceX and xAI will exclusively use Nvidia GPUs because 'they are the best.' He later clarified during SpaceX's earnings call that Nvidia's Vera Rubin NVL72 rack-scale system's design is above everything else that is available today, which is certainly praise for Nvidia, but not such a good sign for other developers of merchant AI accelerators.

"Going forward, we have decided to build exclusively on Nvidia, because we think the Vera Rubin architecture is the best architecture," Elon Musk said during SpaceX's earnings call. "We think it is the best AI computer, and we greatly value our close cooperation and partnership on many levels with Nvidia. We are exclusive to Nvidia. […] We think the design of the NVL72 VR[200] computer is a much better design than, say, having a standard rack style design."

Historically, xAI has exclusively used Nvidia's Hopper and, more recently, Blackwell hardware to train multiple generations of Grok. Although AMD has used Grok-1 on its Instinct MI300X accelerators, there has never been a public announcement or credible evidence that xAI has evaluated or used AMD Instinct, or other non-Nvidia accelerators in production. xAI's Colossus supercomputers have been using Nvidia's accelerators for years, so the official exclusivity looks more like a formality that gives a strong testament for Nvidia rather than a decision that was hard to make.

When it comes to the praise of the cable-less design of compute trays in Nvidia's Vera Rubin NVL72 VR200 rack system, then Musk's admiration of this architecture is understandable, as while expensive, such trays greatly improve serviceability, assembly speed, and reliability by eliminating a large number of manual cable and hose connections that are common sources of installation errors and failures.

Interestingly, but Musk's SpaceX plans to deploy Vera Rubin not only in its own and xAI's data centers, but also in space.

"With respect to the Starmind AI satellite, which will be essentially an optimized Vera Rubin NVL72 computer, this is not some sort of far future distant thing; we expect to start launching this next year," Musk said. "We think the design of the NVL72 VR[200] computer is a much better design than, say, having a standard rack style design. We expect to actually deploy this on the ground as well as in orbit, because we think it is going to be a radical simplification of the normal NVL72 rack."

Deploying an NVL72-scale machine will be by far a more ambitious project than Nvidia has in mind with its Space-1 Vera Rubin Module that is designed to deploy several, perhaps a dozen, of Rubin AI accelerators in space. 36 Vera CPUs and 72 Rubin AI GPUs offer rather formidable performance, though many questions remain about the cooling and reliability of such racks in space.

It is not a secret that proper cooling ensures longevity and enables hardware to demonstrate its full potential. But when it comes to data center AI hardware, proper cooling also means higher sustained performance, which directly translates into money earned by the owner. Frore Systems, a maker of cooling solutions that are made using semiconductor-grade tools, seems to have a perfect idea of how to reduce the temperature of next-generation AI accelerators and increase their performance by 15%.

Frore Systems last week published a white paper which suggests that improvements to the entire cooling stack — from the GPU packaging and thermal interface materials (TIMs) to coldplates and coolant temperatures — can increase token generation per watt by more than 30%. Meanwhile, one of the company's boldest projections based on an analytical thermal model* is that its LiquidJet coldplate technology alone can lower Nvidia Rubin GPU junction temperatures by up to 12°C, which translates into a 10% to 25% improvement in tokens/Watt, while a 10°C reduction could increase token generation by around 15%.

Indeed, modern AI accelerators, such as the upcoming Nvidia Rubin, can dissipate up to 2,400 W, and their die temperatures can easily hit 95°C or more. But while 95°C is not necessarily a problem for silicon longevity, leakage current certainly is. Leakage current rises exponentially with temperature, approximately doubling for every 10°C increase in maximum junction temperature, which is when transistor switching itself also becomes less efficient. As a consequence, hotter GPUs require higher voltages to sustain clocks, which eventually forces Dynamic Voltage and Frequency Scaling (DVFS) to reduce clocks to remain within thermal limits, which in turn will reduce performance and token generation.

This all leads to a simple conclusion: the better the cooling, the higher the performance and token output. Which is generally right. However, cooling is not as simple, as it depends on multiple factors that can be optimized. Furthermore, for AI data centers, cooling itself is no longer a way to preserve CPUs and accelerators from overheating, but really is a way to maximize their performance and token money generation.

Nvidia designs its platforms around Tj(max) temperature; it is one of the fundamental design constraints for the GPU, package, and cooling solution. This works like this:

• Nvidia specifies a maximum allowable junction temperature (Tj,max limit). This is the temperature the silicon must not exceed during normal operation. The exact value is not always public, but Frore uses 95°C for Rubin in its analysis.
• The GPU continuously monitors its junction temperature using tens or hundreds of on-die thermal sensors, yet power management monitors the hottest region.
• DVFS attempts to maximize performance while staying below the thermal and power limits, so if the GPU has thermal headroom, it can sustain higher clocks or lower voltage. If the junction temperature rises, the firmware gradually adjusts voltage and frequency. If necessary, it throttles to prevent exceeding Tj(max).

The problem is that GPUs operate under several simultaneous limits, such as thermal limit (Tj,max), package power limit, current limit, and voltage limit. Usually, power is reached before thermal. Meanwhile, modern cooling systems are designed to prevent silicon from reaching Tj(max). So, even if Nvidia's GPU never reaches Tj(max), lowering the operating junction temperature still improves efficiency because transistor leakage decreases as temperature falls. This is where Frore and its cooling systems come into play.

According to Frore, leakage power approximately doubles for every 10°C increase in junction temperature, while transistor switching power rises by about 2% over the same temperature range, so lowering operating temperatures is beneficial even when the processor is not thermally throttling.

Thermal resistance

According to Frore, the maximum GPU junction temperature used by hardware makers is directed by a deceptively simple equation:

Tj(max) = Tinlet + Q × Rtotal

where coolant inlet temperature, GPU power, and total thermal resistance determine how hot the silicon can be. Meanwhile, total thermal resistance depends on three major elements: the GPU package itself, the thermal interface material between the package, and the coldplate design. As each layer adds thermal resistance, it increases die temperature and reduces overall token money generation. That said, thermal resistance is becoming a major problem, according to the paper.

Frore claims that delidding the Rubin package dramatically lowers thermal resistance (while this is obvious, I must add again that the paper is based on an analytical thermal model*). According to the paper, an unlidded Rubin package can reduce junction temperature by as much as 20°C compared to one with an integrated heatspreader (IHS), which potentially improves tokens/Watt by up to 35%. Of course, there are disadvantages, as delidded GPUs have lower mechanical reliability. We will talk about it later on. In any case, there are cloud system providers that explore the use of delidded Rubin GPUs to boost their token money generation despite all the risks, according to Frore.

Frore's own contribution is, of course, its coldplate. Conventional coldplates are typically manufactured using skiving, a machining process that creates long, straight microchannels inside a copper block. Frore instead borrows manufacturing techniques from semiconductor fabrication — etching and bonding — to build intricate three-dimensional copper microstructures that address hotspots on the accelerator's silicon. These unique microstructures cannot be produced using traditional machining, at least not cost-efficiently, according to Frore.

Improving efficiency

The LiquidJet design features short microchannels that are etched around hot spots, multiple cooling stages, and flow routing optimized for the GPU's power-density map. According to the company's analysis, this enables a 6°C to 12°C reduction in junction temperature and improves tokens/Watt by 10% to 25% in the case of the Nvidia Rubin GPU*. A roughly 10°C temperature reduction would therefore correspond to about a 15% increase in token generation efficiency, the paper claims.

Frore argues that improved coldplate efficiency changes the economics of facility cooling, which is obviously the most important part of the hyperscalers' consideration. Nvidia designed Rubin to operate with coolant entering at up to 45°C, which enables many AI data centers to rely entirely on 'free' cooling without mechanical chillers. While lowering the inlet temperature can further improve GPU efficiency, doing so only makes economic sense if the energy consumed by the chillers is offset by the resulting increase in money token generation. Meanwhile, because LiquidJet requires a lower coolant flow rate to maintain the same junction temperature, it also reduces the chiller coefficient of performance (COP) required for additional cooling to become worthwhile.

In Frore's example, a Rubin GPU equipped with a conventional skived coldplate requires a chiller COP of approximately 6.7 before colder coolant delivers a net efficiency benefit, whereas LiquidJet lowers the break-even COP to around 4.1, which makes mechanical chilling economically attractive across various deployments.

One interesting thing about Frore's analysis is that its LiquidJet is more efficient on Rubin data center GPUs compared to Blackwell data center GPUs* due to the higher transistor density of the former.

Frore's analysis does not stop at exploring the advantages of its own cooling systems, so the company's analytical thermal model extends to other means by which improved cooling and/or lowered thermal resistance can affect temperatures and therefore money token generation.

One of the most striking claims by Frore concerns Nvidia's upcoming Rubin is that Frore claims that delidding the GPU package — removing the IHS and the graphene TIM placed between the die and the lid — dramatically lowers thermal resistance, which therefore reduces junction temperature by as much as 20°C compared to regular GPUs with IHS, which therefore improves tokens per Watt by up to 35%, according to the model used by Frore.

Meanwhile, mechanical reliability becomes a major concern for delidded GPUs. Without the IHS, the bare Rubin GPU packaged using TSMC's CoWoS-L technology becomes considerably more vulnerable to cracking of bridges that connect the two Rubin dies. In fact, even in the Hopper era, some GPUs literally cracked with certain liquid coolers. Furthermore, maintaining uniform contact pressure across multiple exposed dies is substantially more difficult than in the case of monolithic processors. Nonetheless, there are hyperscalers that are exploring the use of delidded Rubin GPUs to increase their token generation and money output.

Thermal interface materials play an equally important role. By default, Nvidia's Rubin reportedly addresses the thermal penalty of a lidded package by using liquid indium metal TIM with gold-plated contact surfaces. Frore argues that an unlidded package paired with a high-performance phase-change material such as PTM7950 still exhibits lower overall thermal resistance than a lidded package using liquid metal, which turns into as much as a 14°C junction-temperature advantage and up to a 28% increase in money tokens/Watt, according to Frore's model.

Summary

The key point of Frore's white paper is that cooling has become a key determinant of AI data center profitability, as lower GPU junction temperatures improve token generation efficiency rather than 'just' preventing overheating.

In a white paper based on an analytical thermal model, the company claims that its LiquidJet coldplate can lower Nvidia Rubin junction temperatures by 6°C to 12°C and increase tokens/Watt by 10% to 25%, while a 10°C reduction could boost token generation by about 15%.

In addition, the company argues that more efficient coldplates make mechanical chilling economically viable across a wider range of AI data centers as it lowers the break-even chiller efficiency required to offset cooling power consumption.

Finally, Frore claims that delidding Rubin and optimizing thermal interface materials can reduce thermal resistance further and improve tokens/Watt by up to 35%, albeit at the cost of greater mechanical risk for these accelerators.

*It should be noted that Frore's analysis is based on an analytical thermal model rather than experimental results. The paper builds on the thermal resistance equation (Tj = Tinlet + Q × Rtotal), published or assumed operating parameters for Nvidia's Rubin GPU, and the company's own estimates of how different coldplate designs affect thermal resistance.

GPU prices are set to increase in South Korea starting this month, specifically Nvidia’s desktop GeForce RTX 50 series. According to a recent report, the price increase is primarily due to the recent increase in price for advanced process wafers from TSMC (Taiwan Semiconductor Manufacturing Company), along with the rising price for GDDR7 memory. Perhaps most troubling is that, due to global market dynamics, pricing doesn't exist in a vacuum for any single region, suggesting that price hikes could be in store for other areas in the future.

For context, TSMC recently asked its customers to prepare for price increases across its advanced chipmaking portfolio. This hike was extended beyond the newer 3nm process to include 7nm and other legacy products. ZDnet Korea’s report additionally cites market research firm TrendForce, claiming that GDDR7 2GB modules used in the RTX 50 series GPUs have increased to $20 per unit. As fabrication and memory costs have increased, Nvidia has raised the prices of the RTX 50 series GPU packages it sells to board partners. These board partners, thus, have little choice but to pass those higher costs on to consumers.

Multiple officials from domestic importers and distributors in the region have reportedly confirmed the price rise and have announced that major graphics card manufacturers plan to raise the prices of RTX 50 series models by up to 30% starting this month.

According to a manufacturing company official, "Major manufacturers have temporarily suspended shipments ahead of the August price hike, and to my knowledge, few companies have secured inventory prior to the price increase." Similarly, a local distributor said, "One manufacturer with a low domestic market share is considering a price increase of about 20% compared to existing levels, and other manufacturers are also preparing for price increases of up to around 30%."

High-end graphics cards with larger GDDR7 memory configurations are expected to see the biggest increase in production costs, leading to higher retail prices. According to Danawa, a South Korean price comparison website, the cheapest RTX 5060 Ti 8GB model now sells for around 700,000 won (about $490), up roughly 100,000 won (about $70). Meanwhile, the lowest-priced RTX 5070 model is listed at around 1.1 million won (about $770), an increase of approximately 200,000 won (about $140).

The flagship RTX 5090 has seen the biggest price hike. Depending on the model and manufacturer, it is currently selling for up to 7.3 million won (around $5,112), an increase of as much as 1.5 million won (around $1,050) compared to last month.

Google was among the first hyperscalers to develop its own custom AI processors about a decade ago and has been steadily ramping their deployment since then. The company seems to be so confident about its TPU v9 due in 2028 that it intends to order 12 – 15 million of such processors, according to a Fubon Research note to clients published by Sean. If the information is correct, Google may not only produce more or a comparable number of AI accelerators than Nvidia, but may also need to use Intel Foundry to meet its goals.

"Based on our checks, Google plans to have 12 – 15 million TPUs in 2028," the paper reads. "Entering 2028, Google’s TPUs will enter the V9 generation with four compute dies, suggesting that their capacity consumption will more than double in 2028 versus 2027."

Fubon estimates that Nvidia supplied 8.2 million data center AI GPUs in 2026 and is on track to increase the number to 12.4 million in 2028. If Fubon is correct about Google's plans to produce 12 – 15 million 9th-generation TPUs in 2028, then the company may produce more, or at least a comparable number of AI accelerators, than Nvidia in 2028.

TSMC is not enough

How the performance of Google's v9 TPUs will stack against Nvidia's Rubin and Rubin Ultra is something that remains to be seen, but the fact that Google intends to use four compute chiplets on these AI accelerators clearly points to the fact that the company bets big on the performance of these processors. Meanwhile, building an AI accelerator with four large compute chiplets is a major engineering effort, which Google seems to have accomplished.

"Although we do not have the detailed allocation yet, we think it is difficult to reach Google’s target with TSMC alone, and Intel's supply is a must by 2028," the paper continues.

Researchers from Fubon are not sure whether Google's allocations at TSMC will be enough to meet the company's demand for 12 – 15 million 9^th Generation TPUs, so they think that Google will have to use Intel Foundry's capacity to meet its volume goals. Over the past few months, we have seen reports claiming that Intel had landed orders to make three million TPUs for Google following months of Google's testing of Intel's advanced packaging technologies. Indeed, if Google wants to make its silicon at Intel Foundry, usage of Intel's advanced packaging services makes great sense. It should be noted that when compute chiplets are developed, they must be developed with their packaging technology in mind, as Intel's EMIB/EMIB-T and TSMC's CoWoS-L are incompatible.

World's largest consumer of AI accelerators

If the information about Google's plans to produce 12 – 15 million TPUs in 2028 is correct (note that the difference between 12 and 15 is 20%, which is huge) and Google will indeed deploy more AI accelerators annually than Nvidia sells to the entire market, it would mark a dramatic shift in the AI hardware landscape. It will not only make Google the world's largest consumer of AI accelerators (as the company will unlikely cease buying Nvidia hardware), it will eventually make Google the owner of the world's most capable AI hardware fleet. Whether or not Google will use its overwhelming AI compute capacity primarily for its own services, or will lend the majority to other is something that remains to be seen.

Meanwhile, for Google's rivals, the milestone will underscore the growing importance of vertically integrated AI infrastructure, where cloud providers design chips tailored to their own software stacks, workloads, and data centers instead of purchasing off-the-shelf GPUs.

Yet, Google's surpassing Nvidia in unit shipments would not necessarily diminish Nvidia's dominant position. AI demand continues to expand so rapidly that both companies could increase deployments simultaneously, but Google will simply grow faster, at least till Nvidia ups production of its AI accelerators with Feynman and Feynman Ultra in 2029 – 2030. After all, Nvidia's AI GPUs are sold out. What Nvidia should worry about is not the volumes of TPUs that Google can deploy, but rather the fact that these processors do rely on a software stack that rivals Nvidia's CUDA, the company's main competitive advantage.

If you were hoping GPU prices would settle down anytime soon, you are in for disappointment. Nvidia’s RTX 5080 and RTX 5090 models from various OEM partners are once again selling well above their launch prices. As per a recent listing noticed by users on Reddit, the Asus ROG Astral RTX 5080 is now priced at $2,099.99 at Best Buy. That's $600 above its official launch MSRP of $1,499.99 and even $100 more than the flagship RTX 5090's launch MSRP of $1,999.

Similarly, pricing for the RTX 5090 has also skyrocketed in recent months. According to a Reddit post, the Asus ROG Astral RTX 5090 is currently listed at $4,329.99 on Amazon, roughly $1,530 above its launch MSRP of $2,799.99. Even the MSI Gaming Trio RTX 5090 has gone up to $4,299.95, a whopping $1,900 more than what it was when it debuted in early 2025.

That said, these hefty prices are primarily limited to premium partner models. Certain mainstream RTX 5080 cards can still be purchased closer to Nvidia's MSRP. For instance, Zotac's RTX 5080 Solid OC is selling for $1,249.99 on Amazon while PNY's RTX 5080 OC is listed at 1,256.99 at Best Buy.

While one cannot ascertain the exact reason behind this price increase, it does take us back to when tech YouTuber der8auer claimed that Nvidia has discontinued its Observed Pricing Program (OPP). This was essentially an incentive scheme that helped board partners to sell certain Nvidia products at or near MSRP. The termination of this program meant that manufacturers such as Asus, MSI, and Gigabyte gained no incentive to keep prices close to Nvidia's suggested retail price. As a result, premium custom cards have become even more expensive, with manufacturers passing higher production costs on to the consumers.

In addition to that, rising DRAM prices thanks to AI-driven demand have led to a massive increase in the cost of GDDR7 memory used in RTX 50-series cards. Nvidia was also said to be prioritizing production of higher-margin products, including the RTX 5080 and AI hardware, which has further tightened supply of certain gaming GPUs.

For potential customers, the latest listings suggest that waiting for premium RTX 50-series cards to return to MSRP may take longer than expected. While Founders Edition models remain the closest option to Nvidia's suggested pricing, they're often low in stock, forcing customers to choose between paying a hefty premium for high-end partner cards or settling for more affordable custom models.

An Nvidia employee has been detained in Taiwan over allegations of forgery and breach of trust, in relation to the Supermicro smuggling scandal, that saw servers ostensibly sold to companies in Southeast Asia routed to China instead. Nvidia itself hasn't been accused of wrongdoing, and it published a statement calling smuggling a "nonstarter," saying that any GPUs sold through such a system would have no "service, support, or updates."

But that hasn't stopped Nvidia from taking its own measures to reduce its exposure to potential future smuggling efforts. Earlier this month, it created a form of "whitelist" for companies it sells to. It also investigated the firms it will continue to do business with, even sending staff members to customer data centers at the urging of the White House for verification.

Prosecutors have made it clear from the start that Supermicro isn't under investigation, merely its employees. The same is true of Nvidia. But as the AI frontier model race heats up and the White House floats banning Chinese models outright, Nvidia could face further restrictions on its hardware sales and greater scrutiny of its international actions.

Investigation escalation

The Supermicro smuggling scandal first came to light in March, when a trio of individuals were detained for deliberately mislabelling servers planned for sale to Southeast Asian countries. Instead, though, they sold them to China, getting around US export controls. The detentions included Supermicro co-founder, Yih-Shyan "Wally" Liaw, as well as a Supermicro sales manager in Taiwan, and a third-party broker who previously worked at Supermicro.

Where those detentions happened on U.S. soil, though, the investigations went international in May, when the Taiwan Keelung District Prosecutors' Office executed search warrants against three individuals it claimed were involved in illicit smuggling efforts. Although it was said to be independent of the U.S.-led investigation, it involved a similar scheme designed to smuggle Nvidia hardware into China.

In Taiwanese law, selling GPUs to China — even the U.S.-restricted kind — isn't strictly a crime, but filing fraudulent paperwork and falsifying documentation absolutely is. That's why Taiwanese authorities have leaned on local fraud laws to tackle this increasingly international case.

Although the authorities were clear that Supermicro as a company wasn't being investigated, a number of high-level employees were. That continued in June when Taiwanese officials raided the Supermicro offices in Taiwan, as well as the homes of six individuals and three company sites, all said to be involved in the smuggling scheme.

The widening scope of the investigation ultimately pulled in workers from Supermicro distributor Albatron Technology and data center operator Chief Telecom. Taiwan has since said it is considering placing a criminal ban on all AI chip exports to China, locking down smuggling routes that have been actively exploited for several years.

But now the investigation is escalating up the supply chain and has now reached Nvidia itself. Although the company isn't under investigation, Nvidia's culling of potentially problematic suppliers and buyers might not do much if its own workers are facilitating the smuggling actions.

This is serious

The Nvidia employee in question has the surname Chang, but has remained otherwise unnamed. He was detained on suspicion of falsifying business documents, with authorities searching his home and workplace on July 24, marking the first time that Nvidia's premises have been investigated in this manner since the start of the smuggling scandal.

Prosecutors consider him strongly suspected of the charges, with a very real risk for attempted flight, destruction of evidence, and collusion with witnesses.

"Smuggling is a nonstarter," an Nvidia spokesperson told Tom's Hardware. "We primarily sell our products to well-known partners, including OEMs, who help us ensure that all sales comply with U.S. export control rules. Even relatively small exporters and shipments are subject to thorough review and scrutiny on both sides of the globe, and any diverted products would have no service, support, or updates."

Although authorities are clear that they are not investigating Nvidia as a company, an employee's involvement in the scheme will put a spotlight on Nvidia's actions and raise further questions about any additional involvement it or its employees may have had.

CEO Jensen Huang said in May that there was "no evidence of any AI chip diversion," but the situation has obviously changed since then. At the beginning of June, U.S. Senator Elizabeth Warren wrote to Nvidia general counsel Tim Ter, asking for evidence that supported Huang's claims.

Supply and demand

At the time of writing, there is a legitimate channel for Chinese firms to purchase Nvidia GPUs, but they're not the most cutting-edge Blackwell chips. There are older Nvidia GPUs granted licenses that are reviewed on a case-by-case basis, with the U.S. government taking a 25% revenue share cut of the sales. This reportedly adds up to just 75,000 units for 10 different Chinese companies - a relatively trivial amount of GPUs for Nvidia.

This is for the China-only, neutered Nvidia GPUs like H20 and H100s — not the cutting-edge GB200 and GB300 Blackwell-based stacks available to Western AI developers.

But this legal demand comes despite the lack of cutting-edge hardware options, the regulatory hoops that those involved need to jump through, and the Chinese government using carrots and sticks to encourage the use of domestic chip options.

That's because for certain tasks, Nvidia GPUs remain the best. For training, there's nothing that can compete with Nvidia's options. Chinese firms like Deepseek have tried previously, but they had to switch back to Nvidia when Chinese alternatives didn't measure up. Although some post-training fine-tuning is now possible on Chinese hardware, the Moonshot's headline-grabbing Kimi K3 was trained on potentially smuggled Nvidia Blackwell GPUs.

Considering the impact that Kimi K3 has had on the AI industry, it's hard not to imagine other Chinese AI developers looking to have their own "Deepseek moment" wouldn't search out access to Blackwell GPUs themselves.

The net may be closing on the Supermicro smuggling scheme, but the incentive is there for others to take its place, if they haven't already.

Update: July 30, 2026, 2:45 AM (PT) — Headline edited to reflect broader trends in AI GPU smuggling, altered passage to clarify that multiple schemes were previously in operation.