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.

Walmart isn't the first choice you think of for computers and components, but it, of course, does stock them and is guilty of being a bit of a sleeper when it comes to hosting some great tech deals if you can find them. One of the main deals I spot at Walmart is on their gaming laptop selections, and today, we have a $350 saving on a little 15-inch gaming powerhouse for under a grand. MSI's Cyborg 15 with RTX 5060 graphics is just $949. That's a pretty good price for an entire gaming laptop in the current economic climate, and at a price that many can afford.

● Check out this deal at Walmart

The Cyborg has a 15.6-inch 1080p display with a 144Hz refresh rate, more than ample resolution for a small screen. Images will appear crisp thanks to the pixel density on the 15.6-inch IPS panel. Powering the display is an Nvidia RTX 5060 laptop GPU with 8GB of VRAM, plenty of memory for 1080p gaming. Rounding out the rest of the internal specs for the MSI Cyborg is an 8-core Intel Core 5 210H CPU, 16 GB of DDR5-5600 RAM, and a 512 GB PCIe 4.0 SSD.

The Cyborg 15 has a distinct aesthetic, with a slate gray coloring to its outer shell and a somewhat symmetrical pattern on the keyboard, with some transparent plastic cutouts. There's 4-zone RGB lighting on the keyboard, a fingerprint reader, a webcam shutter, and a healthily large 55.2Wh battery.

Connectivity options include Wi-Fi 6E and Bluetooth 5.3 for wireless networking, and an Ethernet port for a stronger cabled network. For your peripherals, there are fast USB ports that include USB-A and USB-C (DisplayPort and Power Delivery 3.0) ports, and an HDMI 2.1 (4k 60) port for hooking up to an external display.

The MSI Cyborg 15 in this deal is a 2026 model selling for just $949 at Walmart right now, making it one of the cheapest up-to-date RTX 5060 gaming laptops we've seen in a while, and a great counter to the ever-rising price barriers to jumping into PC gaming.

If you're looking for more savings, check out our Best PC Hardware deals for a range of products, or dive deeper into our specialized SSD and Storage Deals, Hard Drive Deals, Gaming Monitor Deals, Graphics Card Deals, Gaming Chair, Best Wi-Fi Routers, Best Motherboard, or CPU Deals pages.

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.

We've been highlighting the benefits of going down the pre-built gaming PC and laptop route a lot recently, thanks to the ever-rising prices of PC components. Today is another example of a price cut on a ready-made gaming computer that might be the saviour of those looking to buy into PC gaming in the current climate. Hitting a new all-time low price at Amazon is the Acer Predator Helios Neo 18 AI, falling to $2,199.99. That's a saving of $600 from its most recent sale price of $2799.99, which we've confirmed via price comparison sites.

● Check out this deal at Amazon

The Acer Predator Helios Neo 18 AI features an Intel Core Ultra 9 275HX processor, an Nvidia GeForce RTX 5070 Ti laptop GPU with 12GB of fast GDDR7 VRAM, 32GB of RAM, and a 2TB PCIe Gen 4 SSD. These components power your gaming on the huge 18-inch IPS display with an insanely fast 240Hz refresh rate and 3ms overdrive response time.

The chassis of the Predator Helios Neo 18 AI is a mix of metal and plastic, with an abyssal black finish. There is room to upgrade parts in the future if RAM and SSDs don't become unaffordable, with two DDR5 RAM slots that support a maximum of 64GB and two M.2 slots. For that gamer aesthetic, there's plenty of bright RGB zones all over the laptop, so you can dial it up to 11 if you want to go for the full light show.

The RTX 5070 Ti is one of the more powerful laptop graphics chips, and although not comparable to a desktop version, it still has 12GB of VRAM and will be able to run games smoothly on the 18-inch 2560x1600 WQXGA+ screen. Being an Nvidia GPU, the laptop will be able to access Nvidia's DLSS software to increase or smooth out frame rates on more taxing titles. This is a large laptop at 18 inches, and quite expensive due to its size and amount of memory and storage used. So it's good to see it hit a new all-time low price at Amazon of $2,199.99.

If you're looking for more savings, check out our Best PC Hardware deals for a range of products, or dive deeper into our specialized SSD and Storage Deals, Hard Drive Deals, Gaming Monitor Deals, Graphics Card Deals, Gaming Chair, Best Wi-Fi Routers, Best Motherboard, or CPU Deals pages.

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.

Keeping the upper hand in the AI arms race has become a vital goal for both the U.S. and China, and Nvidia's Blackwell AI chips are one of many flashpoints in that fight. The US government bars their sale to Chinese firms, while Chinese policies block their import as the country tries to spin up an advanced AI chip industry of its own.

But as we've discussed multiple times and then some more, Chinese AI firms are quite creative with workarounds for these restrictive policies. That's the case of Moonshot AI, which has reportedly made good use of Blackwell for training the recently released Kimi K3 frontier-level model, and is seemingly looking to obtain additional access in preparation for Kimi K4.

The Information says "people with knowledge of the matter" told it that Moonshot employed two Chinese firms that have Blackwell chips in their respective datacenters despite the bilateral restrictions we mentioned. Given that those chips are scarce enough right now even when obtained legitimately, it's unsurprising that neither firm had enough of them on hand to let Moonshot train K3. This reportedly forced Moonshot to figure out how to join multiple eight-chip Blackwell servers together and across datacenters in order to harness the necessary computing power.

The report also mentions "a researcher at a major Chinese tech firm who works on model training" as stating that Kimi K3 has "started a new round of arms race" in the country's AI industry. They further added that training frontier models is difficult or impossible with the promising but slowly developed homegrown chips. By that source's account, Chinese AI accelerators remain a generation or two behind Nvidia's current offerings and are reportedly several months in backorder.

For inference work, Moonshot reportedly relies on Nvidia's China-market HGX H20, a last-gen chip that isn't blocked by trade laws on either side of the Pacific. The firm recomends setups with at least 64 H20 GPUs for running Kimi K3. Those requirements, combined with that frontier model's desirability, meant that Moonshot quickly ran out of computing capacity to run K3 and currently has subscriptions on a waiting list. Given it's an open-weight model, and that its weights were released this week, many other inference providers are serving it, perhaps alleviating that bottleneck.

Meanwhile, White House Director Michael Kratsios claimed last week in a tweet that that Moonshot AI both "acquired GB300-equipped servers and has accessed GB300s in Thailand." While buying Blackwell chips is illegal, renting them is apparently fair game, at least until the proposed Remote Access Security Act takes effect. That law is designed to prevent the rental loophole by treating remote access as an export event. There's no telling exactly how the U.S. would enforce this law across other jurisdictions, though.

At any rate, the Department of Commerce is formally investigating if Chinese firms are accessing advanced U.S. chips like Blackwell GPUs, and that's likely to be an ongoing point of contention as the war for frontier model supremacy continues.

In China, it's an open secret that many of the country's high-level own or have access to Blackwell and other advanced chips, but despite all the trade restrictions and pushing the usage of local-made chips, the CCP has seemingly yet to crack down on said AI players. Some have theorized that the turning of this blind eye is intentional so Chinese firms like Moonshot can catch up to the likes of Anthropic and OpenAI.

Everyone loves previews of pre-release hardware that just happened to fall off the back of a truck — particularly when it's a piece of kit that's been lighting up news headlines. Fouquin, a TechPowerUp reader, "accidentally" stumbled upon a Microsoft Surface Laptop Ultra prototype that ensconces an Nvidia RTX Spark N1X SoC.

As a quick recap, the N1X is supposed to herald a generation of "AI laptops", meaning it's an alternative to the RTX Spark desktop, Mac Studio, Ryzen AI Max machines, and the Macbook Pro. This is due to the fact that they're all designs with large pools of unified memory and competent GPUs, making them suitable for non-datacenter AI work. The N1X packs 10 MediaTek-designed ARM CPU cores in a 5p5e configuration with multi-threading for 20 threads total, plus a GPU purportedly equivalent to a desktop RTX 4070 or a mobile RTX 5070. It can be wired up to 128 GB of LPDDR5X onboard in a unified pool, though the tested machine only has 24 GB.

Fouquin notes the limitations of their "review" and notes they're not an AI person, though they tried running the Phoronix AI test suite nonetheless. This yielded a mixed bag of results, as very few tests would reliably work, seemingly due to pre-production drivers. Some Vulkan tests did run properly, and the reported performance appears to be indeed in the ballpark of an RTX 4070, though there are two very important caveats.

First, this system and its drivers appear to be very buggy and unoptimized, particularly around power management. Fouquin says the system came with ancient 591.33 drivers from November 2025, eventually upgraded to 616.00 preview drivers with CUDA and Vulkan support. Apart from that, the tester found the machine's TDP spends and power plans behaving erratically, and none of the Phoronix CUDA tests produced results.

Second, head-to-head benchmarks are hard to come by, as RTX-series desktop cards have limited pools of VRAM compared to the 24 GB on the test machine. All told, there's a fair chance that final hardware might perform better, especially considering this pre-production machine displayed many outstanding issues with the LCD display, idle current draw, and that plugging it in or running it from battery didn't make any difference. Interestingly, despite the 616.00 drivers adding features, performance actually took a significant turn for the worse.

The Cinebench 2024 multi-threaded CPU test produced a result of 1386 points, trailing the 12-core Macbook M4 Pro, while Cinebench 2026 yielded 5771, a fair bit behind the Apple M4 Max. Fouquin did run some GPU tests, and the best results in 3DMark were with the older drivers. Port Royal showed 26-31 FPS on older drivers, while Nomad got 20-21 FPS. Meanwhile, Unigine Superposition actually improved with the 616.00 software, netting a maximum of 56-58 FPS.

Compared to desktop cards, those results would roughly match RTX 3060 or RTX 3060 Ti, once again underscoring the pre-release nature of the Nvidia drivers and related software. As if more illustration was needed, the Balanced power plan almost always produced the best results.

The story isn't much different in gaming, as Fouquin says that while most of their library of games ran fine, the performance was very sluggish, with multiple-second stutters and with GPU clock speed "bouncing all over the place." Helldivers 2 would outright crash the system, as did the GPGPU integer tests. Fouquoin believes these are all issues specific to this particular implementation of the N1X rather than a problem with the platform altogether. It's worth adding that presumably the games ran under the ARM emulation layer, which can introduce its own set of issues.

N1X chip and fixable warts aside, Fouquin actually liked the machine, remarking on the high build quality, the keyboard feel, touchpad input, and display quality. Given there were previous claims the machine would be serviceable, Fouquin put those to the test and opened it, finding that while that claim is technically true, "many snap-on thin aluminum sheet panels covering all the primary components (even encasing the SSD) means actually attempting to service the laptop will lead to much bending and/or breaking." Here's to hoping that this too is fixed before final release.

Taiwan's Keelung District Prosecutors' Office said on Tuesday it has detained a man surnamed Chang on suspicion of falsifying business documents, after investigators searched his home and his workplace on July 24 in connection with the AI chip smuggling case it opened in May. Bloomberg reports that Chang works for Nvidia and that the workplace search covered his desk at the company's Taipei office, which would make this the first known legal action against an Nvidia employee in a chip diversion case. Prosecutors said they consider him strongly suspected of the offenses and cited risks of flight, destruction of evidence, and collusion with witnesses, but haven’t accused Nvidia of any wrongdoing.

"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."

Taiwan doesn't treat the unauthorized export of AI chips to China as a crime, which is why every detention in the case has so far concerned fraud accusations. The three people arrested in May were pursued over shipping declarations rather than the shipments, and the six summoned during June's raids on Super Micro and two supply-chain partners were questioned on the same basis.

Huawei and SMIC were among 601 entities added to the International Trade Administration's strategic high-tech commodities entity list in June last year, a designation that requires government approval before a Taiwanese company can ship to any of them. The list works off buyer names and carries no performance threshold, so a rack of accelerators sold to a company that isn't on it needs no approval.

It was reported last month that Taipei is weighing performance-threshold controls modeled on Washington's, and the Ministry of Economic Affairs confirmed consultations with the U.S. on bringing advanced chips under regulation without setting a timeline. Seven weeks on, prosecutors are still building the case out of the Criminal Code.

Senator Elizabeth Warren, ranking member of the Banking Committee, wrote to Nvidia general counsel Tim Teter and audit committee chair Brooke Seawell on June 1 asking what records support Jensen Huang's public claim that "there's no evidence of any AI chip diversion," and whether the committee had reviewed export compliance following March's indictment of Super Micro co-founder Yih-Shyan "Wally" Liaw over roughly $510 million in diverted servers. She set a June 18 deadline for answers.

A legal channel into China has existed since December, when the Bureau of Industry and Security began reviewing H200 export licenses case by case under a 25% revenue share, clearing around 10 Chinese buyers for up to 75,000 units each. Commerce Under Secretary Jeffrey Kessler told the House Foreign Affairs Committee on July 14 that shipments under those licenses remain trivial. Blackwell parts stay off the table entirely, and Chinese demand for them has pushed five-year-old A100 servers to $82,000 on the domestic gray market.

OpenAI is in advanced talks to lease SB Energy's 10 GW data center campus in Piketon, Ohio, with Nvidia in discussions to guarantee roughly $250 billion of the financing behind it, the Wall Street Journal reported on Sunday, citing unnamed people familiar with the matter. The site would be OpenAI's first as a tenant rather than a customer of Microsoft, Amazon, or Oracle, and Nvidia is separately discussing financing the accelerators going inside, which could run to another $350 billion. Terms haven't been settled, and the arrangement could still collapse.

OpenAI has no investment-grade credit rating, and Nvidia's involvement would let SB Energy raise debt against Nvidia's balance sheet instead of its tenant's. Nvidia has already put $30 billion into OpenAI, which has raised its projected compute spending to around $750 billion through 2030, up from roughly $600 billion earlier this year, according to the Journal. Commerce Secretary Howard Lutnick controls allocation of the site's power, and Anthropic, Microsoft, and Google have all spoken to him about it in recent weeks.

Nvidia's Q1 FY2027 10-Q caps maximum gross exposure across all of its partner facility lease guarantees at $3.5 billion, shrinking as partners pay their lessors, with $712 million sitting in escrow against it. Nvidia took the guarantees in exchange for warrants and carries them as credit derivatives, describing their fair value as immaterial.

The first one, disclosed in the third quarter of fiscal 2026, was capped at $860 million with $470 million of escrow behind it. The partner separately contracted to sell the data center cloud capacity, and Nvidia retained the option to assume the lease for internal use or sublease it if the escrow and that contract came up short. Neither remedy has an obvious equivalent at a 10 GW campus on federal land.

Nvidia held $62.6 billion in cash, cash equivalents, and marketable securities when fiscal 2026 closed on January 25, against full-year revenue of $215.9 billion and net income of $117 billion. A $250 billion guarantee works out at roughly 71 times the guarantee book Nvidia has disclosed, more than a year of revenue, and about four times its cash.

SB Energy broke ground at the former Portsmouth Gaseous Diffusion Plant on March 20 alongside Energy Secretary Chris Wright, Lutnick, and SoftBank chairman Masayoshi Son. The site enriched uranium for the U.S. weapons program from 1954 until 2001 and is still being decontaminated. The Department of Energy had listed it among 16 federal sites opened to data center construction, and SB Energy leases the land rather than owning it. Powering the campus takes 9.2 GW of new natural gas generation plus $4.2 billion of transmission work with AEP Ohio, funded by $33.3 billion Japan committed under its trade agreement with the U.S. The first phase, roughly 800 MW, is expected in 2028.

OpenAI gave up on building its own data centers last year in favor of leasing capacity, and SoftBank carries more than $130 billion of debt while funding buildouts in Ohio, France, and elsewhere.

Imperial Valley Computer Manufacturing has filed a lawsuit in a bid to gain access to Colorado River water, 287 million gallons of which it says it needs to cool a 330-megawatt data center, which would be the largest in the state. Despite only representing a fraction of the region's water supply, the buildout of the data center may affect the local farming and adjacent industries and terminate hundreds, if not thousands, of positions, reports Business Insider.

After two cities in the region denied the California-based AI data center recycled wastewater for cooling, it filed a lawsuit demanding to get water from the Colorado River for cooling. The 330-megawatt facility was not only designed to be the biggest AI data center in California, but it specifically committed not to use water from the Colorado River because it was promised wastewater. But now the owner of the data center is essentially asking to redirect water supply from agriculture to the facility.

Imperial Valley Computer Manufacturing — the owner of the 330 MW AI data center — is requesting access to approximately 287 million gallons of water per year after two cities — El Centro and Imperial — declined to supply reclaimed wastewater for cooling. The Imperial Irrigation District (IID), which distributes Colorado River water throughout Imperial Valley, also denied the company's request. The Colorado River supplies water to roughly 40 million people across seven western states and serves as the valley's sole freshwater source for roughly 180,000 people. Agriculture consumes about 80% of California's allocation from the river, while roughly 95–97% of the water IID delivers goes to agriculture.

The data center is seeking roughly 287 million gallons per year (about 750,000 gallons per day, or ~880 acre-feet per year), whereas the Imperial Irrigation District (IID) holds rights to approximately 3.1 million acre-feet of Colorado River water annually, which means that the data center demands only a small fraction — 0.028% — of IID's total water supply.

Sebastian Rucci, a Huntington Beach attorney who leads the project, claims that the facility's water consumption would be comparable to that of a 160-acre farm and will require no additional Colorado River allocation. In fact, he states that the facility would not increase pressure on the river because the company intends to purchase nearby farmland together with its associated water allocations.

Under the proposal, irrigation on those properties would cease, thus transferring the existing water quotas to be redirected to the data center cooling, at the expense of local farming output and associated jobs. "There's a lot of resistance in any agricultural community to 'buy and dry' because that's jobs," a senior fellow at the Pacific Institute focused on Colorado River Basin water use told the outlet. According to them, local resistance to the plan is less about the amount of water, and more about buying up farmland and reallocating it for industrial use.

The approach, of course, differs from the earlier plan that intended to avoid using Colorado River water altogether. However, after the data center was denied wastewater from two cities, it does not have a choice if it wants to go ahead with the buildout.

Rucci reportedly indicated that the project would provide substantial economic benefits for the local community, including 1,688 construction jobs, more than 100 permanent positions, and an estimated $2.95 billion in economic impact over 30 years. For a region where unemployment stood at approximately 17% in May, the economic diversification is essential. However, the big question is whether 100 permanent roles could offset the lost positions in the farming industry and industries tied to agriculture.

Water policy specialists interviewed by Business Insider said that the debate extends beyond the project's annual consumption. Instead, they questioned whether converting irrigated farmland into industrial use is an appropriate long-term direction for the region, which has historically depended on farming. The experts also warned that although landowners could benefit from selling land or water rights, surrounding rural communities may lose employment and business activity adjacent to agriculture, which includes equipment suppliers, repair shops, and sellers of fertilizers. Another factor mentioned by the experts was the U.S. reliance on farms around Imperial, California, and Yuma, Arizona, as they were the main suppliers of certain agricultural products in winter.

A computing enthusiast has repurposed a very noisy and largely obsolete enterprise GPU (with lots of VRAM) for local LLM inference purposes. They are now enjoying a system that has doubled its total VRAM quota to 32GB for just a $266 (£200) outlay. That’s a good result, especially in the midst of a RAMpocalypse.

Oscar Molnar explains that a cheap Tesla V100 SXM2 with 16GB HBM2 was sourced, as was an SXM2-to-PCIe adapter, and a PWM mod for the loud-as-a-lawnmower cooler, to complete this VRAM expansion for the hefty local LLMs project. Indeed, these GPUs do look cheap right now, as I can see them listed on eBay US for under $140 each, if you don’t mind buying from China.

As mentioned above, you can’t just get one of these Tesla V100 SXM2 cards with abundant VRAM and plug it into your PC. Molnar says they spent about $66 on an SXM2-to-PCIe adapter, also on eBay.

You might think that was enough. However, the PC and local LLMs enthusiast baulked at the noise of “the fan from hell,” which came as standard with the Tesla V100 SXM2. That shrieking cooler was measured outputting 82dB of noise. Molnar described it as “somewhere between a garbage disposal and a lawnmower.” This may be the most complicated tweak yet, but basically the existing fan wires just needed rerouting and plugging into the motherboard PWM fan header. You could also simply purchase a “2.54mm male to PH2.0 female jumper cable” for the task. Apparently, the fan only needs to run at 10% to keep the Tesla V100 under 50C at full load.

27 billion parameter LLM runs at 32 tokens per second

With the hardware all now fitted and finessed, Molnar had a 32GB VRAM system at their disposal – that’s a PC with RTX 4080: 16GB VRAM, Ada architecture and Tesla V100: 16GB VRAM, Volta architecture. They note you can get Tesla V100s with 32GB of VRAM, but they are double the price.

Getting the system to make use of this 32GB of total VRAM for LLMs wasn’t tricky, says the DIYer. They used NixOS with a legacy Nvidia driver that overlapped support for both Volta and Ada architectures. Testing a local LLM, they got a 27 billion parameter model running at 32 tokens per second, which they say is “fast enough for interactive use” and faster than most cloud API alternatives.

Nvidia and SK Group this week signed letters of intent to formalize their new strategic relationship valued at more than $500 billion. The strategic collaboration is multifaceted and includes a long-term memory supply agreement with SK hynix unveiled in June, SK Telecom's plans to build a 2-gigawatt AI data center based on the latest Nvidia hardware, and future expansions of AI infrastructure.

In addition to the multi-year memory supply and co-development agreement between Nvidia and SK hynix, the key part of the strategic relationship is SK Telecom's planned 2-gigawatt AI data center in South Korea. The installation will rely on Nvidia's DSX AI factory platform and deploy Vera Rubin accelerated computing systems equipped with SK hynix HBM4 memory. The first AI data center is set to enter service in 2027. The companies intend to use this infrastructure to support sovereign AI, enterprise AI, physical AI, and agentic AI deployments across South Korea and the Asia-Pacific region. In addition, the companies will work together on expansion of SK's AI infrastructure going forward, which is a rather vague way to say plans to deploy future AI platforms from Nvidia.

The most important part of the announcement is, of course, the gargantuan value — $0.5 trillion — of the intended strategic relationship. Based on what is disclosed, the figure is best interpreted as the aggregate value of commercial activity expected between the companies over several years, as it bundles together AI infrastructure construction and a long-term memory supply agreement under one umbrella. That activity likely will include the following:

• SK Telecom's purchases of Nvidia GPUs, networking equipment, systems, and other hardware for its AI data centers.
• Supplies of SK hynix memory to Nvidia under the long-term supply agreement.
• Revenue of Nvidia's ecosystem partners involved in building the DSX AI factories (OEMs, ODMs, networking, storage, cooling, power, etc.).
• Potential future expansion beyond the initial 2 GW deployment.

Speaking of the 2 GW AI data center, it is safe to say that it is going to use thousands of NVL72 VR200 racks and hundreds of thousands of Vera CPUs and Rubin AI GPUs. Unfortunately, this is as accurate as we can get with the rather vague announcement.

Nvidia describes DSX as a complete AI factory blueprint that combines its accelerated computing hardware, networking, software stack, and partner technologies into a data center-scale platform designed to deliver the lowest-cost token generation and maximum energy efficiency. Meanwhile, NVL72 VR200 will come with 166 kW – 240 kW per-rack power consumption ratings, whereas DSX can be deployed in various kinds of facilities with different power usage effectiveness (PUE). Since we do not know which NVL72 VR200 configuration SK Telecom plans to use, and since the PUE of the upcoming SK Telecom facility is unknown, it is impossible to estimate the number of racks and AI accelerators with any accuracy.

Jensen Huang joined X last month and used his first post Friday to promote Open Weights and American AI Leadership, a three-page policy letter published the same day and co-signed by 25 companies, including Nvidia, Microsoft, Meta, IBM, Dell Technologies, Palantir, and Hugging Face.

The letter asks Washington to avoid what it calls "premature restrictions on downloadable AI models," and comes just four days after the Trump administration was reported to be reviving a push to ban Chinese models — though the document doesn't directly mention China, Moonshot AI, or DeepSeek. Notably missing from the co-signers are OpenAI, Anthropic, and Google.

The 25 names break down into chipmakers, server vendors, cloud operators, enterprise software firms, security companies, and venture funds: Nvidia, Dell, Microsoft, IBM, Box, ServiceNow, CrowdStrike, Palantir, Telnyx, Replit, Perplexity, Andreessen Horowitz, Y Combinator, and Emergence Capital among them. The model developers on the list, Meta, Mistral, Black Forest Labs, Arcee AI, and Reflection, all publish weights already.

Also present on the list of signatories is the Linux Foundation, which stewards the OpenMDW-1.1 license Nvidia used to release Nemotron 3 Ultra in June, a 550-billion-parameter model that Artificial Analysis scored at 47.7 on its intelligence index against 53.9 for Moonshot's Kimi K2.6.

"The world needs both frontier closed models and frontier open models," Huang wrote in his X post. At Nvidia's CES 2026 press Q&A earlier this year, he put a figure on the shift, saying one in every four tokens generated today comes from an open model. Weights that anyone can download get served from enterprise clusters, regional clouds, and on-premises racks rather than a handful of hyperscaler API endpoints, and those buyers have no in-house TPU or Trainium program to buy instead. The letter's policy section asks for expanded compute access for startups and researchers, alongside public investment in shared datasets and evaluation frameworks.

The letter goes on to urge policymakers not to treat distillation — the practice of training one model on another's outputs — as misappropriation, arguing that unlawful extraction from closed models should be handled through targeted legal frameworks, rather than broad limits on the technique.

Treasury Secretary Scott Bessent said on Fox Business earlier this week that the administration would examine Chinese open-source models for intellectual property theft and could sanction the companies behind them, telling the program that officials had found watermarks from U.S. large language models in Chinese systems. Huang told Axios two days later that American firms should be allowed to use Chinese models, calling claims of Chinese backdoors a misconception. The distillation passage is the only part of the letter that doesn't concern open weights.

On this day in 2006, AMD confirmed its acquisition of graphics chip firm ATI. AMD stumped up a cash and stock deal worth a total of $5.4B for the Canadian PC graphics innovators. With 20/20 vision now 20 years on, we can see the deal helped AMD prosper on three fronts: continuation and innovation of Radeon GPUs, the rise of the APU, and AMD’s dominance in the console business. That’s not all, of course, and it is also interesting to recall that AMD approached Nvidia before it bought ATI.

AMD CEO Hector Ruiz and ATI CEO Dave Orton appeared together in New York on the morning of July 24, 2006, to publicly announce the deal. Ruiz told the press that the deal, unanimously approved by the directors of both companies, would "reinvent our industry." The AMD CEO went on, “We believe AMD and ATI will drive growth and innovation for the entire industry, enabling our partners to create differentiated solutions and empowering our customers to choose what is best for them.” Orton added that “Joining with AMD will enable us to innovate aggressively on the PC platform.”

In the next couple of years, the ATI graphics brand slowly melted away. For example, the ATI R600 (Terascale 1, unified shader architecture) GPU, which was in development at the time of the acquisition, would become the Radeon HD 2900 series under AMD branding. As a transition product, some HD 2900 XT boxes would still carry ATI packaging and branding. Taking the cooling solution off an AMD graphics card, you might still see an ATI GPU under the thermal paste, all the way up to around 2010. In 2011, Tom’s Hardware published its 25 Years Of Graphics History: A Farewell To ATI, In Pictures, which is a must-read for fans of the era.

While graphics card development rolled on in a tit-for-tat battle with Nvidia, AMD’s next fortuitous chunk of synergy came from integrating its Radeon IP alongside its CPU cores to create APUs. It started this journey with the AMD Fusion line in 2011. Nowadays we have APUs that have taken this vision far further, with the Ryzen AI Max / Max+ (Strix Halo and Gorgon Halo) series. The integrated graphics on these processors pack up to 40 RDNA 3.5 Compute Units and can go toe-to-toe with desktop graphics like the RTX 4060/4070, depending on workload. They also benefit from unified memory, allowing users to configure oodles of VRAM, if they have it spare.

As the Radeon developers forged ahead moving from the GCN to RDNA graphics architecture era, AMD saw an opportunity in the console space. From the early to mid 2010s, AMD made inroads into APU development that meant the processors became attractive solutions for console developers. It still holds pretty tightly to that market today, which has spilled over to handhelds. However, Intel looks far more serious in this market now, with Panther Lake and B390 iGPUs. Moreover, Nvidia could surely make a dent on consoles with Arm plus GeForce semi-custom SoCs if it wasn’t living it up in the lucrative AI market.

Red and green would never be seen

Before the AMD and ATI deal was inked, reports indicated there were chances of a similar merger involving AMD and Nvidia. Our 2012 report on this ‘missed opportunity’ suggests Jensen Huang was being quite difficult during the negotiations. Apparently the man in the leather jacket insisted that he become chief executive of the combined company. That made Hector Ruiz pretty cool on the prospect, so AMD’s attention was diverted towards ATI.

Nvidia CEO Jensen Huang thinks that American companies should be allowed to use Chinese AI models, even as Washington is trying to ban them. When Axios co-founder Mike Allen asked Huang in an interview if Americans companies should be allowed to use Chinese AI models, Huang responded with “absolutely.” The answer comes right after Chinese firm Moonshot AI released a 2.8T open-weight model called Kimi K3, which — although it isn’t as powerful as frontier models like Fable 5 — is comparable to GPT 5.5 and Claude Opus 4.8 while costing just a third of these models.

One of the biggest concerns of U.S. leaders have is that these AI models might come with vulnerabilities that the Chinese government can use to attack American interests, but Huang said that this is an incorrect assumption. “There is a misconception that somehow there are backdoors that are somehow connected to China in some way,” said the Nvidia chief. “You download the models, you can fine-tune it, you can enhance it, you can guardrail it as you desire.”

Huang shares the same sentiments about American AI models. Just last month, the U.S. enforced an export restriction on Anthropic’s Mythos and Fable 5, citing security threats — although access was eventually restored after its developer placed a filter to block these tools from identifying software vulnerabilities. OpenAI’s ChatGPT-5.6 received the same treatment, and Washington warned the firm that it should not release its latest model without getting the green light from the government. Huant argues that, instead of restricting access to these powerful models at launch, AI firms should make their models available to all and make them more secure through rapid testing and fixes.

But even as he advocated the need for everyone to have access to closed models, Jensen also noted that various industries, such as the sciences and cybersecurity, need open models as well. He claims that these models make AI more secure, as other people can inspect them to look for weaknesses and fix them as required.

“If everything just becomes one single model, one single point of attack, one single source of failure, I think the world is much, much more vulnerable,” Huang said.

As for the market’s negative reaction every time cheaper, open-weight models become available, the Nvidia CEO says that investors misunderstand their impact. Huang said that this happened when DeepSeek arrived for the first time, and it’s happening again with the arrival of Kimi. He says that these open models, which cost less to run, will encourage more people to use AI. So instead of cutting data center demand, these cheaper, more efficient models are actually good for the industry in general because they will drive demand. (And with higher demand, there’s more incentive to build data centers and buy AI GPUs, which is ultimately good for Nvidia.)

Nvidia invited a group of about a dozen journalists out to the company's HQ to learn more about its Vera CPU, as well as how it fits into the larger Vera Rubin NVL72 rack design at the heart of Nvidia’s next-gen agentic AI platform. Part of that was seeing Vera Rubin in action, not as a disassembled tray on stage or a rack with a few blinking lights at a trade show — real racks running real workloads in a (partially) real data center. And we got to see those racks in action at Nvidia’s Engineering SuperLab.

It’s not a proper data center, or at the very least, it’s a sub-optimal data center. Nvidia was clear that the Engineering SuperLab is built for engineers, allowing them to quickly stand up and swap out racks to see the hardware in action. You could sense a bit of insecurity in the air; if Nvidia were building a proper data center, it wouldn’t look like this. This lab is where the engineers live, and if you’ve ever been around a group of engineers with a lot of hardware to play with, you know that things aren’t always as tidy as you’d expect in a proper data center.

The SuperLab Nvidia showed us is one of four nondescript locations near Nvidia HQ. These locations haven’t, up to this point, been disclosed. Each of the four locations has popped up over the last two years, giving Nvidia some floor space to play with as it rolls out new hardware.

The hardware in question here is the Vera Rubin NVL72 rack, but we saw a few other demonstrations, as well. Most notably, Nvidia showed us a sidecar running 800VDC power into an NVL72 rack. Nvidia also laid out some parts, demonstrating the assembly process for a Vera Rubin tray, which slides together with various retention arms in a matter of minutes.

This is a look behind the scenes of our tour, how the Engineering SuperLab is set up, and some choice data center eye candy. We’ve published a full breakdown of the Vera CPU and how it fits into Nvidia’s larger AI infrastructure, which goes into the technical details of the platform. Here, we’re mainly giving you a peek behind the curtain.

Nvidia Vera Rubin NVL72 running in the flesh

Vera Rubin is in full production, and we’ve seen some short videos of racks being stood up in data centers. But this is our first look at a rack running a real workload in the flesh. Nvidia says the racks here are running some workloads for OpenAI, in fact, and as you can see from the image above, it looks like some trays are running OpenAI’s GPT‑Rosalind model.

On the front of each tray here, you can see the ports for the dual ConnectX-9 NICs, along with the Bluefield-4 DPU in the middle. Around the back is Nvidia’s NVLink spine, an almost mediaeval-looking contraption, with sharp pins that connect the various trays together. It houses 5,000 copper cables that measure over two miles in length, delivering up to 3.6 TB/s of bandwidth per GPU and 260 TB/s of scale-up bandwidth per rack, on Nvidia’s sixth-gen NVLink.

Each Vera Rubin NVL72 rack houses 18 compute trays and 9 NVLink switch trays, the latter of which orchestrate communication between the various trays to function as one large, unified system. Nvidia demonstrated the MGX NVL design for us, which is a single reference rack with 72 Rubin GPUs and 36 Vera CPUs. Nvidia’s MGX ETL design replaces the NVLink spine with either a Spectrum-X Ethernet spine or direct chip-to-chip spine for a scale-out system featuring up to 256 GPUs.

The business-end of things is around the back of the racks, though. Although the back is clear of cabling thanks to the NVLink spine, power and coolant delivery are still a major factor. The organized chaos of the piping and cabling you can see in the images below shows just how much goes into standing up even one rack, let alone dozens or hundreds in a data center.

Nvidia’s previous-gen GB200 and GB300 NVL72 racks featured hybrid cooling, but Vera Rubin trays are entirely cooled by liquid. There aren’t any fans, which Nvidia says could, eventually, lead to much quieter data centers. That wasn’t the case in the lab here, which still called for eye and ear protection. I didn’t have a decibel meter handy — imagine if I carried one around with me casually — but my guess is that it was somewhere around 80 to 90 decibels inside; louder than an A/C unit, but quieter than a motorcycle. That’s a fairly typical noise level for a data center.

Regardless, Vera Rubin trays are entirely liquid cooled, with a process called “dry cooling.” Assuming the inlet temperature of the coolant is 45 degrees Celsius or less, Nvidia says it’s able to cool the entire system with a heat exchanger. If true, that would cut costly (in terms of power, space, noise, water consumption, and actual dollars) chillers out of the cooling equation.

Massive piping brings the coolant in (usually antifreeze or deionized water) at the top, and there are outlets at the bottom of the rack to move the warmed coolant out. Along the way are a series of inlet connections that allow trays to automatically hook into the cooling system, leaving just the main connections at the start and end of the loop. Nvidia has standardized everything on a Vera Rubin NVL72 rack with the Open Compute Project (OCP), even as far as shipping specifications. The company says just 47 minutes passes from when the truck pulls up to powering on the rack.

800VDC power for next-gen AI infrastructure

Nvidia also showed us an 800VDC “sidecar” in action. If you’re unfamiliar, Nvidia (along with other AI infrastructure companies) have been pushing for a new 800VDC power delivery system for modern data centers to reduce AC/DC conversion inefficiencies, as well as deliver the necessary wattage to racks without pushing into current ranges of thousands of amps.

A single Vera Rubin NVL72 rack can easily consume over 200 kW, which is a challenge for traditional power infrastructure in a data center. With current Grace Blackwell racks, power shelves convert the AC power coming into the facility (at 415V or 480V) to 48V/54V Direct Current for distribution within the rack. The problem here is pretty straightforward. If voltage stays constant, and wattage increases, then current also needs to increase. And more current means thicker bus bars, more conversion inefficiencies, and more rack space dedicated to power delivery.

Again, the solution that 800VDC represents is pretty straightforward. If wattage increases and current stays constant, voltage also has to increase. The idea is to convert AC power from the grid to DC power once, and then use a series of DC-to-DC converters within the rack, allowing for denser compute and better power efficiency.

This is not an easy problem to solve, as data centers need to change how power is brought into the facility, not just how it’s converted, stepped down, and moved around. Here, Nvidia showed off a sidecar, which is a rack filled solely with power equipment. This is a “retrofit,” as analyst firm SemiAnalysis calls it, representing the first in a series of transition phases to 800VDC. 415V/480VAC is still distributed throughout the facility, but it flows into this sidecar rather than power supplies within the rack. The sidecar rectifies the 415V/480VAC to 800VDC and feeds adjacent racks.

This is all an explanation to show some pretty interesting power infrastructure at play in this lab. Nvidia isn’t the first, nor only, company pushing toward 800VDC infrastructure, and companies like Google, Meta, and Microsoft have contributed to open sidecar designs like the Mt. Diablo spec. Still, it’s interesting to see one of these sidecars in action.

If you haven’t seen a peek behind the back end of a rack, you can see the large red connectors in the gallery above that bring power into the racks. You can also see the massive power cables and connectors at the rear of the 800VDC sidecar.

Nvidia’s next-gen AI infrastructure laid out

At the front of the facility, Nvidia laid out all of the components of its next-gen AI infrastructure. There’s nothing here we haven’t already seen before, though it's normally seen buried in trade show displays or featured on stage during a keynote.

First is the Vera Rubin NVL72 tray itself, which you can see above sitting next to a GB300 tray. Both are DGX designs, meaning they’re fully built and integrated by Nvidia, and you can see just how stark of a difference there is in assembly right away. The Vera Rubin NVL72 tray features only two cables, no hoses, and no fans. At the rear where the two Super Chip boards live, Nvidia demonstrated a retention arm that allows the boards to slide in and out in a matter of seconds.

The Vera Rubin tray is much cleaner, but it also makes better use of the space. It doesn’t include fans, which you can see take up a significant section in the middle of the tray in the GB300 design. With Vera Rubin, those fans are replaced with the thick midplane you can see above, offering a communication channel between the two ConnectX-9 NICs and Bluefield-4 DPU at the front of the tray.

Holding one of the two cables inside of a Vera Rubin NVL72 compute tray is the busbar, which handles power routing for the different chips inside the tray.

The Vera Rubin NVL72 tray is, of course, not the only deployment of Nvidia’s next-gen AI hardware. The company also showed us a CPU-only tray with eight Vera chips, offering up to 256 chips within a rack. That gives us a closer look not only at the chip itself, but also the SOCAMM2 LPDDR5X memory system, offering similar density and modularity as traditional RDIMMs at a far lower power cost.

Although most of the modules were unlabeled, we were able to snag the picture you can see above showing where the modules came from. These are 128GB SOCAMM2 modules from Micron running at 6400 MT/s, and as you can see from the photo, the slots aren’t all populated. This is the big advancement with SOCAMM2, offering up modularity for a memory standard that is otherwise soldered.

Nvidia’s NVLink handles scale-up communications, split across NVLink switches in the rack and the NVLink spine that you can see above. Nvidia describes sixth-gen NVLink as “putting the oven in the car.” Its goal is to get all of the chips in a rack communicating with each other, ensuring critical operations (like baking the pizza) happen as close to the destination as possible.

Scale-out communication, on the other hand, is handled with ConnectX-9 NICs in the tray and the Spectrum-X CPO (co-packaged optics) switch tray. The Spectrum-X switch tray is massive, and it’s a good illustration of just how much physical space is dedicated to communication (over raw compute) in a modern AI data center.

Of course, the size of the switch tray depends on how wide the scale-out infrastructure is. Nvidia also showed a smaller Spectrum-X CPO tray for smaller deployments. That's all we managed to see at Nvidia's AI data center SuperLab. Vera Rubin is in production, and will roll out in the second half of 2026.

Nvidia’s Vera CPU is its first bid to become a key player in the data center CPU market. Although Grace has seen some success (most notably with Grace standalone deployments at Meta), Vera is Nvidia’s first CPU with a custom core design. It’s arriving at an ideal time, as well, with the server CPU market exploding in the last few months on the back of agentic AI demand.

Vera isn’t a chip built to chip away at the market share of AMD and Intel in the cloud. It’s built to grab market share in an expanding market, as hyperscalers look to widen AI infrastructure beyond legacy clouds. As such, it’s designed in a much different way than Nvidia’s x86 competitors, and it even holds some unique architectural design points compared to the swath of Arm-based designs.

Nvidia has slowly revealed more details about Vera as it ramps into general availability, which is on track for the back half of this year. Now, we have a full picture of the chip. Nvidia shared its Vera white paper, along with unofficial SPEC CPU 2026 results comparing Vera to AMD’s Turin-based Epyc 9755.

We’re going to break down the white paper here, including all of the details about the Olympus core and a look at the benchmarks Nvidia ran. At the end of this piece, we’ll also take a brief look at the larger context of Vera and how it fits into Nvidia’s wider AI ecosystem compared to standalone deployments.

But plenty of ink has been spilled about Vera’s technical capabilities and Nvidia’s next-gen AI infrastructure vision. Let’s start with the important thing: the benchmarks.

Nvidia Vera CPU benchmarks

We’ve seen Vera in action before, though only through a series of selected benchmarks ran at Nvidia HQ by Phoronix. In the Vera white paper, Nvidia shared benchmarks for SPEC CPU 2026, specifically the integer suite from SPECrate, against AMD’s Epyc 9755, with both chips running in a dual-socket configuration. Before getting into the results, there are some important notes about how SPEC runs work, and the reporting criteria for them.

Nvidia’s run here isn’t official, as Vera was tested in a reference system due to the fact that it’s not broadly available yet. It’s ramping for general availability in the second half of the year. Due to that, Nvidia is unable to report its results. That’s why you see “estimated” in some of the charts below. Nvidia ran SPEC CPU 2026; it’s not extrapolating expected performance like we’ve seen from AMD so far with its upcoming Venice chips.

SPEC CPU 2026 is split into four suites, but Nvidia tested the SPECrate integer suite, which is focused on system throughput with integer-based workloads. The “rate” result is looking at how much work is completed within a certain amount of time. Here, each thread in the system has a copy of the workload. The score is how much time it takes for those workloads to complete, regardless of thread count, naturally giving chips with more cores an advantage.

If you want more detail on the benchmarks included in the suite, make sure to read our original coverage of SPEC CPU 2026. Here are the overall results:

Nvidia didn’t share the exact results for the 9755 it tested, short of the overall score of 898. Taking that overall score into account, Vera is 3% ahead of the 9755. It’s worth noting that Vera is ahead here despite a large thread disadvantage. An overall score of 898 for a dual-socket Epyc 9755 system isn’t unreasonable compared to publicly-submitted SPEC CPU 2026 runs, though higher results have been published. SPEC CPU ships as source code, which the tester must compile with their compiler of choice, and that can heavily influence results (particularly with vendor-specific compilers). Nvidia used GNU 15.2 with both systems.

Above, you can see Vera’s results stacked up against the 9755, but these aren’t comparing the numbers directly. Nvidia has normalized the per-core performance, which isn’t how SPECrate results are normally shared. According to the overall numbers, Vera is still completing more work within the same amount of time, despite a thread disadvantage, but the margins aren’t in the range of a 70% or 80% advantage as the above chart suggests.

We asked Nvidia about the results given that they're obfuscated by comparison; we could not reverse-engineer the Epyc 9755's scores with the information Nvidia has provided. Here's the response it gave: "Per-core performance under a fully loaded socket is important because agentic AI and RL run many sandboxes concurrently, while each agent step remains sequential and latency-sensitive. It measures how much performance each core sustains amid contention for shared power, memory, cache, and fabric. We therefore normalize by physical core, with SMT enabled on both systems."

The “agentic” workloads Nvidia has highlighted here are code compilation and interpretation workloads, which is something an agent is often doing, querying repos for dependencies and building source code. Below are data science workloads (or Exploratory Data Analysis), and below that are data processing workloads like SQLite database management. The results here align with Nvidia’s overall messaging of Vera, that it’s highly competent at data-rich, backend operations.

Although Nvidia is sharing per-thread results, it argues that SPECrate is still the correct benchmark to run. The per-thread results here are in the context of a fully-loaded socket. Here’s the justification from the white paper: “This metric is non-trivial for agentic AI and RL systems, where many sandboxes, tools, and environments run concurrently rather than as isolated single-thread tests. Fully loaded per-core performance captures how well each core sustains throughput while sharing socket-level power, memory bandwidth, cache, and fabric resources.”

In addition to running the workloads, Nvidia analyzed the code execution for architectural benchmarks, which you can see in the gallery above. Nvidia claims an overall IPC gain of up to 1.9x compared to Turin, up to 2.3x more branch predictions and 3.5x taken branches per cycle, and up to 2.4x higher instruction fetch operations per cycle.

Outside of SPEC, Nvidia shared a few benchmarks highlighting the capabilities of the Olympus core. First up is PageRank, an algorithm developed by Google to originally rank web pages, which highlights Olympus’ prefetch engine. Nvidia scaled this workload to higher core counts, showing Vera maintaining much of its single-core performance up to 32 cores, while the Turin chip hits a wall around 20 cores.

In addition to the above results, Nvidia shared some tests of the Vera memory system compared to Turin. These microbenchmarks are good for validating Nvidia’s specifications, but they’re looking at architectural performance, not application performance. An architectural advantage translates into a performance advantage, but not always in a linear, expected fashion.

Nvidia used internally-developed tools for the memory tests, though they're available on GitHub for anyone to run.

First is loaded memory latency, stressing the memory subsystem as bandwidth usage increases. Vera has much higher bandwidth overall, but you can see the Turin chip hit a latency wall below its maximum, which Nvidia attributes to Non-Uniform Memory Access (NUMA) domain traversal and CCD-to-CCD latency.

Looking at per-core bandwidth, Nvidia claims Vera provides more than four times the bandwidth of AMD’s 9755. The suggestion here is that “real-world” per-core bandwidth is even better than Nvidia’s specs lead on (or perhaps worse than AMD’s).

Maybe the most consequential of these tests is the one you can see above, looking at core-to-core latency. It’s no secret that crossing the CCD on AMD’s chiplet-based architecture incurs a big latency penalty. You can see that in action even in our Ryzen 9 9950X3D2 review, and the penalties compound as you scale up the number of CCDs.

In fairness to AMD here, chiplet-based designs aren’t built for this type of cross-CCD traversal, preferring to keep workloads localized and optimizing for core density. Vera’s design goal is clearly to keep latencies consistent across the entire die and sacrificing core density in the process. Nvidia’s Ian Buck told us that this design trade-off “will come at the cost of the legacy workload,” when we recently visited Nvidia HQ.

That’s important context. Nvidia isn’t gunning to steal existing market share from AMD and Intel as much as it’s trying to grab market share in an expanding market before AMD and Intel can. Some financial institutions (including Morgan Stanley and Bank of America) suggest the server CPU market could double in size (or grow even larger) by 2030. That context is important because there will be a continuing demand for CPUs that can handle workloads Vera is not optimized for, and it’ll be interesting to see how AMD and Intel tackle that dynamic with future products, trying to keep a legacy base of customers while pushing ahead into the expanded market.

Nvidia clearly has a vision of how that expanded market looks, and to that end, hasn’t shared SPEC CPU floating point results. Presumably, this is due to the fact that SPEC’s vectorized suite is focused primarily on HPC workloads, whereas Nvidia focused on what it believes are critical agentic workloads that are integer-based. Vera has a vector engine complete with SVE, but that doesn’t seem like Nvidia’s focus.

In an end-to-end Nvidia system, those vectorized workloads would be offloaded to a Rubin GPU. Still, we don’t have any vector results for Vera yet. Up to this point, we’ve only seen integer results, which is strange given the memory system at play in Vera.

Vera is Nvidia’s first CPU with a core design created in-house, which is the Olympus core. It’s built on Arm v9.2-A, but the design was created by Nvidia, unlike Grace, which leveraged a stock Arm design. Each Vera CPU has 88 Olympus cores on a monolithic die, breaking from the chiplet-based designs available from Nvidia’s x86 competitors.

Nvidia says Vera comes with a 1.5x increase in instructions per cycle (IPC) throughput compared to Grace, and 50% higher performance compared to x86 competitors (it seems that number is per-thread performance with a fully-loaded socket). Nvidia has a single 88-core design with Vera that supports spatial multithreading for 176 threads.

The CPU has a configurable TDP range of 250W to 450W. It uses a SOCAMM2 LPDDR5X memory system with capacity of up to 1.5 TB and speeds up to 9600 MT/s, and comes with 164 MB of L3 cache and 2 MB of L2 per core. Vera includes significantly less L3 than Intel’s highest-specced Xeon 6 and AMD’s Zen 5 chips. It actually has more L2 than L3 overall. This, presumably, is due to Nvidia’s fabric, which distributes the L3 in a mesh across the monolithic die.

Below, you can see a layout of the Olympus microarchitecture. Nvidia has disclosed some of the highlights of the architecture previously, such as the 10-wide instruction decode and neural branch predictor, but we now have a full view of the architecture courtesy of Nvidia’s Vera white paper.

The front end starts with Nvidia’s neural branch predictor that can run two branches per cycle “with zero penalties,” according to Nvidia. Research on neural branch prediction dates back to the late 90s, but Nvidia says it has a “novel” neural branch predictor, perhaps building on previous research such as BranchNet.

The BPU feeds into the Instruction Fetch Unit, which holds 64 KB of L1 instruction cache, and loads into a decode queue that supports 48 instructions (we’ll go into the memory/cache layout later). At the last stage of the front end is that 10-wide decode, feeding more instructions into the execution engine per cycle than the 8-wide decode in AMD’s Zen 5 microarchitecture.

Past the front end, the mid-core rename / allocation engine is built to keep instructions moving while waiting on dependencies. In addition to renaming and allocation, instructions work through value prediction, which can speculatively execute the instruction, and memory renaming, where the instruction can move forward while a load is happening if the data relationship can be determined.

Inside the execution engine, Nvidia includes eight simple Arithmetic Logic Units (ALUs), two complex ALUs, and four branch units for resolution. For SIMD instructions, the execution engine includes a vector cluster for Arm’s Scalable Vector Extension (SVE), including six vector units that support 128-bit SVE instructions at FP8 precision, along with two crypto-enabled vector units that can handle AES, SHA, and SM3, among other prominent algorithms. Keeping data moving through the engine are four load units and two store units.

The cores support spatial multithreading, giving a Vera CPU with 88 cores access to 176 threads. Traditional SMT time-slices execution, giving both threads access to all of the core resources and sharing them as instructions execute in parallel. With spatial multithreading, each thread of an Olympus core has access to dedicated resources, allowing one of the threads to execute with high-throughput while the other thread handles simple tasks, or to operate as two independent execution environments.

The execution resources are partitioned, explaining the wide decode front end. It’s not clear, however, if the SMT implementation can also opportunistically grab resources, particularly in the scenario Nvidia describes where one of the threads is maximizing throughput while the other handles smaller tasks.

There’s a lot going on in Vera between the 10-wide decode, neural branch predictor, and spatial multithreading, but perhaps the most significant architectural design point is Nvidia’s second-generation Scalable Coherency Fabric (SCF). It underpins Nvidia’s approach of using a monolithic die as opposed to a chiplet-based design, distributing last level cache in a mesh across the die and avoiding the cross-CCD latency penalty with localized L3.

The mesh distributes data through a series of Coherency Switch Nodes (CSNs) that serve as routing points between cores and the 164 MB of distributed L3. These routing points further connect the cores and L3 to the memory system, I/O, and NVLink C2C for cache-coherent communication between chips. Nvidia’s benchmarks comparing Vera to AMD’s Epyc 9755 show that AMD can achieve slightly lower core-to-core latencies within a cluster, but Vera maintains significantly better core-to-core latency across the die, as expected.

Nvidia says SCF inside Vera has 3.4 TB/s of bandwidth, enabling faster core-to-core communication, especially when traversing the die. However, Vera also supports Memory System Resource Partitioning and Monitoring (MPAM), allowing portions of L3 to be partitioned in multi-tenant environments.

Vera uses SOCAMM2 LPDDR5X, which is a relatively new advancement that Nvidia’s competitors haven’t had the chance to benefit from. With the use of SOCAMM2, LPDDR5X provides similar modularity and capacity as traditional RDIMMs, but at significantly lower power draw.

The memory can run at up to 9600 MT/s, with aggregate bandwidth of 1.2 TB/s and per-core bandwidth of 14 GB/s, doubling the bandwidth of Grace. The Vera board supports eight SOCAMM2, offering capacity ranging from 256 GB to 1.5 TB. Nvidia claims a “fully populated” memory subsystem consumes between 30W and 40W depending on capacity.

For I/O, Vera supports PCIe 6.4 with 88 lanes per CPU and bifurcation support down to x2. It also supports CXL 3.1.

Unlike Grace, Vera includes Arm’s Confidential Computing Architecture (CCA) and Realm Management Extension (RME), including Device Assignment and Coherent Device Assignment, offering a boon to multi-tenant environments where VM isolation is key. Nvidia also implements TDISP for coherent devices, allowing for encrypted communication between GPUs and PCIe devices.

Nvidia is already sampling Vera as a standalone chip to partners, and it says it will continue to do so, but the vision is an end-to-end solution built on Nvidia’s CPUs, GPUs, switches, NICs, and even rack specifications. Nvidia doesn’t make all of these individually, at least not at scale — just like with desktop graphics cards, Nvidia provides its MGX reference design, which customers can purchase, but partners also offer their own racks, some built solely to Nvidia’s specifications and others with more speciality.

Each tray comes with two Vera Rubin superchips, each of which contain a single Vera CPU to two Rubin GPUs, giving you two CPUs and four GPUs per tray. At the front, Nvidia partitions off three spaces, with the MGX design carrying two NVIDIA ConnectX-9 SuperNIC on either side and a Bluefield 4 DPU in the middle. Critically, this design doesn’t include any hoses or fans. It’s entirely liquid cooled, and it contains just two cables throughout the entire tray.

Nvidia demonstrated this cable-less design, showing the Vera Rubin Superchip sliding in and out of the track with a retention mechanism in a matter of seconds. The company says assembling the rack takes less than a few minutes and is handled entirely by robots, which is a far cry from GB200 and GB300 trays.

GB200 and GB300 trays are dense designs, but they’re also cluttered with cables and hoses. Nvidia says this massively slowed down production time, eventually leading to production issues that delayed Nvidia’s rollout. The company says that won’t happen with Vera Rubin and its largely cable-less design. Whereas a Grace Blackwell tray took around two and a half hours to assemble by a human, the company says a Vera Rubin tray is assembled within five minutes and entirely automated by robots.

Each tray needs to dissipate several kilowatts of heat, which Nvidia says it’s able to do using dry cooling. Liquid cooling is nothing new in the data center, either through an external chiller (essentially an A/C unit) or evaporation, where a fan evaporates water over a mesh and cools without the need for a compressor. With Vera Rubin, Nvidia uses “dry cooling,” with a maximum inlet temperature of 45 degrees Celsius.

Nvidia says it’s able to get the full performance out of a tray given an inlet temperature of 45 °C, allowing trays to operate without an additional water consumption in environments up to 100 degrees Fahrenheit. The tray essentially uses a large closed-loop similar to what you find from a consumer AIO, just scaled up. Water moves out of the tray and outside the data center, and it passes through a radiator where fans dissipate the heat. There’s some extra power consumption from water pumps and fans, but not nearly on the scale of evaporation methods or chillers.

The result is a tray completely free of fans, essentially noise-less in operation, and doesn’t strain local water infrastructure. That’s what Nvidia says, at least. In many locations around the U.S. where data centers are located (Texas and Virginia chief among them), temperatures easily climb above 100 °F during the Summer, prompting some sort of backup method of cooling. Nvidia says the external temperature ceiling can go higher depending on different factors — running at lower power, for example, and using more efficient heat exchangers — but under normal conditions, 100 °F is the ceiling.

It’s worth noting that nothing about a Vera Rubin tray explicitly requires this method of dry cooling; the hardware is just capable of offering full performance with an inlet temperature of 45 °C.

In addition to an NVL72 design, Nvidia has a Vera standalone deployment that compacts each tray into a series of SOCAMM2 slots and Vera chips. In Nvidia’s 48U MGX design, a standalone Vera deployment can include up to 256 CPUs in a rack.

Underpinning scale-up communication is Nvidia’s sixth-generation NVLink, which is deployed as switches in the rack and connected to compute trays using Nvidia’s NVLink spine. You can see the spine on its own in the image above, which features over two miles of thin copper wire to allow every tray in the rack to communicate with each other.

Localizing storage, networking, security, and telemetry operations is Nvidia’s BlueField-4 DPU. A Vera Rubin NVL72 compute tray includes a single DPU and two ConnectX-9 NICs to maximize CPU/GPU utilization. You can read more about Bluefield 4 in our original coverage from GTC.

Nvidia’s Ian Buck, vice president of hyperscale and high-performance computing and the inventor of CUDA, says the company has “shipped... let's put it in the hundreds of thousands of Grace standalone servers.” In May, Nvidia disclosed that it had shipped over 2.5 million Grace CPUs in total, and the company announced a partnership with Meta to deploy standalone Grace servers in February. Buck’s comments suggest the scale of deployment may be even larger, however, as Nvidia tries to compete in a market dominated by other players.

It’s an interesting comment, though not a surprising one. Nvidia has become the dominating force of Silicon Valley as demand for its GPUs skyrocketed during an unprecedented data center buildout for AI inference. Since peaking earlier this year, however, around $1 trillion in Nvidia’s market cap has been wiped away as investors rally behind CPU makers like Intel. Evolving agentic AI workloads have changed the hardware balance, shifting away from as many as eight GPUs per CPU, toward a one-to-one ratio in some cases.

Nvidia wants to ride that train with its new Vera CPU, which was architected specifically for those types of workloads. Even before the recent rise of agents, however, Nvidia says it has seen demand for its CPUs for data-hungry workloads. “They weren’t running a web server [with Grace]… or they aren’t being used for, what the cloud uses, of cheap, dollar-per-core,” Buck said. “They were being deployed for the backend, data-rich operations, like the data processing.”

Grace represents an on-ramp for Nvidia into data center CPUs. It uses 72 stock Arm Neoverse V2 cores, but it’s differentiated by Nvidia’s Scalable Coherency Fabric (SCF). Vera uses an updated SCF, but it also features Nvidia’s first custom core design, called Olympus. Grace cracked the door, and Vera represents Nvidia's big entrance into the market against AMD and Intel.

Regardless of where Vera ends up in the battle of next-gen data center CPUs — which is heating up now, as AMD is expected to launch its Zen 6 Venice CPUs this week — the design is vastly different from what we’ve seen out of Intel and AMD. Most notably, Vera is monolithic, placing all of its 88 cores on a single piece of silicon. AMD and Intel, years ago at this point, pivoted away from monolithic dies in favor of chiplets, allowing an extremely high density of cores at the cost of latency and coherency issues. Vera is radically different in that regard, not only being built on a single die, but also dedicating significant die space to the fabric.

“One of the reasons we don’t have 128 cores is because we’ve dedicated so much of the die area toward the fabric,” Buck said. “It’s 3.4 TB/s of bandwidth inside of that CPU that is dedicated toward allowing every core to talk to every cache, every memory [controller] at full speed without any collisions.”

For clarification’s sake, Buck is referencing 3.4 TB/s of core-to-core bandwidth in Vera. There’s up to 1.2 TB/s of aggregate memory bandwidth (14 GB/s per core) through the LPDDR5X interface.

But just as chiplet-based designs made trade-offs in per-thread performance, Vera will likely make trade-offs for its unique architecture. The majority of data center workloads are still “legacy” tasks that hyperscalers have built for, and even with seemingly insatiable demand for AI infrastructure, that is unlikely to change for several years.

Buck recognizes this trade-off, asking: “Can Intel and others build rich fabrics? Do they have the IP and the ecosystem to do it and connect it all the way through to LP memory? They need to tell you when they’re going to do it… but that trade-off will come at the cost of the legacy workload.” Earlier this year, at GTC in March, Buck was even more clear. “The world is not going to be served by one SKU of CPU, and that is not our intention,” the executive said in a news conference at the time.

Still, it’s clear Nvidia has ambitions with data center CPUs beyond what headlines are floating around on the New York Stock Exchange. Nvidia says CPUs represent a $200 billion TAM (Total Addressable Market) opportunity for the company, a rather rosy forecast compared to the rest of the industry, which sees a TAM of around $120 billion by 2030 (though recent estimates have climbed as high as $170 billion). And agentic AI is expanding that market, with Morgan Stanley in April estimating that agents could add as much as $60 billion to the data center CPU market.

Vera is in full production alongside Nvidia’s next-gen AI infrastructure, including Rubin GPUs, ConnectX-9 NICs, SpectrumX Ethernet switches, and the various components that go into building a Vera Rubin NVL72 rack. The company says there are around 1.3 million components that go into a rack, and it has a list of over 300 partners globally to build them. As part of our visit to Nvidia HQ last week, we saw a Vera Rubin NVL72 rack in action, running workloads for OpenAI.

Nvidia's upcoming Vera Rubin platform, set to arrive later this year, will take the stage as the AI world shifts towards an era dominated not by frontier training runs but by the demands of agentic AI inference at massive scale. The hunger for generated tokens in agentic workflows and the demands of delivering them quickly, efficiently, and at low unit cost now dominate the discussion.

We’ve already gone in depth on new performance data around the Vera CPU and how it helps to accelerate agentic AI workloads, but that’s not all Nvidia is sharing today. It’s also detailing some new features of the Rubin architecture and how those features are meant to increase inference efficiency from the GPU level to rack-scale and data-center-scale implementations of this accelerator platform.

The full Vera Rubin NVL72 rack-scale system is built up from 36 Vera CPUs and 72 Rubin GPUs, but our focus today is on the GPU proper. Rubin joins two compute dies onto a single package using the Nvidia High Bandwidth Interface. The resulting chip offers 224 Streaming Multiprocessors (SMs) containing a total of 896 Tensor Cores alongside 288GB of HBM4 memory providing 22 TB/s of memory bandwidth.

As an inference-focused accelerator, Nvidia touts Rubin’s 50 sparse PFLOPS of NVFP4 inference throughput as its headline performance figure, although that’s only one of a dizzying array of data types this chip can handle. Here are some key rates to keep in mind for this chip so far:

Let’s dive into some of Rubin’s refinements for inference workloads to understand how Nvidia aims to keep all of those resources fully utilized.

The Rubin Tensor Memory Accelerator efficiently manages growing MoE models

First up, Nvidia highlights efficiency improvements in the Tensor Memory Accelerator (TMA) that help feed the Tensor Cores with data. The TMA is a dedicated engine built to handle memory address calculations and perform direct loads of array data into a GPU's shared local memory.

Leading AI model architectures have moved from dense models where every parameter is activated per output token to a mixture-of-experts (MoE) architecture where only certain specialized sub-networks are activated per token, based on the guidance of a router that helps judge which experts are best suited to processing a given input.

MoE expert weights can be distributed across GPUs in order to efficiently utilize limited per-GPU HBM capacity. Nvidia says that Rubin's TMA has been improved to deal with the challenges of managing the growing numbers of experts in today’s leading models.

The TMA in Blackwell GPUs needed to maintain separate MoE descriptors in memory for the location of every expert, meaning that the overhead of locating and moving those expert weights requires more compute resources as the number of experts grows.

The Rubin TMA now supports GPU kernels that maintain and update a single unified MoE descriptor directly in the TMA instruction at runtime, reducing computation of MoE descriptor metadata and requiring less calculation overhead for data movement. This approach frees up GPU cycles for inference calculations, which is, of course, the place that you want your expensive AI accelerator spending the vast majority of its time.

Doubled K-dimension throughput, double the Tensor Core output

Rubin also improves the fundamental performance of matrix operations in the Tensor Core by doubling the amount of work those cores can perform on the K dimension, or the shared inner dimension of a pair of matrices to be multiplied. Without going too deep into the math, the size of the K dimension is directly related to the number of times the Tensor Core has to loop over the elements of the two matrices being multiplied.

In Nvidia's example, then, the calculation of a result matrix that would require four loop iterations on Blackwell can be completed in only two on Rubin. Nvidia says this improvement has wide-ranging benefits for throughput-, memory-, and latency-bound kernels, and it’s helpful for both context processing and decode phases of inference.

Softmax on Rubin gets up to a 4X boost versus Blackwell

Rubin also focuses on improving the performance of the attention mechanism that’s foundational to transformer-based LLMs More advanced models now support context lengths of up to a million tokens, and quickly performing attention calculations on such long input sequences quickly is a key driver for improved inference performance.

Softmax is an essential operation in attention calculations, and in order to keep up with the improved Tensor Core throughput in Rubin, Nvidia has once again boosted softmax throughput in the GPU SM’s Special Function Unit (SFU).

Since it relies on the transcendental math capabilities of the SFU, softmax throughput can become a bottleneck for subsequent inference work, and it's a limitation that Nvidia already sought to address with enhancements to the Blackwell Ultra SFU. Blackwell Ultra doubled FP32 and BF16/FP16 exponential throughput compared to the first-gen Blackwell GB200.

Rubin maintains Blackwell Ultra's 2X speedup over Blackwell in FP32 exponential math, and it doubles BF16/FP16 exponential calculations again compared to Blackwell Ultra, leading to a 4X improvement in throughput compared to Blackwell for those lower-precision data types.

Finer-grained dependency management, better Tensor Core occupancy

Rubin also increases Tensor Core occupancy by providing finer-grained opportunities for coordination between dependent kernels than on Blackwell. One case that Nvidia cites where these dependencies arise is the generation of activations for an LLM, where one kernel produces and stores data that is then used by a subsequent kernel as a prompt proceeds through a neural network.

On Blackwell GPUs, a long-running producer kernel on one thread block (perhaps within a CUDA structure like a cluster) might delay the execution of a subsequent consumer kernel on those thread blocks, even as other thread blocks of the producer kernel have finished their work.

Rubin offers finer-grained dependency resolution between kernels, such that a consumer kernel can begin executing on individual thread blocks as soon as the producer kernel’s output from each thread block becomes available, instead of waiting for the entire batch of producer kernel data to become available. This finer-grained management results in better GPU utilization, lower kernel-to-kernel latency, and ultimately increases tokens per second per user.

More efficient inter-GPU communication, lower NVLink overhead

All of the improvements we've discussed so far relate to how work happens on one GPU, but the Vera Rubin NVL72 rack-scale accelerator comprises many GPUs connected over an NVLink fabric within the rack. Model weights, key-value cache data, and inter-GPU synchronization messages all move over this fabric, so keeping overhead and latency low is key to realizing maximum performance.

GPUs running CUDA kernels can directly initiate communication with other GPUs in the rack using Nvidia Collective Communications Library (NCCL) API, lowering overhead. Nvidia notes that because the GPU performs those operations directly as part of the compute kernel, the efficient execution of those communications becomes critical to performance.

On a Blackwell system, an NVLink transfer between GPUs might require data store operations followed by a memory barrier and an atomic flag. The Rubin architecture introduces a feature called counted writes that reduces the amount of coordination and synchronization traffic necessary to share data between GPUs across the fabric.

On Rubin, the memory barrier and atomic operations are replaced by a single write counter update on the receiving GPU, reducing network traffic and latency and improving compute utilization by reducing the time spent waiting for coordination overhead.

All told, in tandem with the high single-threaded performance of the Vera CPU for agent harnesses, tool calling, code compilation, and more, the improvements in the Rubin GPU for performance on critical inference operations, as well as improved efficiency for data movement on-chip and across the rack, promise to help create a rack-scale and data-center-scale system that will both increase inference performance and lower per-token inference costs in the increasingly agentic future that Nvidia envisions. We’re excited to see more of what this GPU can do as deliveries of Vera Rubin systems are set to begin this fall.

Our local AI testing in 2026 has focused on large language models that can fit entirely into the 128GB of unified memory on Nvidia GB10 and AMD Strix Halo systems. Useful as those smaller open models can be, there is sometimes no replacement for displacement. Today, we’re exploring what’s possible from a local AI cluster with a pair of Nvidia GB10 systems, namely Dell’s Pro Max with GB10 (henceforth Pro Max), which gives us 256GB of RAM for a local AI sandbox.

Quantizing an AI model from higher-precision to lower-precision data types involves tradeoffs for performance and accuracy. And even in quantized form, some advanced open models are still too large to fit within 128GB. But those models can be distributed across multiple local systems using the network as a scale-out backbone, just as they are in the data center.

Why scale out GB10 systems (or Strix Halos, or Macs)? Local token factories with large VRAM pools built up from discrete GPUs can get crazy, fast. Scaling one to even 128GB of VRAM requires a costly host system with enough PCI Express slots and bandwidth to feed those cards, and going beyond 128GB means spending $20K or more in Nvidia GPUs at a minimum, even if you're building up from older 48GB Ada cards.

The preferred recipe for this kind of setup typically includes a Threadripper Pro or Epyc platform, which means a costly CPU, motherboard, and DDR5 kit even before you start adding graphics cards. The power requirements for such a system can quickly get beyond the capabilities of a standard USA 15A circuit (1,800W maximum).

And having four discrete GPUs running their blower fans at high speeds under load, along with whatever other active cooling you might need for what is essentially a GPU server, is not going to make for the most pleasant company if you’re sharing a space with it.

While a GPU server with four RTX Pro 5000 or RTX Pro 6000 cards is useful for getting the absolute best performance for a given application, those potentially high costs, platform challenges, and quality of life concerns have led local AI enthusiasts to explore other ways of achieving large local memory pools with acceptable LLM inference performance, like the GB10 cluster we’re building today.

Nvidia made the DGX Spark and its Spark-alikes scalable, cluster-able systems right out of the box thanks to their built-in ConnectX 7 200Gbps NICs. These high-end interfaces support Remote Direct Memory Access over Converged Ethernet, or RoCE, so two (or more) GB10 boxes can use them as the backbone for a distributed AI computing cluster.

We didn't have multiple Sparks to test RDMA clustering during our initial review, but Dell sent us a pair of Pro Max GB10 systems along with the QSFP cables necessary to join them together.

These systems still aren’t anywhere near cheap, but at $6332 each as of the time of this writing for the tested configuration with 4TB SSDs, you can build a complete, turn-key cluster with 256GB of RAM for less than the cost of the four 48GB or 72GB GPUs you’d need to scale a similar GPU server build.

If you need to save cash and can trade off absolute performance in the bargain, there’s no cheaper way to get into big local models right now, period, but especially not with the level of networking performance that the DGX Spark platform offers.

Dell’s Pro Max with GB10 closely follows the Spark template, but it adds an extremely handy power LED to the front panel that the DGX Spark lacks, and the hexagonal grilles on the front and rear panels doesn't catch on clothes or microfiber cloths like the metal foam front and rear panels of the DGX Spark do.

Dell also provides a large 280W USB-C power adapter with each Pro Max GB10 system, or 40W more capacious than the adapter included with the reference DGX Spark. However, there’s nothing to suggest this system has a higher TDP or clocks than the reference Spark design as a result.

Dell does drop the Pro Max with GB10 back to a PCIe Gen 4 SSD compared to the launch DGX Spark’s Gen 5 drive, but it appears that the ongoing NANDpocalypse has forced Nvidia to source Gen 4 drives for its reference systems to keep costs down, so we’re not holding this decision against Dell here.

Setting up

We’ve already covered the DGX Spark reference design in its own review, so if you’re unfamiliar with the basics of this platform, we’d suggest reading that coverage first. We’ll keep the focus today on the specific challenges and hurdles of clustering two of these systems together.

While the ConnectX 7 NIC on these systems supports both Infiniband and Ethernet protocols in its add-in card form, Nvidia has stated on its official DGX Spark forums that GB10 systems exclusively support Ethernet, and therefore, RoCE for clustering. Don’t buy multiples of these systems hoping to connect them through any Infiniband switches you might have lying around.

The ConnectX 7 NIC on the Spark and Spark-alikes like the Dell Pro Max is also connected to the GB10 SoC in a somewhat weird way due to some possible platform limitations. In short, the largest PCIe bus width one can apparently get off GB10 is a PCIe 5.0 x4 link, so to achieve 200Gbps on any one QSFP port, the two x4 links to the ConnectX 7 have to be teamed behind any one physical port. As a result, each physical port on the NIC is presented to the system as two logical interfaces.

To achieve the full 200Gbps bandwidth available from the ConnectX 7, you have to configure your networking topology carefully. Nvidia has a Spark playbook on how to do this, and the spark-vllm-docker project also offers its own guide on how to set up these interfaces. I’d recommend following them closely unless you have good reason to roll your own configuration.

After connecting my Dell Pro Max boxes together using the same QSFP cages on their back panels, configuring their network interfaces according to the spark-vllm-docker guide above, configuring passwordless SSH on my second node, and running the recommended NCCL bandwidth test on the link, I found that I was only getting a small fraction of the expected RDMA bandwidth, despite both boxes reporting that they were fully up to date through the DGX Dashboard app.

Community wisdom suggested that a firmware version mismatch was to blame, so I verified that the head Pro Max node in my cluster was fully up to date, both through the DGX Dashboard app and through the command line using the apt package manager.

But even though the DGX Dashboard reported that my second Pro Max system was fully up to date, running the recommended command-line apt checks revealed that there was an update for the fwupd package stuck behind a phasing fence, so I force-installed it.

Once this forced update was complete, it unlocked a new round of firmware updates for the second Pro Max, which I dutifully applied. After rebooting both systems and re-running the recommended NCCL bandwidth tests, I was finally getting something approaching the full 25GB/s one would expect from a proper 200Gbps link.

While none of the issues I had getting my Pro Max GB10 systems clustered were show-stopping, it's also far from a plug-and-play experience. But once both systems were settled in, I didn't see the bandwidth over the ConnectX 7 ports drop back to the degraded performance levels I first observed, even across multiple reboots of the cluster.

Cluster management and performance

If you're thinking about clustering Sparks, you want an inference engine that can handle tensor parallelism, or the distribution of model weights across GPUs during computation. vLLM is an easy choice for doing this on the DGX Spark platform thanks to actively maintained community tools like spark-vllm-docker and sparkrun, but you can also achieve these results with SGLang if that’s your platform of choice.

The spark-vllm-docker project comes with several handy scripts that make starting the cluster and distributing models across it easy, and the sparkrun project provides similar functionality. We focused on spark-vllm-docker for this round of tests, but you have options in this space if you want to explore them.

With our inference engine settled, we went off in search of an advanced model that would utilize a decent chunk of the 256GB of VRAM available from our cluster.

DeepSeek v4 Flash is one such model. It’s a 284-billion-parameter mixture of experts model with 18 billion active parameters per token, and it claims to support a context window of up to 1 million tokens. (vLLM gave us a 400K-token cap on this setup). spark-vllm-docker offers a prebaked vLLM recipe for it, so we downloaded it, deployed it across our cluster, and got to benching.

The DeepSeek v4 Flash vLLM recipe we used takes advantage of this model’s built-in multi-token prediction capabilities, so decoding throughput remains essentially the same even as time to first token climbs with context lengths up to 200K+ tokens, or about 333 pages of A4 text. That’s impressive and usable performance for a model of this size and capability.

We also loaded up CyanKiwi’s four-bit quantization of MiniMax M2.7. This is another large mixture-of-experts model with 230 billion total parameters and 10 billion active parameters, and it supports a context window out to 200K tokens, which is exactly what vLLM gave us after initialization on our Spark cluster.

This model doesn’t have the built-in MTP advantage of DeepSeek v4, so even with the four-bit quantization we used for this test, its time-to-first-token and tokens-per-second throughput follow a more familiar curve. Throughput starts in a relatively usable range, but falls off quickly as we approach the limits of the context window.

Our experience running DeepSeek v4 Flash and MiniMax 2.7 shows that even though a Spark cluster isn’t fast, it can still produce enough tokens per second to be a useful sandbox with these demanding models.

Power and thermal notes

As we discussed in our intro, a major advantage of a cluster like this is that it doesn’t require exotic power and cooling to run, and you also don’t have to banish it to a garage or server closet to keep it quiet.

Imeasured peak wall power draw of about 375W to 415W across my cluster during inference performance testing, which is just a bit higher than the TGP of a single RTX 5080 without its host system.

That figure bodes well for adding even more Spark-alikes to a local cluster if you need to, as even four of them running all-out are likely to need less than 1kW from a circuit (before any outboard networking gear is factored in, at least).

Noise levels from my dual Dell Pro Max setup under load were also well controlled, measuring about 40 dBA at 18 inches away. If you need to keep these systems in an inhabited office space or cubicle, they’ll be perfectly tolerable to be around.

If you’re expanding beyond two GB10 systems, I’d guess that any 200G/400G networking gear that you’d need to throw into the mix will likely be far louder than even four of these systems under load, as it’s likely built for a server closet, not a continuously inhabited space.

Bottom line

If you're a local LLM trailblazer and need more VRAM for large, capable models, and don't want to fiddle with or don’t have the cash for a from-scratch GPU server build with more than 128GB of memory, the Dell Pro Max with GB10 cluster we’ve built here exemplifies how clustering Nvidia GB10 systems is a straightforward, space-efficient, low-power, low-noise, and relatively cost-effective way to scale up your local AI sandbox beyond 128GB of VRAM.

"Relatively" is doing a lot of work here because a pair of Dell Pro Max with GB10 boxes as tested here rings in at $12,664 right now, plus another $50 for the QSFP cable you'll need to hook them together. For organizations or institutions with departmental budgets to spend and existing Dell accounts and support contracts to work within, that dollar figure is likely secondary to the ROI on whatever proposal might drive a purchase order.

But for individuals who just want to build a bigger AI sandbox in their home lab and don't have those relationships to worry about, it's still possible to construct a similar cluster with Asus's Ascent GX10 from stock for under $10K, even at current prices. And as we explained in the intro, going above 128GB of local memory by using multiple discrete GPUs will cost you far more than such a cluster, even before you factor in the cost of a host system.

Not everybody needs to connect multiple Sparks, of course, but if that possibility does intrigue you, the ConnectX 7 NIC in every GB10 box means that there isn't a cheaper way to achieve a 256GB (or larger) distributed memory pool with this class of networking performance behind it.

Some AMD Strix Halo mini-PCs offer PCIe slots for expansion, but they're limited to PCIe 4.0 x4 speeds, so even the funky teamed PCIe 5.0 x4 links to the ConnectX 7 NIC inside GB10 boxes means you're getting far higher potential RDMA bandwidth than you would from adding an aftermarket NIC to a Strix Halo system.

Even though Apple's Mac Studio briefly enjoyed a turn in the spotlight as a cluster-friendly alternative for local AI thanks to the massive memory pools and high bandwidth available from Apple Silicon, along with RDMA over Thunderbolt 5, that star has dimmed, as the company no longer offers memory options larger than 64GB with M4 Max Studios or 96GB with M3 Ultra models. And the lead times on either of those systems are currently over three months out, which is an eternity in the rapidly evolving local AI market.

So Nvidia sort of has this field to itself right now, as GB10 boxes remain readily available from stock with 128GB of RAM at prices that aren't completely bonkers. And if you're a novice to distributed computing concepts, the active community, actively developed tools, and ecosystem software support around GB10 systems are all invaluable for getting your AI cluster running quickly. All that makes Dell’s Pro Max with GB10 (and other Spark-alikes) hard to beat for building a big local AI sandbox to experiment with.

TSMC intends to raise base quotes on advanced chip production services by up to 10%, according to Nikkei, which cites people with knowledge of the matter. The price hike reflects increased demand for sophisticated processors by the AI sector, raising costs of tools and materials, as well as amplified investments in new production capacities.

For advanced process technologies — which TSMC considers 7nm-class and below — TSMC plans to raise baseline prices by 5% to 10%, depending on the particular production node and customer, the report claims. Furthermore, customers that need additional HPC chip capacity beyond their original volume requirements will reportedly have to pay another 10% to 15% premium on top of the standard increase, which means that some services will get a price hike of around 25%, if the report is accurate.

TSMC also intends to increase prices for mature manufacturing technologies, including its 12nm, 16nm, and 28nm-class nodes as well as other legacy fabrication technologies, the report claims. Increases could reach 10%, although certain nodes will reportedly see smaller adjustments, according to Nikkei.

Advanced technologies generated around 77% of the foundry's revenue in Q2 2026, whereas mature nodes accounted for 23%, which essentially means that TSMC is hiking prices on all of its services.

The company reportedly began discussing the new pricing with customers around June and completed negotiations in July. Rather than introducing higher rates immediately, TSMC opted to implement them from the beginning of 2027 to give clients like Apple, AMD, Nvidia, and MediaTek additional time to accommodate the changes and adjust their prices accordingly.

Since TSMC produces the lion's share of advanced processors for AI, HPC, networking, and smartphone applications, its price hikes will inevitably create a ripple effect in the industry and will make almost all electronics more expensive.

TSMC is not alone in raising prices these days. Vanguard International Semiconductor has also raised prices, while UMC began implementing increases in July. Also, memory makers have increased prices significantly, making TSMC management jealous. Intel also recently increased prices of its client and data center CPUs, citing market demand.

"I am really jealous about memory companies' 86% gross margin," said C.C. Wei, chief executive of TSMC, during the company's earnings call with financial analysts and investors. "86% [margin at memory makers] – 68% [margin at TSMC], I will be happy about that."

TSMC rarely comments on its prices to a large degree because they vary based on volumes and relationship with a particular client. Nonetheless, the head of the company stressed that the company has no intentions to increase prices suddenly or dramatically.

"So we do not suddenly increase our price by which I like to have 4x or 5x," Wei said. "You cannot survive for that kind of... for your customer to survive for that kind of price increase. So we earn our value, and we make sure that our profit, our gross margin, is enough for our long-term sustaining expansion, that is to the benefit of my customers and TSMC also, that is our philosophy."

A PC gamer has DIYed a passive Nvidia GeForce RTX 4060 graphics card system. Perhaps unhappy with the Palit GeForce RTX 3050 KalmX 6GB still being the pinnacle of commercial passive graphics cards in 2026, Bilibili user NexFrame (h/t Fanless Tech) has bolted a hulking finned aluminum heatsink onto an RTX 4060.

NexFrame appears to be a small or up-and-coming ‘brand’ with a penchant for passive PC systems, from what we can understand from their Bilibili bio (machine translation). Unfortunately, we don’t have a lot of information about this passive 5.5-pound (2.5kg) aluminum heatsink-wearing RTX 4060, like whether it is tuned to run cooler than a standard model for fanless operation.

Squinting at the Bilibili video source, a system monitor window seen towards the end of the video appears to show that the GPU is running in a gaming scenario at 58C, with a 71C hotspot. Meanwhile, the graphics fan is running at 0 RPM, which seems accurate. However, the modder doesn’t seem to be taking it easy on the GPU. Further down the system info screen, we see the GPU reportedly pulling 198W. A standard actively cooled RTX 4060 would consume far less than that under load, more like 115W to 120W, so some data must be mangled here, or being misreported.

Elsewhere in the images shared by NexFrame, we can see that the CPU is also being cooled passively. It looks like a hexacore Intel CPU has been combined with a Noctua NH-P1 Passive CPU cooler. This commercial passive cooler was capable of keeping a CPU consuming up to 75W cool and stable enough over extended periods during our review testing. The system monitor tool overlaid on NexFrame’s game testing session shows the CPU sipping between 35 and 50W.

Passively cooled PCs are of interest to a significant number of enthusiasts for eliminating what is typically the noisiest component of a modern PC – fans. In a standard gaming PC, there will be fans built into the CPU cooler, on the GPU shroud, in the PSU, and also arranged at strategic places around the case. Even so-called liquid cooling systems usually rely on their CPU or GPU contact heatsinks being attached to an array of fans cooling a radiator in the case. Just think how blissfully peaceful your computing experience could be if all these moving parts were eliminated.

We live in an age where artificial intelligence increasingly dominates the internet, making it harder to distinguish real content from fake news. Any picture or video you see today could be AI-generated and the traditional markers that would give it away are fading rapidly. There are tools that help identify if something has been made with AI, and Nvidia — arguably the main beneficiary of the AI race — has just released its own, called the "Synthetic Video Detector" (SVD).

SVD is an Nvidia Inference Microservice (NIM) part of the company's AI for Media Private Access Program, so it's not publicly available to consumers, but a demo exists. Anyhow, SVD's job is simple: detect whether a video is real or if was generated using AI. It can analyze videos at scale, breaking them down frame-by-frame to spot anomalies. It's based on cutting-edge research that won awards at computer vision conference ICCV.

Instead of looking at the video file as a whole, SVD splits it into cropped frames, each carrying a 504x504 resolution. These frames are then passed through two powerful Vision Transformers made by Meta: DINOv2 and DINOv3. A job of a vision transformer is to learn to form patterns without needing human-labeled data. They're commonly used for image classification, image retrieval, object detection, and depth estimation.

As such, once the frames go through these transformers, their distinct spatial features are quickly assessed, and each one is assigned a score between 0 and 1 — 0 representing a fully real image and 1 representing a completely fake image. The scores are tallied at the end to form an average, which tells the user whether the video is real or not based on a percentage score out of 100.

This way, news agencies, broadcasters, and media outlets can authenticate footage they receive much quicker and with better certainty. SVD is even designed to work with the reality of social media compression since videos uploaded online will have their imperfections masked. But the transformers can still detect patterns that the human eye cannot, seeing past surface-level anomalies to instead focus on intrinsic artifacts.

As visible in AI GVD bench above, uncompressed video still delivers the best results with SVD showing an insane 92% accuracy rate. At 15% compression, the model drops down to 87% accuracy, while a 50% compression rate still achieves a very impressive 82% accuracy in detecting AI-generated content. Since this is a microservice, it has exceptional latency as well, processing 1080p video in just 22ms on Nvidia RTX GPUs and 30ms on Nvidia's workstation models.

That being said, SVD requires the NVENC encoder, so datacenter cards like the B100 cannot run it natively. Nvidia said it's already working with Wowza to bring real-time synthetic video detection into livestreaming workflows. A demo version is available to try right now at build.nvidia.com but beware that it takes a long time to process since it happens in the cloud, the max file size limit is only 100MB, and it often just times out.

TrashBench recently decided to test whether adding more and more fans to what used to be one of the best graphics cards would improve its performance. The result wasn’t significantly faster performance, sadly, even when the thermal headroom was used for overclocking (vs. stock OC). Nevertheless, lessons were learned, and the self-described “punk-rock GPU death lab” was still proud of the temperature reductions, plus the GPU contraption's “awesome” looks and sounds.

The experiment began with an overview of the Asus ROG GeForce RTX 3080, a nice example of the breed. But it was a choice that would perhaps end up making the 11-fan wonder look like less of an accomplishment. TrashBench stated the goal was to add more and more fans, plus duct tape and cable ties, then see whether the reduced temperatures from the boosted airflow result in more frames.

After cleaning and repasting the guinea pig GPU, a baseline was set with the stock cooler. Running 100% fan speed on the Asus ROG resulted in a stable temperature of 63°C (down from 70°C) during stress testing with the Unigine Heaven benchmark.

Replacing the Asus ROG cooling shroud with a trio of Arctic case fans dropped the reported GPU temperatures to a stable 52 degrees Celsius. That’s a decent result. Next, the trio of case fans was swapped for thicker server fans, shaving another 2 degrees Celsius off the GPU temperature, bringing it to 50 degrees Celsius precisely. Duct tape was added to prevent air venting from the sides of the server fans. Oops, the GPU temperature actually stabilized at a warmer 54 degrees Celsius.

So, that was the end of fans-at-the-front modifications. TrashBench next looked at adding a quintet of tiny Arctic server fans that run at up to 15,000 RPM along the top of the card. This jet-engine-soundalike configuration didn’t shift the needle, though. The RTX 3080 still wouldn’t hold below 50 degrees Celsius when tested for any length of time.

Trying backplate fans was the next idea. After another ineffective endeavor, though, TrashBench decided to upgrade the backplate fan to an AiO 360mm cooler. Wow - a new best was recorded with this setup, with the GPU reporting a top stable temperature of just 41 degrees Celsius in Heaven.

Momentarily happy with this low-temperature achievement, the tech tinkerer decided to check whether benchmark runs in Shadow of the Tomb Raider showed any benefit. There were some performance uplifts charted, but only very modest, as you can see from our results table, which even includes cases where the newfound overclocking headroom was taken advantage of.

TrashBench concluded with some positives. “It nearly cut the temperatures in half. It looks awesome. It sounds awesome,” underlined our hardware hacking hero. However, the tech tinkerer kept it real by adding “And it got me 2 FPS. So, not worth it.” In some ways, then, the good quality of the stock triple-fan Asus ROG RTX 3080 graphics card detracted from the 11 extra-fan hijinks.

The upcoming Super refresh of the Nvidia RTX 50-series GPU is reportedly on hold due to the high cost of 3GB GDDR7 memory chips. A VideoCardz source confirmed that one board partner already has RTX 50 Super GPUs on hand, but Nvidia has allegedly told the company that the products are on hold because of the price of 3GB GDDR7 memory chips.

This means that the AI GPU giant has already set an internal release date but is reportedly pushing it back because of memory pricing. If the cost of GDDR7 chips becomes too high, then the RTX 50 Super GPUs would either have a selling price that’s way above Nvidia’s targeted MSRP or, if it forces its partners to stick with or remain close to its set prices, GPU board manufacturers wouldn’t just make any units at all, as they’re going to lose money with every sale.

The RTX 50 Super GPUs are rumored to have 3GB GDDR7 chips, which offers 50% more capacity than the 2GB found in current-gen RTX 50-series graphics cards. This would allow the upcoming GPUs to have more memory without needing to increase or change their memory bus configurations.

According to the publication, the cards expected to be released soon include the RTX 5080 Super, RTX 5070 Ti Super, RTX 5070 Super, and RTX 5050 9GB. The first two will each receive 24GB of GDDR7 VRAM with a 256-bit bus width, while the RTX 5070 Super will have 18GB of VRAM with a 192-bit bus width. Unfortunately, these chips cost twice or thrice as much as their 2GB variants, which will likely push the retail price of these cards beyond Nvidia’s envisioned MSRP.

Nvidia used to supply VRAM chips alongside GPU dies to its board partners, but it changed this policy in late 2025 as the memory chip crisis unfolded. Because of this, the companies that complete the final assembly of the graphics cards are forced to source their own memory chips in an increasingly competitive market. SK hynix, one of the big three memory chip manufacturers, even says that 2027 is set to be the “worst year” for the memory shortage and said that the crunch will last until 2030.

Even Nvidia, one of the biggest winners in the AI race, has been affected by the RAMpocalypse, with the company not announcing a new GPU at CES 2026. This is the first time this has happened in five years, with Jensen Huang releasing the 30-series, 40-series, and their respective mid-generation refreshes despite supply chain limitations and several other issues that arose during that period. Its latest AI systems are now more expensive than ever, with memory accounting for 25% of the BOM, as costs have soared by nearly 500%.

It’s still unclear what Nvidia and its board partners plan to do about the memory situation, especially as things don't seem to be improving. While it could delay the launch of the RTX 50 Super, it can only do so for so long, especially if it’s true that its dies are already in the hands of its board partners.

‘The Jensen Jacket: Jensen Huang's Tom Ford Leather Jacket’ achieved a hammer price of $960,000 this weekend. High-end auctioneer Sotheby’s listed the leather garment, which was claimed to have been worn at least once by the Nvidia CEO, with a far lower price estimate of $40,000 to $60,000. Happily, the bountiful proceeds of the auction are going to charity.

Bidders could be quite confident that this Tom Ford jacket was genuinely worn by Jensen Huang at one or more landmark product launches, thanks to the work of the PSA [Professional Sports Authenticator]. Specifically, PSA photomatched this jacket and its unique leather wrinkles to the Hon Hai (Foxconn) Tech Day in Taipei on October 18, 2023, where Huang wore it on stage and met with other execs. Various close-up photos in the sales catalog showed telltale signs that this was/is the real deal. Separately, a signature on the garment was authenticated by James Spence Authentication.

The near $1M hammer price achieved is many multiples of the estimate. In our earlier reporting on the sale, we guessed this would happen for a number of reasons. Firstly, we thought Sotheby’s ‘low’ estimate was intentional to draw in the crowds and stoke excitement. Secondly, we mustn’t neglect the importance of the not-so-secret ingredient - Jensanity. And, right now, the AI boom with Nvidia at its center is still growing apace.

You can buy a brand new Tom Ford SS2023 menswear collection jacket, like the one shown, without any infusion of genuine Jensanity, for around $9,000. However, we can’t begrudge the success of this auction as the proceeds are earmarked for charity. The Sotheby’s page says that the auction was organized by Long Journey Ventures to benefit the Edge Institute, a non-profit that convenes people working at the frontiers of tech, science, culture, and society in pop-up villages (Edge Cities) to live together and experiment towards a brighter future. Fellowships, grants, and residencies for the next generation of young builders will be funded by this bumper lump of cash.

Nvidia today announced that it's working with Japan's Noetra Corp. to build a 140-megawatt AI factory packing 27,500 Rubin GPUs and 13,750 Vera CPUs, the compute foundation for FRONTia, the Japanese government's state-funded physical AI program. The facility will be built from Vera Rubin NVL72 racks on Nvidia's DSX reference platform, connected with Spectrum-X Ethernet, and will train open multimodal foundation models for robotics, digital twins, and industrial automation, with pretrained weights shared broadly with domestic developers.

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

The chip counts divide exactly into 382 Vera Rubin NVL72 racks, each housing 72 Rubin GPUs and 36 Vera CPUs. Neither company disclosed the project's cost, but VR200 NVL72 systems are currently quoted at $5 million to $7 million apiece, which puts the rack hardware alone somewhere between $1.9 billion and $2.7 billion. Morgan Stanley estimates Nvidia will charge $55,000 per Rubin GPU in volume, pricing the GPU silicon at roughly $1.5 billion before memory, networking, and cooling.

No deployment timeline was given in the announcement, but Rubin racks are only expected to reach volume production in the second half of this year, and Nvidia said the facility will support trillion-parameter model training "as the AI factory expands," suggesting a phased ramp.

Noetra is a new consortium founded by SoftBank Corp., Sony, NEC, and Honda, with investment from 44 companies and organizations, NEC said in a press release also published today. Noetra and the national research institute AIST won a NEDO public tender on June 30 to run the FRONTia project from fiscal 2026 through fiscal 2030, with ¥387.3 billion (roughly $2.4 billion) in first-year funding and up to ¥1 trillion (roughly $6.1 billion) over five years, Asia Times reported. Funding beyond the first two years is subject to annual stage-gate reviews, so the full amount isn't guaranteed.

Noetra's roadmap targets a reasoning foundation model in fiscal 2026, an omni-modal model that processes text, images, video, and audio by fiscal 2028, and "real-world native AI" capable of spatial awareness by fiscal 2030, per NEC.

The AI factory follows SoftBank's Blackwell-based DGX supercomputer, announced in 2024, and FugakuNEXT, the $740 million RIKEN, Fujitsu, and Nvidia zetta-scale system due around 2030, but it's the first that's state-tendered national infrastructure rather than a corporate or scientific machine. Japan's AI Robotics Strategy, released in March, targets more than 30% of the global AI robotics market by 2040, an opportunity the government estimates at $133 billion.

Jensen Huang, chief executive of Nvidia, denied reports about delays of the company's next-generation AI platform and said that production volumes of the upcoming Vera Rubin platforms are 'giant.' He didn't address reports about delays of Vera Rubin Ultra-based rack-scale systems carrying 144 AI GPUs.

"[The reports about Vera Rubin delays are] not true," Huang told reporters on the sidelines of an event in Japan, reports Bloomberg. "Vera Rubin is already in production. Giant amounts of production incoming."

Nvidia confirmed production of its Vera Rubin platform in January and then sampling in February, so the current comment reiterates what we already know. Nvidia stressing that 'giant amounts of production' are incoming is meant to reassure investors that the company is on track to sell a boatload of its next-generation Vera CPUs, Rubin GPUs, and Vera Rubin NVL72 systems in the coming quarters, which means more record-setting quarters.

What Huang did not address — or perhaps he wasn't asked — is Nvidia's rumored delay of its Kyber NVL144 rack-scale solution with copper interconnects due to the system's complex PCB midplane by more than a year from 2027 to 2028. An alternative dual-rack design has reportedly been canceled and an even larger CPO-based NVL576 configuration may also face delays or limited availability, the same report from SemiAnalysis claimed earlier this month. The setback could leave Nvidia's Rubin Ultra platform with a smaller NVLink scale-up domain than originally envisioned. Nvidia says its roadmap is intact.

The Kyber NVL144 architecture was designed to connect 144 Rubin Ultra GPUs using a copper-based NVLink 7 scale-up fabric, so the machine required a sophisticated PCB midplane to carry high-speed electrical links between the system's components. SemiAnalysis claims that this midplane was challenging to manufacture, leading to a delay. The report does not identify defective chips or problems with particular components mounted on the board, but specifically points to the manufacturability of the PCB infrastructure itself.

"Our roadmap is intact," a spokesperson for Nvidia told Tom's Hardware.

Nvidia's statement on the matter neither confirms nor denies the report, but indicates that the company will be able to offer products mentioned in its roadmap without revealing whether they also remain on their previously announced launch schedules.

Nvidia reportedly considered another copper-based design, called NVL72x2, as an alternative to Kyber. The system would have placed two Oberon racks back-to-back to expand the size of the NVLink scale-up domain without using optical interconnects. However, SemiAnalysis says customers rejected the unusual design and operational requirements, but does not specify their individual objections that could include serviceability, cooling, cabling, and data-center layout.

Meanwhile, the planned NVL576 rack scale solution that was supposed to combine eight Oberon racks interconnected using co-packaged optics between NVSwitches has also been postponed, or shipped in relatively small quantities because of 'ongoing CPO challenges,' SemiAnalysis claims.

The existence of the planned NVL576 configuration suggests that Nvidia had been developing some form of CPO-enabled NVSwitch connectivity for the Rubin generation. In theory, similar optical switch-to-switch connectivity could potentially be used to join smaller GPU groups into an NVL144 system and bypass Kyber's problematic copper midplane. However, the available information does not clearly indicate whether the CPO technology intended for NVL576 could reproduce Kyber's topology, bandwidth, and latency characteristics, or whether it was sufficiently mature for high-volume deployments by potential NVL144 customers.

The reported Kyber delay comes on the heels of another report saying that Nvidia had canceled quad-compute-chiplet version of its Rubin Ultra in favor or a dual-compute-chiplet design that is projected to deliver 2X lower performance. With Kyber NVL144 delayed and NVL72x2 cancelled, Nvidia will only be able to offer 72-way scale-up systems till sometimes in 2028, meaning that AMD and Google may end up with more competitive scale-up systems in 2027 – 2028. AMD's Mega Pod based on the Verano CPUs and Instinct MI500-series accelerators, is expected to pack up to 256 accelerators. Google's TPU 8i can provide roughly 1,024–1,152 accelerators within one low-latency domain, whereas the TPU 8t goes much further and can get to 9,600 chip packages per domain.

Nvidia's RTX Spark platform arrives later this year, and the company is hard at work building the partner ecosystem around the GB10 Superchip to ensure that Windows and applications are ready for its agentic AI PC vision. As part of that groundwork, the two companies announced today that Sega will support the RTX Spark platform with its upcoming Virtua Fighter Crossroads, coming in 2027.

The two companies also committed to RTX Spark support for "future Sega titles,” meaning that we might see official support for other evergreen franchises like the Yakuza series, the Persona games, and the upcoming Alien: Isolation 2 and Total War: Warhammer.

Although the companies didn’t go into detail about exactly what full support for the RTX Spark means for Sega games, one would expect that the developer’s titles will be natively compiled for Windows on Arm instead of relying on the Prism x86 emulator for compatibility.

It also seems safe to expect that future Sega titles will incorporate DLSS technologies like upscaling and Multi Frame Generation in order to deliver the best possible experience on RTX Spark systems. Despite having GPU compute capabilities similar to those of a desktop RTX 5070 on paper, the unified memory architecture and relatively limited memory bandwidth of the GB10 Superchip behind the RTX Spark platform present challenges for gaming performance that are likely to make the incorporation of DLSS tech important for the best experience.

The relationship between Nvidia and Sega spans over 30 years, tracing its roots to the ultimately abandoned development of the GPU for the Dreamcast console. Despite its eventual decision to use an NEC-produced PowerVR GPU for that system, Sega offered Nvidia a $5 million lifeline that gave the company the runway that it needed to develop and deliver the Riva 128, its first DirectX-compatible GPU.

That investment proved to be historic, as Nvidia now has a market cap of over $5 trillion and is in the process of shifting the very foundations of computing through its Grace Blackwell and upcoming Vera Rubin AI platforms for the data center.

Whether the RTX Spark platform reshapes the PC when it arrives in the fall of this year remains to be seen. But if you’re a fan of Sega’s IP, this partnership means that you can at least look forward to a first-class experience with its games on RTX Spark systems.

The Sino-American chip wars have resulted in many back-and-forth salvos and negotiations as the countries try and strike a balance between technology access and trade. Currently, both sides have set respective import and export controls, letting specific companies on a case-by-case basis. Today, Reuters reports that Chinese telecoms giant ZTE and server firm Maginfra have received U.S. approval to buy Nvidia's last-gen H200 "Hopper" chips.

ZTE joins a club that counts Alibaba, Tencent, ByteDance, and JD.com among the roughly 10-strong group of Chinese companies with U.S. clearance for those purchases. Additionally, an apparent subsidiary of Kingsoft Cloud got approval to buy AMD accelerators equivalent to Nvidia's H200, presumably Instinct MI300X-class chips.

Over on the Chinese side of the table, Reuters remarks that there's no word on whether the respective authorities will give ZTE the go-ahead for import, as the country has taken on a protectionist stance as it tries to grow its own chip industry. The country has discouraged firms from purchasing foreign tech and has instead pushed companies to acquire homegrown accelerators. Huawei in particular has made great strides both technologically and financially.

But even with those domestic production initiatives, the Chinese hunger for AI silicon is so deep that six months ago, Reuters said the nation's tech firms had more than two million H200 chips on order, far more than what Nvidia had on hand at the time. We'd venture that hunger has barely subsided.

ZTE might not be a familiar name Stateside, but the corporation is one of China's largest telecommunication conglomerates, and among many other ventures, it sells all sorts of carrier network gear that's installed worldwide, along with corresponding client-facing equipment, including phones and IoT equipment. Like most any sizable technological venture, ZTE has joined in on the cloud computing and AI push, so it needs accelerators to make those ambitions reality.

The current status of the AI chip trade situation is roughly that the U.S. allows Chinese firms to buy AI chips up to and including the Hopper family (meaning no Blackwell chips), with a 25% export tariff, though final decisions are made on a case-by-case basis. Over on Chinese shores, Beijing's authorities play their cards close to their chest and dole out approvals as they see fit, with no clear rules seemingly set. But China is, of course, a global power with trade connections to most everyone, so interested firms were able to get their hands on Blackwell chips through various creative (and potentially illicit) means.

Whether this change will actually clear the way for any great volumes of H200 accelerators to make their way into ZTE's data centers remains to be seen. CNBC cites a U.S. trade official who today stated that "very few shipments against licenses for H200s and equivalents have taken place. It’s a very small quantity of chips" during a congressional hearing. If H200 shipments become material to Nvidia's bottom line, we'll almost certainly hear about it in future comments or earnings reports.

AI tech giant Nvidia, which builds some of the most coveted AI chips in the world, has reportedly created a new “whitelist” of verified companies to help prevent its products from getting smuggled into China. According to the Financial Times, this roster cuts the number of authorized clients by more than half, with those remaining having passed tougher compliance inspections to ensure that they are genuine businesses, not shell companies designed to forward Nvidia GPUs and servers into China. Some of the steps that Nvidia took to help safeguard its chips reportedly included sending staff to customer data centers, contract verification, and interviewing end users.

Sources told the publication that the company made this move after Washington pressured it into tightening its legal compliance, which comes months after the arrest of Supermicro co-founder Yih-Shyan “Wally” Liaw, alongside two other suspects, for allegedly smuggling $2.5 billion worth of Nvidia hardware into China. This clampdown also extended into Singapore, which saw the seizure of a $42-million mansion tied to alleged AI GPU smugglers, and Taiwan, where authorities raided the offices of Supermicro and two supply-chain partners as part of a chip smuggling probe. Nvidia was not immediately available for comment on the news.

Although the U.S. has banned the latest AI GPUs for export into China since 2022, various investigations showed Chinese companies could still easily get their hands on these coveted chips until recently. Washington’s and its allies’ crackdown on AI GPU smuggling have cut supply in China, which is now making it harder for AI companies to procure the processors they need. President Donald Trump took a 180-degree turn in December 2025 and finally allowed Nvidia to export its H200 GPUs to select customers in the region, which would have alleviated the situation. However, Beijing refused to allow Chinese companies to buy these AI processors — instead, it’s banking on domestic semiconductor manufacturers to make up for the shortfall, but it’s apparently still not enough. One tech executive even told the Financial Times that all domestic suppliers are sold out and that they’re even considering less powerful chips, as long as they could be put to use.

As Nvidia reportedly cleaned up its verified list of clients and made it harder for non-vetted companies to acquire its chips, the company has also told its partners to fix their export control compliance. “We insist our partners are compliant,” Nvidia CEO Jensen Huang told the media last May after Taiwan started its operations against AI chip smuggling into China. “We hope that they will enhance and improve their regulation compliance and prevent that from happening in the future.”

When the RTX 50 series launched, reviewers quickly discovered that the hotspot temperature was being misreported in standard diagnostics tools such as HWiNFO or MSI Afterburner. Eventually, people realized that Nvidia had outright removed the option to monitor hotspot temps, but it seems like the hardware was never removed from the GPU. New testing by Brazilian repair specialist Paulo Gomes has revealed that the sensor is still present and readable with special tools.

In the video, the host shows a Gigabyte variant of the RTX 5070 Ti that was sent to him due to overheating issues. Within Windows, the monitoring tools showed no abnormal signs, as the "average" temperature was reported at 67 to 68 degrees Celsius. However, when diagnosed with a specialized tool called "MODS," the hotspot temperature reached 107 degrees Celsius almost immediately under load.

MODS stands for Modular Diagnostics Software, and it's an internal Nvidia tool used to test GPUs before they hit the shelves or during the RMA process. It's not available to the public and doesn't work on Windows because the OS keeps intercepting calls from the hardware monitoring APIs. You need a Linux distribution that boots directly into a command line, from where MODS (and MATS, for memory testing) can run as intended.

Some repair shops have been known to get access to MODS, such as in this case, which unlocks the hidden hotspot temperature sensor on Blackwell gaming GPUs. Keep in mind that Nvidia ships much more comprehensive diagnostic utilities for its server-grade and workstation GPUs that can actively monitor all aspects of the card. It's unknown why the company decided to keep some sensors locked out of gamers' reach.

Perhaps we can infer the rationale from last year, when Igor's Lab tested several RTX 50-series GPUs and found a "hotspot issue" affecting all of them. The reason was poor PCB manufacturing — not using heavy-duty materials to build the PCB layers, causing certain parts of the substrate to heat up even when the core was relatively cool. This was exacerbated by Nvidia's own guidelines, which told AIBs to compensate for ideal conditions instead of worst-case scenarios.

Anyhow, as Paulo Gomes and his team discovered, the RTX 5070 Ti's hotspot was hitting 107 degrees Celsius, and the card throttled and dropped its clock speeds right away. Nvidia mandates 107 degrees Celsius as the upper limit for RTX 50-series, so it was clear that the card was slowing down to prevent damage. To inspect what was actually wrong, they opened up the card and found poor thermal contact between the cooler and the componentry.

The TIM (thermal interface material) application was inadequate; the paste had accumulated around the perimeter of the core while the center was mostly dry. The repair personnel removed the old material and replaced it with SnowDog Husky paste, which was enough to drop the hotspot temperatures to 100 degrees Celsius. Now, it was within the safe operating range and no longer thermal throttling under load.

What would've been a simple fix on the consumer's end was turned into a repair job solely because Nvidia hid the GPU's hotspot temperature, literally misreporting the card's internal condition. Had this RTX 5070 Ti just run at 107 degrees Celsius continuously, the silicon would wear down incredibly fast, and the customer would never even know why. Not to mention some manufacturers' insistence on voiding warranty upon breaking the GPU's "seal," which is an illegal and unenforceable practice in the United States.

Nvidia and Sega have scheduled an event next week to celebrate their history and longstanding friendship. The invitation-only shindig takes place at GiGO Akihabara, Tokyo (you can apply via Twitter/X). Nvidia CEO Jensen Huang will star at the July 15 event, officially unveiling the RTX Spark for the first time in Japan. Attendees will also get a chance to win a GeForce RTX 5090 FE in a raffle.

If you will be in Tokyo on July 15, it might be worth pitching for an invite, and if you’re exceptionally lucky, you might visit the event, then go home with one of the best graphics cards available in 2026. The GeForce Japan social media managers are asking for comments that encapsulate 'memories of Nvidia or Sega' with photos, videos, anecdotes,” and so on (machine translation). You have until sometime on July 12 to concoct your invitation pitch. Remember, Japan time is about half a day ahead of the mainland US time zones.

Though it is clearly spelled out what the Nvidia side of the celebration will present on the day (the RTX Spark and a raffle RTX 5090 FE), no such specific teases have come from the Sega camp.

The lack of any teaser might make you think that Sega isn’t going to reveal anything new at the event, or conversely that something big is on the horizon. But if Sega were to pull a hardware surprise out of the bag, it could make quite a splash.

The Japanese gaming icon exited the console race back in 2001, when it ceased production of the Dreamcast. However, it released rehashed mini consoles like the Genesis Mini and Game Gear Micro at the beginning of the 2020s. More recently, some Mini Arcades (like Sonic, OutRun, and Golden Axe) have been released in partnership with MyArcade. It would be great if Sega could do something ambitious again, or even come out with a mini console capable of handling Saturn or Dreamcast titles. With some kind of lightgun support, please.

Nvidia and Sega – an enduring bond

As mentioned in the intro, Nvidia and Sega are old buddies. Nvidia nearly collapsed in 1996, just three years after its founding, and only the belief and generosity of a senior Sega executive at the time saved Nvidia from the graveyard. This is according to Huang’s recollections of a very difficult time for Nvidia, shared in interviews in 2024.

The story goes that Nvidia’s work to design a GPU for a next-gen Sega console fell through largely due to incompatibility with the emerging DirectX API. Sega’s management appreciated the efforts, though, and believed in young Mr. Huang enough to invest $5M in the green team. This investment gave Nvidia the breathing room to pivot from its previous graphics architecture and come out with the DirectX-friendly RIVA line (1997), followed by the unstoppable GeForce series (1999).

Only a little while back, Phoronix got the chance to test-drive one of Nvidia's upcoming Arm-based Vera CPUs. In certain approved workloads, the chip put up an impressive showing, nipping at the heels of its Xeon and Epyc x86 competitors. In specific single-threaded scenarios, Vera "absolutely dusted the competition" (our words). But AMD had some things to say about the Phoronix test, firing back with its own metrics of a 3.3x performance gain over Vera for the projected output of a 100 kW rack of its hardware.

And Nvidia is already thinking about this future. It revealed that its next-gen Rigel Arm v9.2 CPU core, shipping as part of its Rosa CPU, will deliver even higher per-core performance than Vera's Olympus core within the same silicon footprint via "better instruction delivery," more L2 cache, and better memory handling.

Now, Nvidia is reasserting Vera's advantage for AI work by describing it with a new product category: a "max single-threaded CPU at scale" rather than a parallel-processing beast. Instead of simply maximizing the core count per socket, Nvidia says Vera's monolithic 88-core design is meant to provide strong performance per core under load, enough memory bandwidth per core to keep active cores supplied with data, and predictable latency.

Nvidia describes AI inference workloads as being bound by single-thread speed. For example, a reasoning AI will run the model for one step, and will run the model again as many times as it takes until the answer is generated. Since each step needs the output from the previous one, no amount of parallelism will help — the speed at which one thread can run is most important. The situation is similar in agentic workloads, as agent B can't get its work started without knowing what happened with agent A.

Vera's design, then, appears to be one aimed at both having and eating the proverbial cake: high single-thread speed with a large number of available threads. Vera is an 88-core design with SMT support for 176 total threads. And to supply each of those cores with adequate bandwidth, Nvidia says Vera talks to LPDDR5X RAM at 1.2 TB/s, and that its monolithic compute die keeps cores well fed and avoids bottlenecks thanks to 3.4 TB/s of core-to-core bandwidth. The company says the latter figure is 3x that of "any other data center CPU."

There are many ways to measure inter-core bandwidth, so direct comparisons are tricky at best, but given the bespoke design of Vera for AI inference tasks, the claim is at least plausible.

The company's latest blog post about the new silicon reiterates this point, claiming its new silicon delivers 1.8x higher performance versus its x86 competition in "loaded CPU workloads that represent agentic execution," 1.5x higher perf in coding workflows, and 3x faster work in database analytics.

The numbers Nvidia touts purportedly come from real-world scenarios, starting with those from Perplexity, whose usage of Vera in coding agent work delivered a claimed 1.5x performance increase over x86, and a 1.9x speedup running concurrent sandboxes.

The claimed speed increases are wider still in database workloads, with Starburst (federated database firm) clocking a 3x uplift in large-scale SQL analytics, while Redpanda's real-time analytics saw a claimed 6x latency drop. According to Nvidia, all this purported performance is delivered by Vera's particular architecture, one that aims to deliver maximal single-thread performance with high thread counts.

We should note that vendor-approved benchmarks should always be taken with a bucket of salt, particularly those for hardware in a field that can shuffle trillions of dollars in a single day. The company doesn't say which precise x86 chips it tested Vera against, but it's a fair guess that they're mid- to high-end Intel Xeon and AMD Epyc models.

Nevertheless, in the blog post, Nvidia describes a conundrum that's familiar to most any server administrator: big-iron server chips can pack obscene amounts of cores, making them ideal for processing many tasks at once. However, the more cores you add, the slower they need to be to keep thermal performance and power draw in check. But that scale is an obstacle for tasks that need to be done now, parallelization be darned.

And the architectural decisions involved in using chiplets to scale to high core counts aren't free, either. Nvidia calls this "chiplet tax", and it says that scaling using chiplets creates memory access and performance inconsistencies that Vera's monolithic design is specifically meant to avoid.

We've long emphasized the importance of high single-threaded performance for a fast and responsive experience for client PCs, and it seems like AI agents are going to end up placing similar demands on hardware as they do their thing. If that's how the agentic AI future plays out, Nvidia's particular design optimizations for Vera make greater sense than prioritizing core count above all, as it might be for a general-purpose server chip meant to satisfy different economic and customer demands.

We'll have to see if Intel and AMD respond with "max single-threaded CPUs at scale" of their own.

Update 7/6/2026 4:10pm PT: An Nvidia representative responded with a short statement to Tom's Hardware: "Our roadmap is intact." Nvidia provided no further details in response to our follow-up questions. As such, it is unclear if Nvidia's statement refers to its original roadmap, planned delivery schedules, and hardware, or if those particulars had already been changed and the statement refers to a newly updated roadmap.

Original Story:

Nvidia reportedly won't ship its Kyber NVL144 rack until 2028, a delay of more than 12 months that pushes the cabinet meant for 2027's Rubin Ultra GPUs into the following year, according to a SemiAnalysis thread on X. The holdup is ostensibly being caused by manufacturing challenges with a PCB midplane that connects eight Oberon racks between the NVSwitches, which Nvidia calls the orthogonal backplane. Nvidia is also understood to have killed NVL72x2, a stopgap rack designed to tide customers over, and that no proven alternative is now available to widen Rubin Ultra's scale-up in 2027.

The orthogonal backplane sits between Kyber's vertically mounted compute trays and the switch trays behind them, replacing the cable harnesses of earlier racks with a rigid board that carries the all-copper NVLink fabric. Kyber runs liquid cooling by default and stacks 144 Rubin Ultra packages, double the 72 packages in a current Oberon NVL72 rack. Every GPU-to-GPU link inside the cabinet runs through that board, and copper traces lose signal integrity as layer counts increase, alongside power delivery and thermal design challenges. Jensen Huang held up the gray backplane on stage at GTC back in March.

Trade analyses of the board describe three 26-layer sections laminated into one 78-layer stack close to a square meter in area, with trace spacing at or below 25μm and impedance held within a tolerance of 5% to keep 448 Gb/s-class signaling intact. A cabled version of the same interconnect would need upward of 20,000 discrete cables, which is why Nvidia is moving the wiring onto a single passive board.

NVL72x2 would have bolted two Oberon racks back-to-back to reach Kyber-class density over copper NVLink, per SemiAnalysis, which said Nvidia abandoned the stopgap after its largest customers balked at running two linked cabinets as a single unit. NVL576, a separate configuration tying eight racks together through co-packaged optics, is likely to slip too or ship in low volume until that optical technology matures.

These cancellations leave Nvidia with "no proven solution to expand the scale-up world size for Rubin Ultra," meaning the largest single Rubin Ultra domain in 2027 could match, but not exceed, what Oberon already delivers.

Nvidia dropped the quad-chiplet Rubin Ultra GPU for a dual-chiplet part last week over manufacturing execution concerns, halving the accelerator's per-package compute. SemiAnalysis has also placed a fully production-ready co-packaged optics NVSwitch no earlier than the Feynman generation that follows Rubin, which leaves copper as the only near-term solution for linking Rubin Ultra at rack scale and thereby puts even more weight on the PCB midplane.

The delay only applies only to the Rubin Ultra phase and its Kyber rack. Nvidia's 2026 Rubin GPUs, which reuse the current Oberon rack, aren't part of the reported delay. We've reached out to Nvidia for comment.

Nvidia shouted proudly in a recent blog post that its network of American manufacturing partners and suppliers now spans 43 states, that TSMC's Phoenix plant is producing Blackwell wafers at volume, and that it plans to produce up to $500 billion of AI infrastructure in the U.S. over four years with partners including TSMC, Foxconn, Wistron, Corning, Coherent, and Amkor. Intel has made its own case in an America 250 post presenting end-to-end U.S. capabilities across design, manufacturing, and advanced packaging.

Both accounts hold up at the wafer stage but omit the same downstream step: every Blackwell die that leaves TSMC's Arizona fab still crosses the Pacific to be packaged, no HBM is manufactured or packaged on U.S. soil, and the facilities intended to close those gaps won’t start production until 2028 at the earliest.

Lofty projects

Foxconn is building a Houston factory to produce GB300 tray modules for Nvidia, and Wistron will assemble and test Nvidia AI systems at a new facility in Fort Worth, Texas. Coherent broke ground in June on an expanded Sherman, Texas, plant that the company describes as the first volume-production 6-inch indium phosphide fab, supplying the lasers and optical components that link AI systems together.

Corning is adding more than 3,000 jobs across optical manufacturing sites in North Carolina and Texas. The post also cites an estimate from consultancy Public First that Nvidia-driven AI demand will contribute $485 billion to U.S. GDP in 2026 and support over 100,000 jobs. “AI is driving a once-in-a-generation opportunity to reinvigorate American manufacturing and supply chains,” said Nvidia’s Jensen Huang in the post.

Meanwhile, Intel's post lists R&D and manufacturing across Oregon, Arizona, New Mexico, and California, describes Ohio as “a planned site,” and devotes most of its length to workforce programs, K-12 AI education, and the company’s America250 partnership. Neither post addresses where the most advanced AI processors are actually assembled into finished chips.

The Pacific round-trip

Nvidia and TSMC produced the first Blackwell wafer at Fab 21 near Phoenix last October, and the site has since moved to volume output of Blackwell silicon on TSMC's 4NP node, the custom 4nm-class process built for Nvidia. On the other side of the Phoenix metro area, Intel's Fab 52 became fully operational in the same month as the first high-volume home of Intel 18A, and Naga Chandrasekaran, Intel's chief technology and operations officer, told CNBC in December that the fab is capable of more than 10,000 18A wafer starts per week. Panther Lake reached broad availability in January, Clearwater Forest is due in the first half of this year, and 18A yields are expected to reach industry-standard levels in early 2027, which I covered in my examination of Intel's fab roadmap.

This ultimately means that leading-edge logic wafers are now being fabbed in the U.S. by two companies on two competing nodes. That’s a genuine change and, by any measure, a monolithic achievement when compared to the start of the decade, and neither company overstates that in their corporate blogs.

However, a Blackwell data center GPU pairs two reticle-sized compute dies with eight stacks of HBM3e on a silicon interposer using TSMC's CoWoS-L packaging, and all of TSMC's CoWoS capacity is located in Taiwan. TSMC’s U.S. facilities currently send 100% of their chips to Taiwan for packaging, including wafers fabbed in Phoenix. A Blackwell die fabbed in Arizona therefore travels roughly 7,000 miles to be diced, stacked, and mounted, then travels onward through system assembly before any of it returns to a U.S. data center.

As for HBM, every stack in production today comes out of SK hynix and Samsung facilities in South Korea or Micron's fabs in Taiwan and Japan, and the ABF substrates beneath the interposer are similarly concentrated in Japan and Taiwan. No U.S. facility currently manufactures or packages HBM. The one company running advanced packaging at scale on U.S. soil is Intel, whose Foveros operation in New Mexico handles its own 3D-stacked products and has started attracting outside interest; Google has reportedly booked Intel to package more than 3 million TPUs in 2028. Intel doesn’t currently appear anywhere in Nvidia's list of manufacturing partners.

Nothing before 2029

Amkor also broke ground on its Peoria, Arizona campus last October, a $7 billion, two-phase project with up to 750,000 square feet of cleanroom, roughly $400 million in CHIPS Act funding, and Apple and Nvidia signed as lead customers. Its first factory will be completed in mid-2027, with production beginning in early 2028. TSMC formalized the relationship on June 16th, signing a 10-year agreement under which it will procure packaging and test services from Amkor, while TSMC executives said in April that the foundry's own Arizona packaging facility will bring CoWoS and 3D-IC capacity online before 2029.

SK hynix began initial work in April on its $3.87 billion advanced packaging plant in West Lafayette, Indiana, targeting mass production of HBM4E and HBM5 in the second half of 2028, the same window Amkor’s aiming for. The timing means the entire Blackwell family, and likely the first Rubin generation, will complete their product lifecycles without a fully domestic manufacturing path. The first AI accelerators that can be fabbed, packaged, and fitted with U.S.-packaged memory without leaving the country will be HBM4E-era parts arriving around 2028 to 2029.

Unfortunately, the Section 48D advanced manufacturing tax credit, raised to 35% last July, doesn’t apply to projects whose construction begins after December 31st, 2026, which gives Coherent's June groundbreaking, SK hynix's April piling work, and Amkor's October start a shared fiscal deadline if they want to benefit from it.

As for Foxconn and Wistron’s Houston and Fort Worth plants, they’ll receive GPUs packaged in Taiwan and assemble them into trays, racks, and systems on U.S. soil. It’s that type of assembly work that’s carrying most of the $500 billion figure, which counts the value of AI infrastructure produced rather than capital spent on factories. Wafers are American, racks are American, but everything in between isn’t. Whether that changes on schedule is a question for 2028, and it depends highly on two packaging campuses in Arizona and one in Indiana meeting their deadlines.

A Chinese reviewer on Bilibili by the name of 杰某 (Jie Mou) got his hands on a special RTX 4080 graphics card that uses a mobile core instead of a desktop one. These GPUs emerged after the Trump admin banned the sale of RTX 4090s in the region, which forced local sellers to resort to strange alternatives, one of which is the supposed "RTX 4080M." It's a custom, modded GPU that doesn't come with a warranty or official drivers, and the benchmarks show it doesn't offer world-beating value either.

The reviewer paid 2,000 RMB (~$300) for the tested card but remarks that it now costs close to 2,700-2,800 RMB (~$400) due to the ongoing component crisis caused by the AI rush. At that price, it's almost as expensive as AMD's RX 9070 GRE or Nvidia's RTX 5060 Ti in China, both of which will be brand new, warrantied cards. However, a similarly-modded RTX 4090M in China would cost an absurd 10,000 RMB (~$1,470).

Before we go over the benchmarks, the test bench used here was comprised of an Intel Core Ultra 270K Plus mounted on a Maxsun Z890-A motherboard alongside 32GB of DDR5-8200 RAM. In 3DMark TimeSpy, the 4080M scored 18,600 points, which is a respectable result in and of itself, but underwhelming when you take its price into account. Someone in the comments showed the same card netting 19,500 points as well.

During the benchmark, the card only pulled around 100W, which is significantly lower than even the mobile RTX 4080's TGP. The mobile core can be pushed up to 175W as per Nvidia's own spec and you'd expect that in a discrete GPU form factor, but it's likely that the custom BIOS or the drivers are holding it back. Speaking of which, the drivers can be easily configured with one-click installers developed by the community.

Moving toward gaming, the reviewer compared the 4080M against the aforementioned RX 9070 GRE because of their similar price brackets in China, and both come packing 12GB of VRAM. PUBG was the only game where the 4080M was clearly superior, achieving a 100 FPS lead over the AMD option at 1440p resolution with Ultra settings. In Delta Force at 1440p Ultra, both cards performed the same, but the 4080M did manage to net 10 more FPS at 4K.

The reviewer then tested AAA titles where the RX 9070 GRE basically smoked the 4080M as the resolution scaled upward. We'll add a table below so you can see the exact numbers, but the closest the 4080M came to dethroning the Red Team was in Shadow of the Tomb Raider. Running at 1440p with Low settings, the 4080M pushed 286 FPS on average, while the RX 9070 GRE still won with 274 FPS.

At the end, the verdict ultimately turns out to be boring: the RTX 4080M is not a sensible purchase at the current Chinese market prices because similarly-priced new GPUs outpace it with ease. However, since it only drew 100W in games, there's an argument to be made for SFF builds. There's limited thermal headroom in an ITX system and that's where the RTX 4080M could thrive.

Nvidia's rich driver suite and superior upscaling tech also add value to the proposition. At roughly $400 converted, perhaps the 4080M can power a DIY Steam Machine that truly undercuts Valve's pricing while delivering much better performance. After all, it's made from salvaged laptop GPUs and qualification samples that are cheaper to acquire, so a MacGyver-ed, console-busting rig is where it can meet its natural match.

One of Jensen Huang’s used leather jackets is up for auction, posted with an estimate of $40,000 to $60,000. Sotheby’s, better known as a purveyor of blockbuster fine art and historic artifacts, is auctioning this item of worn clothing. The storied auction house asserts this is a genuine article, as worn by the Nvidia CEO at an event in Taipei in 2023. It has also made sure the signature within the garment has been professionally authenticated. Having watched a few tech memorabilia auctions lately, I’d say the official estimate is on the low side.

Sotheby’s auction is titled ‘The Jensen Jacket: Jensen Huang's Tom Ford Leather Jacket.’ Its catalog notes that this signature black leather jacket has been Huang’s standard attire for more than a decade and has become “associated with some of the most consequential moments in modern technology.” This jacket, or one like it, has been on stage helping to define the rise of artificial intelligence. It may also have been worn by the Nvidia CEO visiting a humble street food vendor in a Taiwan night market.

So, it is important to understand that this is one of many jackets Huang has sported during keynotes, announcements, and special events over the years. Like any good auction house, Sotheby's has thus certified the provenance of this particular garment.

“The Jacket has been photomatched by PSA [Professional Sports Authenticator] to Jensen Huang, co-founder and chief executive officer of Nvidia Corp., during the Hon Hai [Foxconn] Tech Day in Taipei on October 18, 2023,” writes Sotheby’s in its auction catalog. A very specific array of creases and deformities in the right-breast pocket flap appears to confirm that this jacket was worn by the Nvidia CEO at the Hon Hai [Foxconn] Tech Day in Taipei in October 2023. Sotheby’s also states that “The signature has been authenticated by James Spence Authentication.”

Any readers interested in grabbing this jacket will likely be interested in the condition of the garment. There are a few pictures on the catalog page, including a close-up, which was used to ‘photomatch’ this particular 2023 Tom Ford-made sample. As a Sotheby’s account holder, I also read the condition report, which begins with the assertion that “The Jacket and signature are in pristine condition.” But there follows a lot of legal jargon which can be summed up as – the actual condition and appearance of the jacket might not be perfect, nor look exactly like the photographic representations of it.

If you do splash your cash on this old jacket, you should be pleased to know that your hard-earned money will be going to charity. Sotheby’s says that the sale was organized by Long Journey Ventures to benefit the Edge Institute, described as “a non-profit that convenes people working at the frontiers of tech, science, culture, and society in pop-up villages (Edge Cities) to live together and experiment towards a brighter future.”

A low estimate to attract bidders?

Does the $40,000 to $60,000 estimate take into account the jacket's Jensanity factor, or AI inflation? Also, we are in an AI boom right now, with skyrocketing prices and valuations, yet people might have spent more than the top estimate for a mere NFT of this jacket a few years ago.

On a cautious note, though, we aren’t sure how many of these jackets the Nvidia CEO has in his wardrobe. And if there were another 10 or 20 that may someday be released for sale, then the expected price could slide dramatically.

A brand new Tom Ford SS2023 menswear collection jacket, without a sprinkle of genuine Jensanity, costs around $9,000. In that context, Sotheby’s estimate puts only a modest premium on this verified genuine Jensen Huang-worn and signed artifact.

Nvidia has announced a new business model under which it’ll be able to double-dip for revenue on the same silicon: once when its partner AI clouds use its hardware, and again as an ongoing percentage of the revenue that hardware generates. In a blog post co-authored by CFO Colette Kress, the company pitched the “revenue-sharing and credit-support model” as a way to open compute access to startups that can’t finance it themselves. In practice, cash-poor AI companies trade a slice of whatever they eventually earn for tokens today, while a supplier already running roughly 75% gross margins reaches into its customers’ income statements for a second helping of cash. Australia's Sharon AI and Singapore-based Firmus Technologies are the first named partners.

Under the structure, participating AI clouds procure Nvidia infrastructure and sell Nvidia-powered cloud services to end customers. Nvidia collects its usual product revenue on the hardware plus a percentage of the cloud income earned on that capacity, which the blog post describes as a recurring, usage-linked earnings stream. Per Bloomberg, developers receive token credits in exchange for a slice of their future sales, but neither Nvidia nor its partners has disclosed the revenue-split percentages.

It’s no secret that the credit-support side of this model will help to address a financing gap that Nvidia itself has identified. Even signed, long-term customer commitments have failed to convince lenders to fund large-scale deployments, leaving smaller clouds unable to borrow against the demand they had already generated.

In an 8-K filing dated June 12th, Sharon AI disclosed that the agreement runs for six years and covers 72 MW of new Australian data center capacity built to Nvidia's DSX AI factory design, scaling to as many as 40,000 Grace Blackwell GB300 GPUs. The Nasdaq-listed neocloud separately holds a revenue-share facility of up to $200 million with investor Digital Alpha, disclosed in its CY25 results, meaning portions of its income are now pledged in two directions. Meanwhile, Firmus is building a DSX-aligned campus in Batam, Indonesia, that’s expected to scale to 360 MW and house up to 170,000 Nvidia GPUs.

Nvidia has spent much of the last year funnelling cash directly to its customers, including a $30 billion participation in OpenAI's $110 billion funding round and backing for xAI's $20 billion Colossus 2 financing, arrangements that drew repeated circular financing criticism. The new model inverts that: rather than investing capital that returns as GPU orders, Nvidia extends credit support and collects a royalty on its partners’ sales for years afterward.

That royalty also ties a slice of Nvidia's income to utilization instead of hardware sales. If partner clouds can’t keep racks rented, the usage-linked stream shrinks, a live concern given the depreciation pressure already building on operators paying off hardware that Nvidia refreshes pretty much every year.