Intel's shares plunged 30% on Friday, wiping out nearly $150 billion in market capitalization in a single day, the largest single-day loss in the company's history. The decline followed a disappointing financial outlook, largely driven by an anticipated drop of more than 20% in sales at Intel's client computing group (CCPG), the company's main cash cow. The declines quickly spread across the semiconductor industry. Although losses elsewhere were nowhere near as severe as Intel's, approximately $1 trillion in semiconductor market capitalization was wiped out on Friday, according to intraday estimates by Benzinga.

Fire sale

The PHLX Semiconductor Sector Index (SOX), which tracks the stock performance of 30 major semiconductor companies, is one of the key indicators of the industry's financial health. On Friday, the index plunged 9.14%, or 1,052.82 points, to 10,465.38 points as of 2:31 PM ET, as Intel's disappointing outlook triggered a massive selloff across the semiconductor industry.

Intel was by far the biggest loser, as other chip-related stocks, while Arm (-12.68%), AMD (-12.48%), Qualcomm (-12.14%), Applied Materials (-8.73%), ASML (-8.13%), Lam Research (-8%), Micron (-7.28%), and Broadcom (-7.1%) suffered considerably less. Then again, while Nvidia lost 5.66% of its market cap, this translates to around $270 billion, which is a massive amount of money. Paradoxically, Nvidia lost more market capitalization in absolute dollar terms than Intel.

In an unexpected turn of events, the downturn extended to server manufacturers and cloud giants too, as Dell (-12.67%), HPE (-8.10%), Supermicro (-6.96%), and Oracle (-6.88%) lost nearly 7% to 13% of their value. Reuters noted that investors increasingly question the scale of capital investments required to expand AI infrastructure as well as the sustainability of that spending.

Gloomy outlook

Intel’s client computing and physical AI group (CCPG) generated $8.1 billion in revenue during Q3 2026, down 5% year-over-year and 9% sequentially. Weaker demand and increased competition reduced client processor shipments by 14% year-over-year, which was partially offset by a 9% increase in average selling prices. CCPG accounted for nearly half of Intel's revenue of around $16.5 billion and the company's main source of cash. As strong demand for server processors only partially offsets declining client sales, Intel guided its Q4 revenue to be between $15 billion and $16 billion.

Intel expects the client market to deteriorate further in Q4 2026 and into 2027 as elevated memory prices and shortages of other components impact PC prices and demand. In general, Intel expects the client PC market to decline 10% in 2027 compared to 2026, which will inevitably have an effect on CCPG performance. While Intel expects its data center business to continue growing in 2027, industry-wide supply constraints will limit its ability to fully capitalize on demand.

2026 has been a mixed bag for Intel so far. On the one hand, the company achieved its highest market capitalization ever — $673.43 billion in mid-June — on growing sales of data center CPUs, strong sales of client products, and a return to profitability (if the $12.5 billion non-cash accounting charge associated with shares issued to the U.S. government is excluded). On the other hand, the company's capitalization has dropped by nearly 50% to $338.91 billion since mid-June (with $148.68 billion in just one day), as investors began to realize that the performance of Intel's CCPG heavily depends on memory prices, as customers and businesses tend not to like overpriced systems. Yet on the other hand, now that Intel sells fewer client processors, it can sell more expensive data center CPUs.

What about Apple?

Surprisingly, Apple (-1.66%), which only sells client products, was nearly unaffected by the performance of Intel's client computing group, as investors probably believe that the world's largest supplier of consumer electronics is more immune to memory prices than Intel. Ironically, if we count the A-series SoCs used in iPhones (which tend to outperform mainstream laptop CPUs), Apple sells substantially more processors than Intel, making Apple by far the world's largest supplier of microprocessors. Despite this, Apple's shares were largely unaffected by the semiconductor selloff.

There are several reasons for this distinction. Firstly, Apple's business model is fundamentally different. Intel sells processors to PC manufacturers, whereas Apple designs its own processors and sells them as part of finished products. Consequently, Apple captures revenue from the entire device rather than just the processor. Secondly, the two companies face different demand dynamics. Intel depends heavily on the PC market, while Apple's iPhone business follows the smartphone replacement cycle. Rising memory prices could affect both, but not necessarily to the same extent and at the same time.

There is an additional irony: Apple is also a major customer of TSMC, so its processor supply chain overlaps with those of AMD, Intel, and other semiconductor companies whose shares declined sharply.

While we should not directly compare Apple and Intel, the comparison illustrates that investors were not necessarily abandoning companies that design or use semiconductors, but were rather concerned about the profitability and growth prospects of these high-tech companies. And if we take a look at the situation from this point of view, the whole thing will start looking completely different.

Nvidia’s Vera CPU is an important next step in the company’s years-long spearheading of AI infrastructure buildout. It’s arriving just as the shift to agentic AI workflows has created unprecedented demand for data center CPUs, and it’s designed specifically for this moment, at least according to Nvidia’s marketing bullet points. Although the idea that anyone developed a CPU for a shift in demand that arose just six months ago is reaching, as our benchmarks below show, Nvidia’s claims about Vera aren’t full of hot air.

Based on the workloads we have now — there aren’t standardized end-to-end agentic benchmarks focused on the CPU right now — Vera has strong indicators about its usefulness in agentic workflows. It offers chart-topping performance in Python, for instance, which makes up much of the backbone of the AI software ecosystem.

It also shows commanding memory bandwidth, both at the socket level and system level, with the high-speed C2C interconnect between Vera chips. In contentious agentic environments where agents may be querying large databases, that bandwidth is critical.

Specialization is one thing, but Vera is arriving in a hotly contested server market with custom Arm chips penetrating the data center and the old guard of x86 chips from AMD and Intel, which not only have a foundation in the data center, but also an extensive list of clients that work with their systems. Regardless of how Vera performs, that’s no small undertaking.

What’s surprising about Vera isn’t that it performs well in these agentic-adjacent workloads, but that it offers competent performance in general-purpose server workloads as well, often managing to match or beat the comparable AMD Turin and Intel Granite Rapids-AP chips. The scales are more balanced when looking at frontend workloads, with the core density available on x86 CPUs often the winner, but Vera trades that density for strong performance in backend, data-rich operations.

It’s an impressive showing for Nvidia's first custom core, called Olympus, and a stark improvement in nearly all aspects over Grace, which used an off-the-shelf Arm core design. The 88-core CPU is built on the Armv9.2-A architecture, including two threads per core through Nvidia’s “spatial multithreading.”

The chip is characterized by a 10-wide decode, similar to Arm’s AGI, as well as Nvidia’s second-gen Scalable Coherency Fabric (SCF). You can read more about the SCF and the Olympus core in our Vera deep dive, but SCF is critical to Vera’s performance. It’s a mesh that distributes L3 across the die, offering up to 3.4 TB/s of bandwidth across the die.

Vera has high memory bandwidth as well, leveraging replaceable SOCAMM2 LPDDR5X at 9600 MT/s, offering up to 1.2 TB/s of total memory bandwidth or around 14 GB/s per core, more than double that of AMD’s dense Turin designs.

We’ve heard a lot about the speeds and feeds of Vera, however. It’s time to understand its role in the modern AI data center, as well as see how it performs in our benchmarks against the x86 competition before AMD’s Venice and Intel’s Diamond Rapids start rolling out.

Understanding the CPU's role in agentic AI

Depending on where you’re at in the fast-moving world of agentic AI, you probably have a different definition of what exactly an agentic CPU is. Given the rise in prominence of agentic CPUs, the understanding of what makes a CPU tick for this type of workload has been largely defined by two companies: AMD and Nvidia. Although the points they’ve driven about concurrency and the varied work of agents are correct, it’s useful to derive a definition from how we’re seeing agentic platforms deployed right now.

Let’s back up from there and look at real agentic workflows in action to better understand where CPUs fit into this chain. As we’ve seen with Meta Muse and OpenAI Dots, there’s a class of persistent agent that gives you a slice of compute in the cloud. These CPU-only racks communicate with inference servers to actually run the models, but their workload is defined by concurrency. It’s not about the performance of an individual sandbox but instead how each sandbox performs when the chip is fully loaded.

Concurrency provides important context to some of the workloads companies like Nvidia and AMD have described. Some tool calls are more demanding, like code compilation and large database queries, but reading/writing files, running code tests, and going online in a headless browser is not demanding CPU work in isolation. When the chip is handling potentially thousands of these tool calls, however, slight advantages start to pile up. And, the CPU topology plays a bigger role in how performance is sustained as agent sessions increase.

That’s one major tenet of an agentic CPU, one that can maintain performance with high concurrency. The other is variability, which touches on the CPU, but also the larger infrastructure available. Mark Lohmeyer, VP/GM of AI and computing infrastructure at Google Cloud, described an example to Tom’s Hardware about planning a vacation with an agent. Using AI to plan a vacation is just about as trite as examples get, but Lohmeyer sheds some light on the unique challenge of agentic workloads from the hyperscaler perspective.

In the example, Lohmeyer described planning a vacation over the course of a month, where he would suddenly prompt the agent to do a bunch of work, but may not return to prompt again for another week. “The actual use of the underlying compute, it's happening in basically bursts, right? There's like a burst of use at the start, maybe for an hour, and then it goes away, and then a couple days later, [it] bursts again for some other purpose and goes away,” Lohmeyer said. “They're very long-running workloads, effectively long-running, but often idle, basically.”

Agentic calls for a lot of power on demand, as the right alignment of this bursty work on a server with hardware contention can bring everyone’s sessions to a crawl. This factors into the type of work the CPU is doing, as well, which can include heavy code compilation, graph traversal, and large database queries. Scale that up across several agents, all with their own independent loops that don’t neatly align with each other, and the CPU problem for agentic AI becomes clear.

In the August 2026 study, Architectural Implications of Agentic AI Workflows, from researchers at The University of Texas at Austin and Microsoft Azure, the paper describes three tasks of the CPU in an agentic flow. In addition to a scheduler for GPU inference, which is how CPUs function in one-shot-style LLM queries, CPUs also serve as orchestrators for maintaining agent states and communication, and as runners that call tools and execute agent logic (often in Python).

The paper also shows this contention that can happen when multiple agents are running at once, passing requests to the GPU, receiving responses, acting, and then passing requests back. We’re looking at a cross-section of four agents over less than half a minute, and you can already see how small CPU-based tasks can align, as well as create hangs between GPU time.

Nvidia has been clear that Vera is an agentic CPU, though defining the performance of an agentic loop is still a work in progress. “The industry built a pretty good way to benchmark traditional workloads,” Lohmeyer said. “I think this is happening now for GPUs and TPUs for inference, for example. I think we're going to need the same type of industry-level benchmarks for agentic workloads that are probably still emerging.”

Standardized benchmarks that look at the full agentic loop aren’t available yet. We have our own internal tests that we’re running and hope to have data for soon, and we’ve seen some vendor-level benchmarking from companies like Modal, but the broader industry hasn’t come forth with a set of benchmarks for the end-to-end agentic loop along the lines of a SPEC or TPC yet.

Although those tests aren’t available yet, we can still look at CPU performance through the lens of an agentic workflow, prioritizing aspects of performance that are important for agentic loops. We can also look at common workflows for agents, including code compilation for coding agents and database performance for retrieval agents. We can look at things like JSON parsing through the simdjson library, evaluating how these CPUs parse the large JSON files agents use to communicate. And we can look at the foundation of much of the AI ecosystem with Python performance.

That’s the focus of our benchmarking here, to establish where Vera stands among the x86 competition from AMD and Intel in well-known benchmarks. Hopefully we’ll have the opportunity for further testing in the near future to evaluate full agentic loops once that testing procedure is finalized.

Although we’re evaluating performance through an agentic lens here, Nvidia did not limit what tests we could run nor place any stipulations on our coverage. We’re thankful to Nvidia for providing us with remote access to a dual-socket Vera system for the purposes of testing, but beyond configuration checks to ensure the platform was operating as expected, we were left to run the tests we wanted to run. That includes several tests that are applicable to agentic workflows, but also tests from our general server CPU benchmarking suite, including Java, cryptography, compression, and rendering performance.

A gentle reminder for readers

Servers are not client PCs. You have much smarter operators with deeper access to the hardware, and often a software stack that’s unique to the specific workload at minimum, and highly tweaked and optimized for that workload at a maximum. With client CPUs, we can say that an AMD X3D chip is the best for gaming, or that Intel’s latest flagship offers the highest single-threaded performance. Servers depend much more on use cases.

In a typical review, we’d say that X is better than Y for Z purpose, and back that up with data. Here, that commentary is more muted. The reality is that hyperscalers maintain large fleets with a variety of hardware that can fit any purpose, and enterprises need to choose the hardware best suited not only for their operations, but also for TCO and a list of at least a dozen other variables that are specific to that business.

Nvidia Vera SPEC CPU 2026 estimates

So far, the primary benchmark we’ve had for Vera is SPEC CPU 2026 intrate, so we ran the full SPEC CPU 2026 suite to check Nvidia’s performance claims, as well as look at floating point performance. For anyone who hasn’t read SPEC CPU benchmarks before, there are a few disclaimers before heading into the results.

All of the runs here are estimates, and that’s due to how SPEC reporting works. There are strict reporting rules, and we’re not reporting these results to SPEC directly. The charts say “estimated,” but the numbers aren’t a guess. We ran SPEC CPU 2026 following the standard testing procedure, using the same compiler and flags for each CPU.

For the compiler, we used GCC 15.2 with a few common flags, including link-time optimization and march/mcpu to expose the correct instruction support to the compiler. We also used the general-purpose jemalloc memory allocator across all of the tests we ran.

Starting with the overall intrate, this is a series of applications primarily dealing with integer operations instead of floats, and the SOP is to run a copy of the application on each thread available. The test looks at what a CPU is capable of with many applications running at once, measuring throughput.

Nvidia’s numbers aren’t dissimilar from our own. In the company’s white paper, it showed Vera ahead of the EPYC 9755 by 3%, and with our tests, Vera is ahead by 1.5%. Keep in mind the considerably higher 512-thread count of the 9755 system here against the 352-thread Vera system. Vera absolutely runs away with performance when breaking down the score per-core.

Despite the core density of Intel’s 512-thread Xeon 6980P in a two-socket configuration, it’s not in the conversation here. Retesting with oneAPI represented an impressive 10% increase in performance with Intel-optimized binaries, but even against optimized code, Vera is 8.6% ahead with generic GCC.

There are some tests where Vera steals the show in the above album, as well as some where it falls behind, reflective of the testing we have later in this article. You can see that GCC compiler performance and Python performance are particularly strong points, with Nvidia even beating out AMD’s top-of-stack EPYC 9965.

Elsewhere, there are some weaknesses. SEAL crypto provides a look at the weaker cryptography performance of Vera, which is reflected in our other testing. Zstd compression also shows Vera trailing AMD and Intel, which makes sense. In our separate Zstd testing, Vera offered impressive compression performance with Zstd but fell behind on decompression.

We were curious about float performance on Vera given that Nvidia led with the standard intrate suite in its initial benchmarks. Vera tops the charts here on the back of some particularly strong showings in some of the tests. Our estimated PALM numbers, for instance, show off Vera’s second-generation Scalable Coherency Fabric (SCF), while ROMS pushes on memory bandwidth. This strong memory performance shows up in Marian and LBM, as well. There are a couple of misses, notably FemFlow and CACTUS, but the overall performance picture is clear.

The throughput benchmarks are what matter here for chips in this class, but we also ran SPEC CPU 2026’s intspeed suite for some estimated results, scaling up to use every thread available. No one is realistically running a single application on servers utilizing every thread available like this, but the numbers are there to browse if you’re interested.

Nvidia Vera Python and AI benchmarks

If there’s any direct benchmark for agentic AI right now, it’s Python performance. There isn’t one universal language for AI agents, but Python (and, to a lesser extent, TypeScript) make up the foundational agentic frameworks like LangGraph, CrewAI, and AutoGen. A strong agentic CPU right now is defined in part by strong Python performance, and Vera has exactly that.

Starting with a broad overview, you can see the total average test times for PyBench above, with Vera leading the pack. It’s 4% ahead of the EPYC 9755, which itself leads Intel’s Xeons and the larger 9965. Python is largely single-threaded, especially for these basic, built-in functions, so we see the 9755 pair leading the 9965 pair throughout all of our Python benchmarks.

PyPerformance provides a much more granular look at Python performance, evaluating start-up times for the Python interpreter, regular expressions, and even full Python applications. The story doesn’t change, however. Outside of a couple of misses in web server-focused tests, Vera tops the charts across the board.

The AI ecosystem right now is largely built on Python, so strong performance here is a good early indicator of strong AI performance in the data center. Of course, this space is rapidly evolving, and other languages are working their way into the ecosystem. For this moment, however, Python is critical, and it’s an aspect of performance Vera nails.

We also ran Llama.cpp with a CPU BLAS backend, checking text generation and prompt processing (at different token lengths) on the CPU using gpt-oss-20b-q8. For prompt processing, much of the process is handled on the GPU, so this pure-CPU benchmark isn’t totally representative of a real deployment. However, the CPU still plays a role here, particularly in tokenization.

Unsurprisingly, we can see the single 6980P topping the charts in prompt processing, with a 2P Xeon 6980P coming up in second place, likely on the back of Intel’s AI-specific ISA extensions. We expect AMD to further extend support for these instructions in Zen 6, which should level the playing field.

Nvidia Vera memory and compression benchmarks

Nvidia has touted the memory bandwidth of Vera as one of the chip’s strongest points, and the industry-standard STREAM memory bandwidth benchmark backs up those claims. Vera comes with a memory bus width more than twice as large as Grace, plus a large jump up to LPDDR5X-9600. It’s capable of up to 1.2 TB/s of memory bandwidth through the LPDDR5X interface, and up to 1.8 TB/s of bandwidth between Vera sockets.

AMD’s support for DDR5-6000 is relatively slow, and although Intel offers more bandwidth with MRDIMMs reaching up to 8,800 MT/s, Vera is in a league of its own here. That’s true without optimization, as well. After my initial STREAM pass, Nvidia suggested passing the OpenMP “spread” policy, which distributes processes as far away from each other as possible to reduce cache/memory contention.

With AMD and Intel, we see a slight regression with this setting, but it offers Vera a massive jump in memory bandwidth. Undoubtedly, the scales will start to level off as AMD and Intel roll out their next-gen Venice and Diamond Rapids parts, both of which tout much higher memory bandwidth than their predecessors.

Flipping over to the simple MBW bandwidth benchmark, we can see similarly strong performance from Vera, regardless of copy size.

The classic CacheBench benchmark flips the script, with Vera trailing the pack in the combined read / modify / write test. This weaker cache performance here is largely overshadowed by the memory bandwidth. The performance level is slightly below Nvidia’s contemporaries, but not by enough to make a significant difference.

Vera loses some points in compression workloads, which we see echoed in our SPEC runs in dedicated benchmarks. Using Zstd, Vera offers chart-topping compression performance at the default level 3 compression, as well as the maximum level 19 compression, across both standard and Long Mode runs. However, in decompression, the Turin chips take over, with Vera sitting alongside or below a dual 6980P configuration.

7-Zip traditionally favors Zen, so it’s no surprise to see both Turin configurations at the top of the charts. Vera loses out to the dual 6980P system in both compression and decompression performance via 7-Zip.

Nvidia Vera Java, database, and crypto benchmarks

Python is the language of choice for the AI ecosystem right now, but JavaScript/TypeScript are a close second. We ran three standard Java suites to evaluate performance: Java SciMark, DaCapo, and Renaissance.

Java SciMark is the best place to start. AMD wins out with the Zen 5-based 9755 in the composite score, but Vera maintains surprisingly strong positions in straight scientific operations, including FFTs, sparse matrix multiplies, and a Monte Carlo simulation. It falls behind in dense LU matrix factorization and Jacobi SOR.

DaCapo and Renaissance are both Java suites with a wide variety of real Java workloads, focused on native performance in DaCapo and VMs in Renaissance.

Database performance for Vera is strong across our ClickHouse and SQLite benchmarks. In max-thread SQLite, Vera is only beaten by a single-socket 6980P server, and it jumps into the lead in our like-for-like 64-thread and 128-thread tests. ClickHouse provides some interesting data that goes beyond a steady-state SQL database.

On the second and third runs of ClickHouse, Vera is competitive with a dual EPYC 9755 server, but from a cold cache, Vera is much faster, racking up an advantage of nearly 40% in the first run.

For bringing that data in, we measured JSON parsing speed with simdjson, which, as the name suggests, relies heavily on modern SIMD instructions. Turin has a natural lead here with its native AVX-512 execution.

In cryptography workloads, Vera falls behind the pack significantly in the John The Ripper password cracker, while trailing the massive core arrays of its competition in OpenSSL.

Nvidia Vera HPC and compilation benchmarks

HPC is usually the domain of dense CPUs, but Vera performs well in a variety of HPC tasks that we tested. In Discrete Fourier Transforms (DFT) using FFTE, Vera tops the charts, as it does in Octave and CloverLeaf. Although not strictly HPC, Vera falls in the middle of the pack in intensive mathematical operations like m-queens, and it trails chips with massive core arrays in the heavily threaded Primesieve.

Code compilation is a more contentious workload, with Vera trading blows against AMD and Intel depending on the workload. Starting with the compilers themselves, Vera takes a strong lead in a timed GCC compilation, beating out a dual 9755 system by 12%. It falls into last place in our LLVM compilation tests, however, both using standard Makefiles and Ninja. These tests measure the time it takes to compile the compiler itself, not the time it takes to compile an application with it.

Elsewhere, Vera shows up strongly in ImageMagick and PHP compiles while losing out slightly to the dual 9755s in Godot. Compiler performance is important to agentic AI given the prominence of coding agents, which often need to build software from source as part of an agentic coding workflow.

Nvidia Vera encoding and rendering benchmarks

Encoding and rendering workloads aren’t the focus of Vera, but we ran our standard list of benchmarks, which includes some rendering and encoding tests. Vera has strong performance here in some applications, though it certainly takes a backseat to the headline Python and memory performance shared earlier in this article.

The fight is between AMD and Nvidia here, with the 9755 and its full Zen 5 cores topping the charts in our AV1 encoding tests. With x265, Vera comes out on top, though by a margin lower than the 25%+ we can see the 9755s hold with AV1.

We normally run Blender, though there isn’t an official Arm Linux version available, so we skipped the test here. Instead, we have the cross-platform C-Ray and POV-Ray. Vera manages to outclass even the 768 threads available on a 2P EPYC 9965 server, both at 1080p and 4K. Vera also leads in POV-Ray, though on a technicality. Every server we tested was tightly coupled short of the 2P 6980P server.

For a more granular look, we have OSPRay, where Vera tops the charts in real-time path tracing, though falls short of the AMD and Intel flagships in real-time scientific visualization. The “dim_512” subset is worth drawing attention to. Strong AVX-512 performance allows AMD to offer three or even four times the performance of Vera in this workload.

Nvidia makes a delayed debut

Although Vera isn’t Nvidia’s first data center CPU, it’s the first one that the company is aggressively pursuing with the same fervor that it carries with its accelerators. It’s an impressive chip, often punching far above its weight class, especially in those workloads that can show off its unique capabilities and topology.

Still, it’s a single 88-core chip arriving in a market characterized by granular SKU lists to meet a wide range of specific workloads. Vera won’t be the right choice for all of them, which is something that Nvidia itself has already stated. “The world is not going to be served by one SKU of CPU. And that's not our intention,” said Nvidia’s Ian Buck when Vera was revealed in March.

That’s not the intention with Vera, but the performance against contemporary x86 CPUs shows that Nvidia’s new push into standalone data center CPUs has legs, more so than the various building-block-style Arm chips we’ve seen from hyperscalers, certainly. If nothing else, Vera demands some attention from AMD and Intel.

The x86 crew is responding shortly, with Zen 6 queued up to roll out through the end of the year and Intel’s Diamond Rapids targeting a 2027 release. As you can imagine, we'll be putting those chips through their paces as well.

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The AI industry continues to gobble up manufacturing resources that were once dedicated to serving PC, smart device, and gadget makers. We’ve seen huge demand/supply driven price rises in RAM, GPUs, SSDs, and more through 2026. Now that most basic and unglamorous electronics necessity, the PCB, looks set to join them. A news report published by Nikkei Asia quotes several leading PC and PCB industry managers, making it clear we are going to see PCB (and PCB subcomponent) shortages and price rises in the coming months.

An unnamed exec from an American PC maker spoke to Nikkei about the dire shortage of PCBs. “Even though PC shipments could decline again next year, we are still struggling to secure enough PCB supplies to meet our needs,” said the manager, who asked to remain anonymous. They were talking about securing enough supplies to make plans for products in the coming year. Several commenters pointed to AI computing demand eating up production capacity.

The outlet also talked to business people further up the supply chain – the makers of PCBs and the makers of the components/materials needed to actually make PCBs. Traditional PCB demand is becoming underserved as AI products demand and pay more. One PCB maker talking to Nikkei said that a significant number of products have lead times that have doubled. Specifically, some PCB orders will take as long as 30 months to fulfill. Another PCB supply firm explained to Nikkei that a new order today would not be fulfilled until late in 2028.

Though PCB makers are planning to invest and expand, they also need to secure multiple upstream materials. These can include copper clad laminates (CCLs) and the specialized fiberglass used for PCB construction.

One Taiwanese PC maker noted that costs for CCLs have surged at least 12 times since July. Moreover, one of the world’s leading suppliers of CCLs has told the Nikkei that it has an order book that is currently filling up to 2030. This situation appears to have occurred despite its plans to triple capacity by 2028.

In closely related developments, Nikkei observed that Nvidia recently announced partnerships with several major chip substrate makers. Chip substrates are essentially the tiny and intricate PCBs that CPUs and GPUs are mounted on. Japan's Ibiden, South Korea's Samsung Electro-Mechanics, and Unimicron of Taiwan all have new agreements with the Green team. Unimicron is looking at expanding capacity by 2.5x over the next three years.

PCBs have been viewed as an unglamorous lower-tech part of the supply chain, partly due to the small numbers on the BoM. That may change. Companies are now racing to ramp up production of advanced PCBs and chip substrates to meet demand, and boost income.

Finally, the source report also notes that it is increasingly difficult to get the engineering staff required at new production facilities. Salaries have already risen 20% to 30% across the PCB industry to keep skilled employees.

The Nvidia GeForce RTX 5080, Nvidia's second-fastest GPU from its current generation, now costs as much as the RTX 5090 did, on paper, when it launched. Prices have reached $2,000 and higher, with most of the available stock online limited to third-party sellers. If you can find it in stock, it may end up costing you two to three times as much as it did when it launched.

The cheapest, in-stock RTX 5080 available to buy from a reputable seller online for delivery is this PNY RTX 5080 for $1,999 from Best Buy, the same price as the RTX 5090's on-paper MSRP. You might otherwise end up paying $2,299.99, at least, for this Asus ROG Astral mode at Newegg, or you could pay nearly $3000 for this Gigabyte RTX 5080 from MemoryC, now priced at $2,983.23.

Stock for the RTX 5080 is otherwise limited everywhere, with just one or two available for local pickup (in Best Buy's case), and limited options for delivery. You can also find similar in-store-only pricing at Micro Center, depending on location, which has a Gigabyte card for $1,979.99.

You'll also see a lot of the listings online from sketchy, third-party sellers.. For instance, you could pay at least $2,211.17 for this PNY RTX 5080 from a third-party Amazon seller. Empowered PC, the seller for this card, has had 81% positive ratings over the last 12 months, with 202 ratings in total.

Reputable seller or not, this price scale means you're paying significantly more than the $999 launch price for this GPU. As our GPU price tracking shows, though, this isn't an isolated case. RTX 5080 prices have, like the rest of the RTX 50-series, risen steadily since it launched. However, this is the first time that its price floor has been around the RTX 5090's launch price. That PNY RTX 5080, for instance, was being sold for around $1,500 on average back in August.

It's a bitter pill for gamers, though, that the RTX 5080 has now reached and breached the RTX 5090's launch pricing. That GPU has an MSRP of $1,999, although it'll now cost you thousands more. Prices for the RTX 5090 hit as high as $9,500 last month, but the situation is even worse today. What little stock that remains on sale online can be found for as high as $12,000.

The RTX 5080 uses Nvidia's GB203 GPU and comes with 10,752 CUDA cores, along with 16GB of GDDR7 VRAM. Unfortunately, it, like the rest of the consumer gaming GPU market, is being affected by AI. Demand for high-end GPUs hasn't stopped, but Nvidia's attention has shifted to the enterprise market. So, too, has the focus of memory manufacturers, meaning DRAM allocations and production lines that would otherwise produce memory chips for graphics cards have instead gone to feeding the demand for high-margin HBM for AI data centers.

Multiple sources claim that a top executive at a Singapore AI firm suspected of steering Nvidia AI GPUs to China and who has close business ties with Jensen Huang was apparently passing information to Beijing as a Chinese intelligence asset. According to The New York Times, U.S. intelligence has been aware of her activities for over a year already and has even briefed President Donald Trump on the situation. However, Trump reportedly chose not to do anything about the information, as he apparently did not want to affect Nvidia sales or antagonize China.

The suspect, Alice Huang or Huang Le, was listed as the managing director of Singaporean AI firm Megaspeed, an Nvidia cloud partner that builds data centers and rents out the compute to Chinese firms like Alibaba. The investigation into the firm began when the U.S. Commerce Department visited the firm’s Malaysia data center and found the Nvidia equipment that it ordered still packed in boxes. This raised suspicion that the machines were either just shipped in or about to be shipped out, instead of being installed on the site. However, it remains unclear what made U.S. intelligence agencies investigate Ms. Huang. Megaspeed was accused of helping smuggle $2 billion worth of Nvidia chips last year.

There have been multiple instances where Ms. Huang was spotted in public with the Nvidia chief. She was photographed sitting beside Jensen Huang alongside other executives in an informal event around Computex 2024 in a LinkedIn post by NetThunder, identified as Megaspeed Chairwoman Alice Wong. About a year later, around the same time as Computex 2025, she was once again seen with Jensen Huang leaving a restaurant in Taipei alongside other business associates.

“Megaspeed has been no different than many other regional cloud customers. Jensen’s interactions with Ms. Huang are not unusual or unexpected. Meetings with customers are part of a CEO's job; Jensen talks with thousands of customer executives every year,” an Nvidia spokesperson told Tom's Hardware. “Our regional cloud customers receive ordinary business information — not sensitive NVIDIA trade secrets — and zero information relevant to national security." Still, the possibility that Chinese intelligence is targeting a senior figure close to President Trump is apparently troubling some U.S. officials.

The Chinese embassy in Washington denied the allegations, saying that it was merely “groundless smears and slanders” against China. Nevertheless, it would also make sense for Beijing to keep tabs on senior executives of the top American firms that arguably single-handedly fueled the AI boom. And given Jensen Huang’s status as a celebrity CEO, it’s not surprising that he is surrounded by all kinds of people from both the U.S. and China. Sources cited in the report claimed that Mr. Huang hasn’t yet been given a defensive briefing — a warning that U.S. counterintelligence agencies would sometimes give to American citizens who have been targeted by foreign intelligence services — even though the surveillance against Alice Huang continues. The report speculates it is possible this was done to avoid alerting Alice Huang that she was being investigated.

As for Jensen, U.S. officials do not believe that he is aware of Ms. Huang’s intelligence activities. "We're confident that if the U.S. government believes that any person is a threat to American industry, it would let us know," Nvidia told Tom’s Hardware. "We often receive updates from the U.S. government regarding parties and entities of concern and always act accordingly."

Nvidia is reportedly facing questions from the United States Department of Justice over whether its purchase of Hugging Face will have an impact on competition in the growing AI space. Nvidia agreed to buy Hugging Face in September, paying almost $13 billion. Hugging Face offers an open-source platform for the hosting and testing of new AI models, and that appears to be behind the DOJ's concerns. Reuters reports that the department has made inquiries about how the Hugging Face deal could change the landscape for Nvidia's competitors. The move could be seen as a way for Nvidia to lock in a key distribution channel for its own products. Hugging Face also sells AI infrastructure. Nvidia has declined to comment on the story.

This isn't the first time that Nvidia has found itself in the DOJ's crosshairs. As Nvidia becomes more vital to the tech industry as a whole and AI technologies specifically, the company has come under increasing scrutiny within the U.S. government, stretching back to the Biden administration. Things didn't improve when the Trump administration took over, with the U.S. president reportedly tempted to break the company up — despite being unfamiliar with what it was. More recently, Nvidia and other AI hardware makers found themselves under export controls. Nvidia's powerful AI-focused hardware can only be sold to approved entities, while it cannot ship to countries including China, Iran, North Korea, and Russia.

At home, the DOJ has also looked into Nvidia's purchase of SchedMD, a company whose software is used to run supercomputers that have ties to national security. Nvidia promised to continue to support the software as part of a larger three-year investigation into its conduct. Officials have also considered whether Nvidia dangled a larger allocation of its AI chips in front of customers who also purchased its networking hardware as well as Groq chips — Nvidia bought Groq for $20 billion in December of 2025. The deal included the acquihires of key members of the AI chip startup's team, including its CEO.

As for Hugging Face, Nvidia has similarly confirmed that it will continue to support a range of chip and AI providers once the buyout completes. It's expected that Hugging Face will officially become part of Nvidia in the first half of 2027.

Anthropic spent less than a third of its $8.9 billion first-half compute bill on serving customers, with most of the rest going into training, research, and running Claude internally, its S-1 filing shows. It has meanwhile signed up for hundreds of billions of dollars more in TPU and GPU capacity. By our math, it has meanwhile signed up for roughly $800 billion more in TPU and GPU capacity, and revenue per compute dollar jumped from $0.40 in Q1 2025 to $2.08 in Q2 2026, which the company links to capacity purchased 12–18 months ahead.

Anthropic’s IPO is likely to beat that of rival OpenAI, which filed confidentially before CEO Sam Altman said its own won’t come this year, amid widespread doubts about an AI bubble. Whether AI labs can cover what it costs to serve their models is an open question.

Anthropic appears to do so easily: it spent $2.4 billion serving customers in the first half of the year, versus $16.3 billion in revenue. Paying for what it has on order is more difficult. Its five compute deals with the biggest stated amounts would cost an estimated $103 billion a year on average once delivered, more than twice its revenue at Q2’s pace of about $46 billion a year. The company also warns that margins may compress as new hardware lands ahead of the revenue it will support.

The company has three main costs: the cost of revenue, which is the inference cost of running Claude for paying customers; R&D, which is mostly compute for training and research into new models plus any internal use; and sales and marketing, which covers the cut taken by channel partners and cloud platform resellers.

Comparing the first half of last year to this year’s, cost of revenue is up about 2.3x, but with revenue up 14x, that means Anthropic keeps an estimated 85 cents for every dollar after serving costs, up from just 12 cents. R&D is up about 5 times, with 66% or more of the compute going to it, and that’s the main source of its operating loss. Sales and marketing is up 16x, and doesn’t show up in cost of revenue, but its partner fees and marketing would lower what it keeps to 66 cents a dollar. The trend here is that serving Claude now more than covers its costs.

Serving has kept getting cheaper for customers since June: in September, Anthropic’s Opus 5.5 model cut costs per task by about 40% versus Opus 5, says the company, and Fable 5.1 cut the cost of typical workloads by about 25% against Fable 5; each came from “a combination of a lower price and improved efficiency,” made “while preserving our margins.”

As for compute, which runs through all three costs, Anthropic runs it as “a single, shared resource across all workloads” and doesn’t disclose how it decides the split between training, research, and serving. Serving was limited, as the company has “consistently operated in a compute-constrained environment where customer demand exceeds available compute,” which suggests serving costs were muted by a lack of supply, not just efficiency.

Anthropic runs a “multi-chip compute architecture across AWS Trainium, Google TPUs, Nvidia GPUs, and AMD GPUs,” without giving any percentage splits or GW measurements. For TPUs, it has five-year equipment leases with Broadcom for “TPU-based compute capacity,” with rent of “approximately $161.2 billion excluding interest”; some of that capacity came online this year, with more from 2027. Another $114.5 billion or more is with Google “between April 2026 and July 2033,” with more from Google and other TPU providers from 2028 to 2031, estimated to be in the $125–$150 billion range.

Trainium comes through Amazon, with a deal for “at least $110.0 billion between May 2026 and April 2036,” though the filing doesn’t split AWS spend by chip. Nvidia GPUs come through a series of deals, including “up to $120.5 billion … extending into 2029” with SpaceXAI, “at least $44.9 billion” extending into 2033 with Microsoft, “up to approximately $44.6 billion” also into 2033 with Nscale, and about $41.3 billion into 2034 with Lambda. There is also an AMD hardware supply agreement which, in aggregate, may “exceed $20.0 billion” if Anthropic takes the full initial capacity. These deals each have their own conditions for termination.

Anthropic’s minimum purchase commitments, which exclude equipment leases such as Broadcom’s, have gone from about $55 billion at the end of last year to about $265.6 billion by the end of Q2 this year. Since then, it has signed about $245 billion in new minimums, leases included, and TPUs are the biggest line overall. The Nvidia capacity, Microsoft’s aside, is also the easiest for Anthropic to walk away from.

With all these multi-year commitments, bought well ahead of delivery, expenses are paid and recognized “as compute becomes available to us,” the company says. That compute base “did not fluctuate with in-period demand,” so revenue per compute dollar increased as demand surged. When demand outruns that capacity, Anthropic buys short-term compute, which “typically carries a higher cost per unit.”

Our $103 billion estimate is straight-line, spreading each deal evenly over its term except SpaceXAI, which is at its full monthly rate, while payments ramp up as capacity is delivered. Also not included in the estimate is interest on the Broadcom leases. Compute also does not necessarily have to earn revenue, especially in R&D.

The filing has many blanks, such as the total commitment figure, the contractual obligations table, Q3 revenue, and Run-Rate Revenue, which an amended S-1 would normally fill in before pricing. No date for that has been given. Microsoft’s minimum starts in November, and SpaceXAI’s full delivery is expected in December, with non-cancelable periods ending in January and March 2027, after which either side can cancel on 90 days’ notice. More Broadcom TPU capacity and AMD’s first tranche follow next year, by which point most of these deals will be billing.

Malaysian customs have seized over $12 million (49.5 million Malaysian ringgit) worth of advanced AI chips in a single bust, as the country clamps down on a suspected semiconductor smuggling syndicate. According to a Reuters report on October 28, officials found 30 servers containing the chips at Kuala Lumpur International Airport’s free trade zone on October 15th. Initial investigations revealed that the shipment originated in an Asian country and was bound for another undisclosed Asian country.

The airport’s customs director, Aspalila Ag Tuah, confirmed that the servers were falsely declared as ordinary electronic goods, saying the suspected scheme used Malaysia as a transit point to avoid restrictions on the goods’ onward movement. The Malaysian company involved in the shipment has been summoned to assist in the investigation.

Authorities suspect that the foiled smuggling attempt was carried out by the same group responsible for a similar attempt last June. On June 5, customs officers discovered 72 server units containing advanced AI chips at the Kuala Lumpur International Airport, valued at approximately $13 million. This shipment had also been falsely declared as computer components and was meant to be reshipped without the required permit. Aspalila said the network has been linked to seizures worth over $25 million so far across at least five cases. She declined to provide further information on the network or the current case, citing ongoing investigations.

Several key facts remain unknown, including the chip type, country of manufacture, final destination, and intended recipient — whether a company or a military organization. The U.S. had placed export controls on the movement of advanced Nvidia and other U.S.-origin AI chips to China, the most likely destination for the seized chips. Smuggling networks began using Malaysia, a U.S. ally not affected by the controls, as a hub, exporting the banned chips to the country before re-exporting them to China.

While the country’s geographic location and mature logistics infrastructure make it important for legitimate international trade, these characteristics also make it ideal for smuggling sensitive technology. To restrict this avenue, Malaysia subsequently imposed export controls on the ​movement of advanced AI chips of U.S. origin, at Washington’s request. Other Asian U.S. allies, such as Singapore and Taiwan, have implemented similar controls. Demand for powerful Nvidia GPUs remains high in China, fueling a booming market for smuggled hardware.

On October 21, during GTC Berlin, Nvidia announced several massive, multi-billion-dollar deals with supply chain partners across substrates, advanced packaging, semiconductor testing, and optical engines to expand production capacity. Aimed at overcoming severe bottlenecks restricting the current and future AI infrastructure buildouts, the deals feature commitments and production roadmaps stretching through 2028, securing capacity for Nvidia and ensuring resiliency and redundancy in its AI accelerator supply chain. “We are in the midst of the largest infrastructure buildout in human history. Demand is extraordinary. Capacity is the constraint,” said Nvidia CEO Jensen Huang. “Our supply chain partners are the world’s best manufacturers. I’m grateful they are expanding capacity around the globe to scale production and help us deliver the AI supercomputing systems powering this new industrial revolution.”

The deals cover two major AI accelerator supply chain categories: hardware interconnects and OSAT (Outsourced Semiconductor Assembly and Test). Under hardware interconnects, Nvidia is securing capacity for both substrates and co-packaged optics. Nvidia has signed deals with Ibiden, Unimicron, and Samsung Electro-Mechanics — three of the world's five largest substrate manufacturers — to support its production capacity expansion and reserve significant portions of that capacity for the chipmaker. The company says it plans to secure 2.5x dedicated substrate capacity over the next three years. Nvidia has also signed agreements with SPIL (Siliconware Precision Industries Co., Ltd) to expand manufacturing capacity for the optical engines used in co-packaged optics by 15x by mid-2027.

On the OSAT side, Nvidia and Amkor are targeting a 300% increase in interposer wafer capacity by the end of 2028 through expanded advanced packaging capacity in South Korea, to support higher production volumes for Nvidia's platform. The company also plans to double its semiconductor testing capacity by 2027, investing $1.1 billion to reserve capacity from KYEC (King Yuan Electronics Co., Ltd.), the world's largest semiconductor testing company, which currently handles nearly all of Nvidia's chip testing.

Nvidia currently leads the AI chip market by a considerable margin, with its extremely powerful accelerators sitting in a vast majority of AI data centers globally. These deals target major bottlenecks in the AI chip supply chain, suggesting Nvidia doesn't plan to relinquish its position soon. So, why these specific deals? What are the exact details? What's the broader play? And where do Nvidia's competitors and the rest of the AI industry stand?

Uncorking bottlenecks

The substrate base, a specialized intermediary layer in every accelerator package that connects the tiny silicon chips above it to the board below, is one of the most critical bottlenecks in the AI chip supply chain. As we detailed in our comprehensive substrates roadmap, this indispensable physical layer, manufactured by only a handful of companies, is expanding in both the X-Y and Z axes as chip makers package an increasing number of components onto one board. Nvidia's Blackwell generation places two reticle-sized GPU dies and eight HBM3E stacks on a single package. Its Rubin and Rubin Ultra architectures mount even more components. As a result, each substrate now consumes much more material and production capacity, even as demand for individual substrates soars.

Fubon Research estimates that Nvidia would supply around 12.4 million data-center AI GPUs in 2028, up from a projected 8.2 million in 2026. Each one would feature a substrate layer. Nvidia is staying ahead of the curve, with a planned 2.5x capacity expansion over the next three years, led by Ibiden. Earlier this year, the company signed a deal to provide a multibillion-dollar capacity reservation advance to support Ibiden's Ono Plant in Japan, targeting 3x the plant’s current individual capacity.

To reach its 2.5x target, Nvidia has also brought on Samsung Electro-Mechanics, Samsung's substrate-manufacturing subsidiary, as a supplier. Beyond the immediate capacity from Samsung Electro-Mechanics’ existing operations, the deal will support facility upgrades and new plants in Busan and Sejong. Nvidia is projected to account for a significant portion of the substrate manufacturer’s output by 2028. To build even more redundancy, Nvidia also signed a capacity reservation agreement with Unimicron, supporting the latter's substrate manufacturing expansion in Taiwan. The expansion will reach full capacity by mid-2028 but is expected to start providing capacity by Q2 2027.

Next on Nvidia's to-expand-capacity list announced on October 21 is co-packaged optics. As next-generation speeds surpass 224 Gbps per lane, traditional copper traces on the board consume considerable power to move data even a few inches from the processor to the edge of the board. To address this, the industry is moving the optical engine into the accelerator package itself via co-packaged optics. Nvidia had long detailed its plans for silicon photonics and co-packaged optics as the standard for future AI accelerator packages. The company has signed a deal with Taiwanese optical engine manufacturer SPIL to expand production capacity by 1,400% by mid-2027, with plans to double again by the end of 2028.

A 4x increase in packaging, double the testing, and $1.1 billion specifically for U.S. facilities

Interestingly, the world's biggest semiconductor company, Nvidia, doesn't own a single Fab. After TSMC manufactures the logic chip and binds it with memory chips on a silicon interposer, final-assembly companies such as Amkor package that integrated module with the substrate and protective casing into the final accelerator package. These final steps are increasingly incorporating co-packaged photonics chips. Nvidia’s deal with Amkor aims to expand the latter's interposer wafer capacity by more than 300% by the end of 2028, through advanced packaging capacity expansions in South Korea. Amkor confirmed the deal is worth $1.3 billion, bringing Nvidia's total investment in Amkor across its Arizona and South Korea facilities to $3 billion.

After final assembly, finished Nvidia AI accelerator packages are shipped to KYEC for extreme testing. KYEC currently handles nearly all of Nvidia's chip testing. It therefore comes as no surprise that Nvidia expects semiconductor testing capacity to double from calendar 2026 to 2027 through buildouts across KYEC’s facilities in the U.S., Taiwan, and Singapore. On October 13, Nvidia signed a deal to pay KYEC $1.1 billion to reserve testing capacity at KYEC's new $1.4 billion U.S. facility, where high-volume production is slated to begin in April 2027. The funds will build domestic testing capacity and bolster the domestic supply chain. At the same time, KYEC is aggressively expanding operations in Singapore, where it expects high-volume production to begin in February 2027.

The bigger picture

Nvidia has been aggressively securing production capacity and long-term supply commitments to meet anticipated demand for its next-generation AI accelerators. According to its August 2026 financial disclosures, the company had accumulated $279 billion in outstanding supply and manufacturing-capacity commitments by July 2026. The company has since signed several additional deals. These commitments cover future component purchases and manufacturing capacity reservations, particularly for memory and chip production. Combined with its more recent agreements, Nvidia is securing nearly every stage of its AI accelerator supply chain, from semiconductor fabrication and HBM memory to substrates, advanced packaging, optical interconnects, final assembly, and testing.

Of course, the rest of the industry isn't standing by. On May 21, 2026, AMD announced over $10 billion in investments across Taiwan's semiconductor ecosystem, specifically targeting advanced packaging and manufacturing partnerships. On October 6, CEO Lisa Su confirmed that AMD intends to increase those investments further. The company is working with TSMC, packaging partners, substrate manufacturers, and memory suppliers to increase production substantially in 2027. Google and Amazon are also aggressively expanding their custom TPU and Trainium accelerator programs.

Intel, meanwhile, is positioning its foundry and advanced packaging operations as alternatives to TSMC's increasingly constrained production capacity. The company is expanding its EMIB-T packaging technology, with reports indicating that Google has already reserved Intel capacity to package more than three million TPUs in 2028. Intel is also advancing its 18A and 14A manufacturing processes as it attempts to attract major external customers.

Samsung is expanding its semiconductor operations as well. The Korean company, which held approximately 5.9% of the global foundry market by revenue in Q2 2026, is ramping up its 2nm manufacturing technology and preparing its Taylor, Texas, facility for volume production in 2027. As detailed in our comprehensive Samsung foundry roadmap, the company is investing heavily in its manufacturing operations as it attempts to close the considerable gap with TSMC.

Perhaps the most ambitious player is new entrant Elon Musk's Terafab, a joint semiconductor manufacturing project involving Tesla, SpaceX, and xAI. The project aims to eventually manufacture the equivalent of one terawatt of AI computing capacity annually, an extraordinarily ambitious target given the industry's current scale. Intel joined the project in April, and Musk later confirmed plans to use the company's 14A fabrication technology. The project aims to deliver its first experimental chips in 2027 and begin high-volume production by the third or fourth quarter of 2028.

Nvidia and other semiconductor firms are continuously fighting against emerging bottlenecks, but compute doesn't come online overnight, and fabs aren't built in a day. So, while almost every inch of the semiconductor supply chain remains constrained, it remains likely that big-name players will continue to invest heavily to ensure that products continue to flow into neoclouds and data centers alike.

Cadence, one of the largest electronic design automation (EDA) companies, has unveiled Maestro, its new portfolio of domain-specialized AI models tailored for its chip design agents, in a press release sent just ahead of earnings. In initial results compared with general-purpose AI models, Cadence claims up to a 30% improvement in the quality of results and that Maestro effectively uses fewer tokens for the same task, with up to 5x the token efficiency. The first models “are available to select early access customers” in Q4 2026, and customers may run them on Cadence’s cloud, public clouds, or customer-controlled on-premises environments and post-train them on their own “proprietary data, designs, and workflows.” To learn more about Maestro, we spoke with two Cadence executives: Rob Knoth, senior group director of strategy and new ventures, and Paul Cunningham, senior vice president and general manager.

This approach is distinct from Synopsys’ own specialized model, GPT-Synopsys, which will run on OpenAI-hosted infrastructure, without using customer data to train the model. The models of both EDA giants, announced less than a month apart, now split on where a customer’s design data goes. Where they might not differ is why: Paul Cunningham, senior vice president and general manager at Cadence, told Tom’s Hardware Premium that agent costs have “really started to explode,” adding, “And I think the world realized so.” That makes it a natural step, he said, to move more of the agents’ iterating “into the weights of the models themselves.”

Maestro takes this step through Cadence’s AI Super Agents, which “interpret design intent, decompose tasks” and “orchestrate the Maestro portfolio with other leading frontier and open-weight models,” evaluating trade-offs to guide decisions with domain expertise. It also uses reinforcement learning (RL), where Cadence’s “deterministic solvers evaluate outputs against engineering objectives, constraints and ground truth,” and, importantly, the results feed back into the models for improvement.

The models include Nvidia’s Nemotron, and customers can run the customer-data training step themselves with Cadence’s fine-tuning harness, potentially “on their own GPUs, on their own network,” Cunningham said. He added that Nemotron, specifically, was “giving really solid results and actually improving very, very fast.”

One constant that keeps popping up with agentic EDA is the need to nail down what the numbers actually measure. In chip design, quality of results usually refers to the design’s power, performance, and area, though Cadence didn’t specify what its 30% figure measures. Both it and the 5x token efficiency figure are measured against “a general-purpose foundation model,” which Cadence told us is the open-source model Maestro started from before its training. The company wouldn’t name the foundational AI model that Maestro is derived from beyond mentioning that it is “one of the leading open source models that’s very competitive with commercial frontier models.”

Cadence credits the token savings to balancing its EDA and system design automation (SDA) “tool calls with LLM invocations.” As an analogy, Cunningham said, “You don’t need to drive to Safeway in the jet engine,” and you want to use “the right model at the right time for the right task.”

Runtime does matter, too: as Rob Knoth, senior group director of strategy and new ventures at Cadence, told Tom’s Hardware Premium in September that a tool call could take anywhere from hours to days to return a value, while the AI industry’s training loops are “used to minutes.” Efficiency could also lower costs, since Maestro use counts toward Cadence’s agent pricing, a subscription with a consumption allowance and charges for use beyond it, Cunningham said. He didn’t say whether the token savings cut a customer’s bill.

Being flexible for the customer is a key element of any solution, and for Cadence’s competitor Synopsys, this lies in Autopilot, which Synopsys says allows customers to “choose among commercial, open-source, and fine-tuned language models” across “Synopsys-managed and customer-managed deployments,” with cloud or on-premises infrastructure. For agent security, such as sandboxing, Cadence says it’s working with Nvidia on an integration with Nvidia’s OpenShell Reference Architecture, which Nvidia launched last week, naming both Cadence and Synopsys. These options can help protect customer intellectual property.

Cadence has had a busy 12 months with customers and agents alike. During Cadence’s July earnings call, CEO Anirudh Devgan said ChipStack, its design and verification agent, “has more than 20 customer engagements,” and ViraStack, its custom and analog agent, “more than 25 customer engagements.” Socionext said just last week that it was applying InnoStack. Silicon Labs has also credited ChipStack with accelerating debugging, and both companies say they are watching Maestro next.

Cadence's agentic consolidation began with its acquisition of ChipStack back in November 2025. In June 2026, the company claimed that its agent had reached Level 5 autonomy, which we explored in detail. In September, Knoth teased Maestro’s ingredients to Tom's Hardware Premium a month early, two days before the GPT-Synopsys announcement: “reinforcement learning loops, domain-specific post-training of open-weight models, model routing.” Maestro is now sitting under all four super agents, targeting a similar year-end window to Synopsys’ Autopilot; the table below has the full timeline.

This rapid pace of progress means that a clear distinction has to be made between early access and general availability. Both Cadence and Synopsys can point to customer engagements, and, at least in the latter’s case for the AgentEngineer portfolio and Autopilot platform, general availability remains a goal by year’s end; Cadence hasn’t given a general availability date for Maestro. For GPT-Synopsys, though, only “early technology engagements are underway,” according to its partners. The platforms and agents remain works in progress.

For Maestro, Cunningham hopes for “more case studies, more quantifiable benefits” through Q4. The real story, as he aptly put it, is that “we’re going to bring agents and agentic AI natively into our vertical, not that our vertical is going to get embedded into general-purpose agents.” Only customer results on real workflows will test Cadence’s 30% number, but going by what both companies have shown so far, their own early figures are extremely promising and exciting for the EDA industry.

Modders have discovered a way to remove imposed half-speed AI performance limits on Nvidia RTX 20, 30, and 40-series GPUs with a firmware exploit, VideoCardz reports. Without the limitation, the RTX 4090 can achieve double its peak tensor rate, and matrix math can improve by up to 80% for the RTX 3090 Ti, with gains on other cards varying. Real workloads gain significantly less than the RTX 4090’s doubled peak would imply, and chatbot token speeds remain about the same, as that performance is dependent on memory bandwidth.

For all impacted GeForce generations, Nvidia imposed a limit that, per its own published specs, runs FP32-accumulate math at half each card’s FP16-accumulate rate. That does not apply to its Turing and Ampere pro cards. The unlock returns native matrix-math throughput to owners of the cards, which can speed up prompt processing and image generation.

Three separate projects offer generation unlocks, each with its own unofficial Linux module, while experimental Windows builds pursue a different approach. Card coverage varies by generation, with gpu-unlock handling Ampere and listing the GA102/GA103/GA104/GA106 series IDs. An Ada fork, gpu-unlock-ada, covers the RTX 4090 and, experimentally, the 4090 D, 4070 Ti Super, and the 4060 Ti 16GB. The turing-unlock project, adapted from gpu-unlock, works with the RTX 2080 Ti. See Table 1 for a breakdown.

How much faster the cards get

The peak doubling is for tensor rates, with the Ada fork developer’s RTX 4090 moving from 165.6 to 331.6 TFLOPS on FP16 and 165.5 to 330.6 TFLOPS on BF16. The gpu-unlock-ada README states that, in one independent owner’s matrix-multiply benchmark, cuBLAS GEMM medians only saw improvements of 12–34% and up to 20%, respectively. A separate data point, for an RTX 4070 Ti on Windows, saw larger matrix-multiply gains of +80.1% and +76.4%, the difference possibly caused by power limits.

In the matrix-multiply benchmark, a tested RTX 3090 Ti scored +35% for FP16, +80% for BF16, and +94% for TF32, based on a user’s test with a modified Nvidia driver. The gpu-unlock README lists BF16 matrix-multiply gains from user reports of +25–67% for the RTX 3090, +70–73% for the 3080 10GB, +56–59% for a memory-modded 20GB version, +73–81% for the 3070, and around +80% for the 3060 12GB, though it notes they are not directly comparable. The power limit “matters a lot”: the lowest figure, +25%, came from cards capped at 225 W. For Ampere, LLM prefill improved 14–36%, with diffusion and FlashAttention speeding up, but LLM decode and INT8 stayed “about the same.”

An RTX 2080 Ti with a 22GB memory mod, a Dell OEM card limited to 250 W, saw FP16 matrix-multiply improvements of +36% to +64% depending on size; BF16 is unaffected, since Turing has no BF16 hardware. A user’s variant of the card at 260 W saw a +59% improvement. A separate project, KohakUnlock, on a Leadtek 2080 Ti 22GB saw an 85% improvement on an instruction rate test, with a best matrix-multiply median of +53% against a second, locked card of the same model.

There is “not a claim of a 2× application/game speedup,” says KohakUnlock’s README. See Table 2 for a rundown of the numbers for all three generations. The turing-unlock README notes that the mod “may damage your hardware,” while KohakUnlock adds that it “can hang a GPU or host, prevent boot, corrupt data, or damage hardware,” so caution is advised.

How the exploit works

The explainer for gpu-unlock states that the rate limits are in two “speed selector” registers that the host driver can’t write, but Nvidia’s signed SEC2 “Booter” firmware has a stack buffer overflow which can be exploited to run code in “heavy secure” mode. This was discovered after Nvidia published a debug Booter encrypted with a known public test key. A single 4-byte write with the V67 payload opens the privilege mask so the host can set the full rate, and the module then resets the card before Nvidia’s driver binds it.

The underlying bug is described in Jon Pry’s “A Canary in the Crypto Mine: Defeating Stack Protection in a GPU Secure Coprocessor,” published on Zenodo in June. The stack overflow was found on the CMP 170HX in the Heavy Secure Falcon, and the V67 constants come from WildFlash1st/cmp90hx-unlock. The pre-driver timing gpu-unlock uses appear to come from WildFlash1st’s separate Windows EFI bootloader, cmp90hx-unlock-for-windows, while the best-known use of the exploit is the CMP Unlocker for the CMP 170HX. Zagitta, the maintainer of gpu-unlock, said on Hacker News that the project is their work but “the exploits … aren’t my ideas by any means” and that the unlock “just flips the 2 bits” needed.

Zagitta said that RTX 40 support is in progress and that forks are already unlocking those cards. Zagitta added that Hopper and Blackwell cards are “out of scope for this approach” at least.

The Ada fork says other boards “remain unverified” and that shared firmware “does not establish support for untested cards.” The turing-unlock project, likewise, has a list of untested Turing IDs. Gaps remain for GA103 and most Ada and Turing cards. None of the projects contains a roadmap, but data is being gathered, as gpu-unlock is asking GPU owners for compatibility reports.

Nvidia CEO Jensen Huang has put the annual cost of licensing the company’s AI software at around $2,000 per GPU to 'run all the software that Nvidia provides that runs agents on these AI factories,' according to a recent video of the comments (below), a pricing model that could generate $50 billion over five years under a proposed scenario involving five million paid licenses outlined by FourWeekMBA.

Huang discussed the pricing at GTC Berlin on October 22, where the company outlined its broader push to build an AI infrastructure business spanning chips, software and AI-agent security. “Software licensing will become increasingly a major part of our company’s business. The way that we license will be…for example, it’s about $2,000 per GPU per year to run all the software that Nvidia provides that runs agents on these AI factories,” said Huang.

The numbers add up quickly. At $2,000 per GPU each year, Nvidia would bring in $10 billion annually if five million GPUs were covered by paid licenses. Over five years, that would amount to $50 billion, provided customers kept renewing their subscriptions. The catch, however, is that Nvidia has not disclosed how many GPUs could be covered by the proposed $2,000 fee, or whether that’s its standard price. It’s also unclear which software the license would cover, or if there is overlap between products.

The distinction matters because Nvidia already charges for enterprise software. Its AI Enterprise platform is listed at $4,500 per GPU for a one-year subscription. Huang’s reported $2,000 figure is lower, but Nvidia has not confirmed that the two prices cover the same products or licensing terms. It should not be treated as a confirmed price cut.

The licensing discussion comes as Nvidia develops more of the infrastructure needed to deploy and manage AI agents in enterprise environments. These systems can use tools, access data, and execute tasks, creating new security and management challenges for businesses running them at scale.

On October 21, Nvidia announced its OpenShell Reference Architecture, a system that combines its Vera Rubin computing infrastructure, Vera CPUs, and BlueField-4 data processing units (DPUs) running Nvidia Sentry. OpenShell provides a secure runtime for AI agents, establishing boundaries around their actions and access to tools and resources. Sentry monitors agent activity independently and can quarantine an agent that attempts to cross those boundaries, according to Nvidia. The architecture is designed to bring security controls into the underlying infrastructure rather than relying solely on safeguards built into an AI model or application.

Nvidia had previously introduced its Open Agent Safety Platform in September. The newer reference architecture builds on that effort, extending the approach to enterprise infrastructure and edge systems.

Other companies are also incorporating these controls into their products. Anthropic is integrating OpenShell and BlueField hardware with Claude Managed Agents to give businesses additional control over what their AI agents can access inside execution sandboxes. Salesforce has developed a Slack Admin Bridge for OpenShell that lets administrators review agent access requests, approve or reject permissions, and monitor activity directly in Slack.

The proposed software pricing fits Huang’s wider argument that Nvidia is becoming more than a supplier of GPUs. The company has expanded into networking, systems, enterprise software, and infrastructure designed to run AI workloads. Speaking on the BG2Pod podcast recently, Huang said, “One of these days, people might not categorize us as a chip company anymore.” He pointed to Nvidia’s presence across industries including healthcare, automotive, telecommunications and life sciences, where the company provides broader technology stacks rather than chips alone.

That said, Nvidia has not said when the $2,000-per-GPU fee would take effect or which software it would cover. It also remains unclear how the proposed pricing would fit into the company’s existing enterprise software business.

If you are wondering why the GeForce RTX 5090, despite being one of the best graphics cards, has evaporated from retail shelves, the answer lies in China’s industrial conversion factories. A newly surfaced video reveals the staggering scale of the country’s grey-market AI operations, showing industrial workshops dismantling brand-new GeForce RTX 5090 graphics cards to repurpose them for the AI boom.

So, what exactly is a GeForce RTX 5090 doing in China when the graphics card is not even officially available there? The answer is simple. They are there to be transformed into AI workhorses. The recently leaked footage offers a glimpse inside these Chinese workshops, where skilled technicians meticulously desolder the pricey flagship’s GB202 silicon and GDDR7 memory chips. They then transplant the components into a dual-slot blower design tailored for server use.

The GeForce RTX 5090 blower graphics cards will ultimately end up in high-density server racks where they will become the backbone of massive AI compute clusters. The scale and efficiency of these operations are impressive, with production lines converting shipments of brand-new GeForce RTX 5090 graphics cards, from Nvidia's custom partner models to the chipmaker's highly coveted Founders Edition. After testing and validation, workers would repackage these modified GeForce RTX 5090 graphics cards and ship them to clients, likely data centers or AI labs.

Many of these Chinese factories are scattered throughout the country. The conversion process is massive in scale, and probably thousands of GeForce RTX 5090 graphics cards are being repurposed every day. Middlemen in these grey-market supply chains are constantly hunting for GeForce RTX 5090 stock to fuel these operations.

Nvidia has reportedly halted production of the GeForce RTX 5090 and is, in all likelihood, redirecting the valuable silicon to higher-margin professional GPUs like the RTX Pro 6000 Blackwell. At the same time, a vast network of Chinese conversion factories continues to operate at full speed. Redirected supply and aggressive grey-market recycling have nearly wiped out GeForce RTX 5090 stock from U.S. retailers.

Custom GeForce RTX 5090 graphics cards now start at an eye-watering $8,500. However, a new workaround has emerged to sidestep the stock shortage and inflated prices: prebuilt gaming systems. Sometimes these GeForce RTX 5090 prebuilts cost less than a standalone GeForce RTX 5090, while offering a fully assembled system with a manufacturer's warranty.

If you had told me a few months ago that buying an entire gaming system would be cheaper than a single high-end graphics card, I would have called you insane. Yet here we are. In today’s chaotic market, prebuilt systems often offer better value than standalone graphics cards.

Lava, an Israeli security firm, says that it has been able to identify 32 Nvidia Blackwell Ultra B300 chips within the network of China Telecom, a state-owned company that provides telecommunications services within the country. These same chips have already been barred from export by U.S. export controls, a report by The Information notes, which begs the question of how they have turned up in a Chinese data center. Such chips can only be sold to whitelisted buyers who agree not to export them to sanctioned parties, including those in China.

However, banning companies from buying Nvidia's high-performance GPUs and AI data center chips hasn't always worked. In 2025, it was reported that Chinese companies smuggled $1 billion worth of Nvidia AI chips into the country in three months. At the time, the B200 chips were the most sought-after, but some distributors were already gearing up to sell B300-series chips when they became available.

While the latest finding shows that the proliferation of export-controlled chips into China continues to be an issue, Nvidia has downplayed the report but didn't deny the findings. "Governments, researchers, and media have all found insignificant diversion of controlled US products, less than one half of one percent in recent court cases — a drop in the bucket compared to the ocean of domestic compute China already has," an Nvidia spokesperson told Tom's Hardware. "With tens of millions of GPUs in service in approved countries, this report again demonstrates that smuggling is a nonfactor, blocked by governments on both sides of the globe."

It's unknown how B300 chips found their way into China Telecom's server racks, nor can Lava confirm exactly where within the company's network they are located. But they are there, with Lava identifying them via Nvidia's own DCGM Exporter tool. The software can be installed by data center operators and is used to monitor server health and performance, and while designed for internal use, it can oftentimes be accessed beyond the data center's network. Oddly, it doesn't always require password authentication, either. Nvidia previously confirmed it was working on a tool that would allow its chips to be located, but it is yet to be released.

It's easy to see why Chinese firms are keen to get their hands on the Blackwell Ultra B300. Announced in March of last year, the data center GPU is one of the most powerful on the planet, offering huge performance gains with 50% more memory and FP4 compute than the previous-generation B200. At the time, Nvidia touted that the chip was "built for the age of reasoning," while name-checking more sophisticated AI LLMs like DeepSeek R1. At the time of its announcement, the B300 was expected to ship by the end of 2025, and it didn't take long for it to become hot property. In August 2026, nine people were indicted in Taiwan over the illegal export of B300 GPUs to China. This came just days after Supermicro confirmed that it had fired several employees following an investigation into smuggling. The complicated smuggling process included the use of a Taiwanese server trading firm called Flying Tiget Tech — a firm with whitelisted status for GPU purchases.

Nvidia's chips have proven so vital to AI data centers that demand for even its older silicon has caused skyrocketing prices. The company's A100 servers have tripled in price, despite being five years old, and can cost up to $82.,000. The ongoing crackdown on Nvidia chip smuggling has even seen some resort to retooling gaming GPUs to run AI workloads where possible.

Following reports over the weekend that an Nvidia RTX 5090 shipment worth $470,000 was stolen from a Brazilian airport, a contraband graphics card linked to the shipment has shown up for sale online. Brazilian media also reports that the police and airport authorities have identified three employees thought to have been involved in the heist, according to GMC. (Machine translated)

According to the report, Guarulhos International Airport has issued a statement confirming that "The investigation of the facts is the responsibility of the competent authorities to clarify the circumstances of what happened." A Brazilian TV news network appears to have identified one of the cards, a Zotac Gaming RTX 5090 for sale on the online marketplace OLX for over $6,000. Quite unfortunately, the reseller uploaded clear pictures of the card's serial number, linking it to the missing haul of graphics cards. Per the report, there is no information to identify the seller, and the listing appears to have since been taken down.

However, police information reported by the outlet does reveal a chain of events and some possible suspects in the case. The shipment, imported from the United States and thought to be worth nearly $500,000, arrived in Brazil on September 20 and was registered with customs on September 24. The two pallets went missing after being authorized for release on September 25. Police believe "two outsourced forklift operators" removed the pallets from the warehouse on September 21, an act apparently recorded by CCTV.

Police state the equipment was loaded onto a truck and hasn't been seen since. A third "checker" employee is said to have released the merchandise from the system to pass through electronic controls.

The RTX 5090 is the world's best consumer gaming GPU, but high demand for AI compute has seen it all but disappear from conventional retail, with the card now fetching upwards of $10,000 in the few remaining marketplaces it can be found, nearly always offered by third-party sellers. As such, an idle, unguarded shipment of such cards would make ripe pickings for thieves, a tempt

Protesters disrupted Jensen Huang's Nvidia GTC keynote in Berlin on Wednesday morning. Just minutes into the CEO's customary address, the broadcast was interrupted by a heckler from the crowd demanding the CEO answer claims that the company's chips were found inside Russian drones, and charges of environmental destruction more broadly. While the incident wasn't fully picked up by Nvidia's livestream, footage filmed by attendees shows protestors standing on chairs with a large banner that reads "Nvidia, clean up your dirty business!"

Multiple videos and images shared to X show protestors brandishing a large banner with a Greenpeace logo. "Jensen, what about the technology that was found in Russian drones? Those were Nvidia chips. What are you doing about that?" one of the protestors called out across the auditorium. "What are you doing about that? What are you doing about the environmental destruction?" the heckler demanded.

Huang can be heard on the broadcast and in the video stating, "It's okay, go easy," as security rushed to escort the protesters out of the hall. "I'll talk to you later," Huang stated as the individuals were removed from the premises.

The complaint raised by the protester appears to relate to recent reports that Nvidia Jetson chips have been found in Russian cruise missiles used against Ukraine, as well as in AI drones. Reports indicate Russia's S-71 'Monochrome' weapon has autonomous targeting capability powered by Nvidia's consumer-grade Jetson Orin chip. "Our Jetson Orin modules are consumer-grade products sold to students, developers, and startups for a wide range of beneficial applications," Nvidia told Tom's Hardware at the time. The company reiterated that the chips are not available in Russia and are not designed for military purposes, but admitted it cannot track the resale of pre-owned Jetson chips after they are sold.

A report in August claimed that a Russian AI drone killed three civilians in Ukraine, the first documented case of civilian deaths caused by a Russian drone using fully autonomous targeting. Images of the recovered drones confirmed they contained Jetson Orin modules. "All important issues, but let me get through my keynote," Huang quipped before continuing his presentation.

Huang is in Berlin for Nvidia's three-day GTC event, taking place from October 20-22. The customary keynote, given this morning, also includes fireside chats and talks for developers and business leaders.

Chinese technology giants ByteDance, Alibaba and Tencent have reportedly spent over $35 billion on orders for more than 3 million Nvidia RTX Pro 5500 workstation GPUs. The huge order for the RTX Pro 5500, fully compliant with U.S. restrictions on exports of AI-capable GPUs to China, is reportedly far beyond Nvidia's planned production for this card. As a result, Nvidia has, according to sources who spoke to Reuters, told its customers that it isn't in a position to fill the orders as it far outstrips its planned production for this GPU.

The RTX Pro 5500 is a workstation GPU with the same number of CUDA cores as its flagship RTX 5090 GPU, but with 84GB of GDDR7 memory attached, making it a good option for AI applications. The RTX Pro 5500 is a popular choice at the moment for Chinese buyers as exporting it isn't currently restricted by the U.S. government. It's less powerful than the H200 and B300, Nvidia's AI-focused GPUs for the enterprise market, but those GPUs remain either banned or heavily restricted. As China competes with the U.S. in the race to produce newer, better AI models, GPUs like the RTX Pro 5500 continue to be indispensable.

The report reveals some interesting data about the Chinese tech firms orders. Two of its sources revealed that the cards are priced at around $11,400 each, reaching a total order value of $35 billion. Meanwhile, another source suggested that Nvidia has informed its partners that it may only be able to deliver enough cards to meet between 5% and 10% of the demand. Meanwhile, Nvidia is reportedly in a position to only produce 3 million RTX Pro 5500 cards globally in 2027.

Neither Nvidia nor the three Chinese companies have confirmed any of this on the record. However, an Nvidia spokesman told Reuters that demand from the Chinese market for these chips has faced "unprecedented growth since 2022" since export restrictions were introduced. "U.S. firms continue to be restricted by a combination of outdated U.S. export controls, which cover gaming products released nearly half a decade ago, and China's own limits on U.S. imports."

While trade hostility between the U.S. and China shows no sign of abating, demand for Nvidia's GPUs hasn't slowed down, despite U.S. attempts to restrict Nvidia's ability to export to China. Meanwhile, export-restricted Nvidia AI chips continue to reach China despite the restrictions, bypassing the Trump administration's efforts in the process.

Popular graphics post-processing injector software ReShade, commonly used to mod DLSS 5 into unsupported games, now has a foveated imaging add-on, including another standalone project that doesn't need precursor software, allowing DLSS 5 to affect only what's in view, reducing GPU load and increasing FPS.

Foveated imaging is nothing new; it's a technique that's been used in VR to increase performance. It works by only rendering what's in view, often increasing the resolution in the center and reducing it at the edges. Our eyes work in a similar way, with the clearest image in a small area in the center of your vision, blurring on the edges. With foveated rendering, you barely notice the lower-resolution edges as the peripheral vision is less concerned with fine details.

Instead of varying the resolution of the image, however, it varies the level of detail in DLSS 5 Neural Rendering. DLSS NR loads a whole AI model into your GPU and renders images in real time, making it a resource-intensive task; but the task is minimized greatly when most of the detail is only asked for in the middle of the image. That's what projects like maximedgoulet’s Foveated DLSS5 and ClarkCheekyKent’s CheekyFoveatedDLSS aim to do; with Foveated DLSS being a ReShade add-on and CheekyFoveatedDLSS capable of being a standalone integration.

Both implementations allow you to adjust the size of the center area with the highest rendering quality, with independent width and height adjustments to suit your personal situation, as some people have a larger focus area than others or have a different monitor distance. You can also adjust the render fall-off rate, so the image quality lessens more quickly or slowly past the focus area.

As Maximedgoulet's implementation is an add-on for an injector, the pipeline is quite long compared to the standalone integration of CheekyFoveatedDLSS, which Reddit comments claim is more convenient and more polished. Though both show a big improvement in performance, Maximed's Reddit post on Foveated DLSS5 claims an improvement from 48 to 80FPS in The Witcher 3 with 2x Multipass at ultrawide 7680x2160p on an RTX 4090. Considering Maximedgoulet’s 4090 could not handle DLSS-NR on an ultrawide, this implementation might genuinely be feasible to make Neural Rendering more playable.

Nvidia has earned billions selling AI accelerators, but with the emergence of agentic AI, CPUs are going to play a considerably more significant role than before, which creates new opportunities for the company. Reuters reports that Nvidia has secured a major design win with its Vera CPUs with AI cloud provider Modal. According to Modal, preliminary evaluations showed that Vera could deliver twice the throughput of its existing AMD and Intel processors, although the company does not intend to cease using CPUs from its current suppliers.

Modal provides cloud infrastructure that enables AI developers to deploy AI agents that can perform complex operations autonomously over extended periods. Such workloads require substantial CPU resources for software execution, data processing, and other tasks that cannot be efficiently handled by GPUs. Modal currently relies exclusively on AMD and Intel processors for these operations, but its tests indicate that Nvidia's new Vera CPUs could significantly improve performance in its specific workloads. Of course, since Vera is based on an Arm instruction set architecture, Modal's decision to adopt Vera will require substantial modifications to its software. Nevertheless, Modal believes that the potential performance improvements justify the additional engineering effort.

The reported 2x throughput advantage is particularly interesting because agentic AI workloads can involve numerous independent tasks that compete for CPU cores, cache capacity, and memory bandwidth. However, Modal has not disclosed detailed benchmark configurations it used, specific AMD and Intel processor models, or performance-per-watt figures. Consequently, it remains unclear whether Vera's advantage is enabled by advantages of its Arm v9.2-based Olympus CPU cores that can decode and execute up to 10 instructions per cycle, its memory subsystem with 1.2 TB/s of bandwidth, or other architectural characteristics. Also note that tests compared Vera against currently available AMD and Intel processors rather than their upcoming generations that will compete against Vera in the coming years.

Since Modal cannot port all of its software to Arm ISA overnight, the company will continue using x86 CPUs from AMD and Intel, according to Modal chief executive Erik Bernhardsson. Rather than sticking to one ISA for everything, Modal intends to operate a heterogeneous server fleet and select the most suitable processors for each workload. Naturally, the company intends to evaluate upcoming AMD and Intel processors once they become available to make its choices in the future.

For Nvidia, the agreement represents a major step toward making Vera a standalone server processor that commands a major share of the server market. The company expects Vera sales to generate at least $20 billion during its current fiscal year and estimates the potential total available market for these CPUs is approximately $200 billion. The company is on track to sell at least four million Vera CPUs in fiscal 2026.

The announcement comes shortly after Modal secured $750 million in financing led by Accel, which valued the company at $15.75 billion. Modal expects its annual revenue to reach $1 billion within the next year. The company's service consumption has increased 3X in the last 12 months, while revenue has surged more than 15 times compared with the previous year, Reuters reports.

Panchovix on GitHub recently released a VBIOS configurator for RTX 30-to 50-series cards that doesn’t require flashing. The loader provides a UI for easy configuration and lowers the risk of bricking your GPU by avoiding a risky flash process, and it's available for Linux, too.

Software like MSI Afterburner or ASUS GPU Tweak is often the go-to tool for quickly changing your graphics card’s settings; however, those tools have hard limits to how much you can push the graphics card. If you want to tweak your GPU beyond the firmware’s set limits, you can change it by tweaking the parameters in the VBIOS. Configuring VBIOS is similar to changing BIOS settings in the BOOT menu. With the VBIOS loader, you can change settings beyond the firmware’s hard limits.

The way it works is by storing the modified VBIOS image in the GPU’s register key; as the Nvidia driver looks for a VBIOS override on startup, it finds the VBIOS image and restarts, applying the override settings. The flash chip on the graphics card is not overwritten, reducing the risk of bricking your GPU. However, this does not protect the card from any extreme settings that could destroy electrical components; it merely provides a safer and more convenient way to adjust and apply VBIOS settings, including a way to roll back to the default VBIOS settings should you face any instability.

Bricking a flash chip isn’t as catastrophic as it seems, however. As long as you have a backed-up version of the VBIOS, you can easily buy an SPI flash chip programmer or even build your own using a cheap Raspberry Pi Pico and its many clones; sometimes programming the flash chip may not even require desoldering. Programming a flash chip is very tedious and inconvenient, though, which is why avoiding the risk of a botched flash process altogether is so appealing.

Unless you’re familiar with power delivery, voltage tables, and the patience required to test for hours, we wouldn’t recommend jumping into this unless you’re prepared to risk your GPU. However, you can still safely take advantage of this software by tweaking the fan curves and other settings you’ve applied and tested in software like MSI Afterburner if, for some reason, the startup settings don’t stick. This software will give your GPU new default settings that override at every startup, so long as Nvidia doesn’t remove the registry key that allows the VBIOS override.

The DLSS-NR project on AMD by Daniel Blanco was first released on September 3, 2026, bringing DLSS 5 Neural Rendering to Radeon GPUs; in the latest 0.8.0 update, however, it makes all the main DLSS features available, featuring DLSS SR, Frame Generation, Ray Regeneration, and the newest DLSS NR model, along with more performance improvements.

While AMD has equivalents for most DLSS features like FSR upscaling, frame generation, ray regeneration, and anti-lag, it’s not as commonly supported by most games compared to Nvidia’s offerings. Even if players could use the AMD equivalent features, Neural Rendering is one that AMD just doesn’t have an equivalent for yet, which is why mods like DLSS-NR on AMD were made.

Daniel Blanco wanted to fill the gap with NR in its initial release, but in the latest update, he added all the DLSS features: DLSS Ray Reconstruction, Super Resolution 4.5, Frame Generation, and the latest 310.9.16 improved Neural Rendering build that we covered recently with modders using it on unsupported games. Interestingly, the update notes say it includes FSR 4.1.1 upscaling; AMD GPUs already use FSR, but having FSR 4.1.1 specifically gives the mod a consistent version of FSR to work with, instead of having to be compatible with the various implementations of FSR in different scenarios.

The mod currently supports three generations of Radeon GPUs: RX 9000, 7000, and 6000 (RDNA4, 3, and 2, respectively), in any DirectX 12 game that uses FSR, DLSS, or XeSS. You can install the mod by downloading the NeuralHub mod loader, which provides a UI for you to install it into the games it detects. You then enable FSR, DLSS, or XeSS in the game, and the mod will insert itself into that upscaler pipeline, allowing it to inject any of the settings you configured for the game in the NeuralHub; it even provides an overlay to tweak the DLSS settings without leaving the game.

With a few improvements from the first version, the developer says the output quality is very similar to what DLSS produces on Nvidia GPUs natively, except for “some effects that may not be working 100%.” In the developer’s own testing, he found it to be playable at 90-100FPS at 1080p on an RX 9070 XT, while saying that he’s making improvements every day, planning to match the performance of a 5070Ti.

Real-world results are mixed, however, as Reddit users report showing no performance loss over native FSR, while others experience losing single-digit FPS to up to 25% lower FPS in various games. The developer still has to account for many factors since there are a myriad of PC configurations, but the results look promising as he bridges the gap between AMD and Nvidia GPU features — though it won’t work with anti-cheat, unfortunately.

A Brazilian electronics and IT distributor, Oderço, has gone on Instagram after failing to find answers about a missing shipment of RTX 5090 GPUs worth around $470,000 or R$2,500,000. According to its Instagram post [machine translated], the company has never faced an experience like this in its 38-year history. It was expecting two pallets of Zotac RTX 5090 GPUs at Guarulhos International Airport in São Paulo, which supposedly arrived there but then disappeared. The company has not provided any details, like the shipment’s arrival date or any tracking numbers, but claims that there hasn’t been any official response regarding its shipment inquiries. Because of this, the company went on the social media platform pleading for answers.

RTX 5090s are currently in short supply across the world, with the top-tier card disappearing from online retailers in the middle of September. There have been rumors that Nvidia has ceased production of the GPU to focus on more expensive professional AI cards, although it insists otherwise. Still, it doesn’t help that the flagship GPU is marked “not carried” in Micro Center’s online retail store, making it insanely difficult to find one of the best gaming GPUs you can buy today.

We don’t have any idea how many GPUs are missing, as the distributor only mentioned two pallets’ worth of RTX 5090s. A Zotac Gaming GeForce RTX 5090, which goes for around $8,000 on Amazon presently, is listed at R$33,999.99 on a Brazilian online retailer Promotech. This means that the company is missing 63 GPUs at the minimum, although this is likely going to be so much more considering that the $R2,500,000 amount the company said is its acquisition cost, not the retail price that it’s expected to go for.

We’ve seen various scams where retail customers lose their GPU shipments on the way to their home or delivery address, as well as unscrupulous buyers stripping off the GPU core and memory modules from an RTX 5090 and then shipping it back to the seller. This also isn’t the first instance that a shipment of high-value graphics cards has been stolen — one brazen crew has even rammed security escorts to hijack an AI hardware shipment, while another group stole two trailers marked with the Nvidia logo, only to find that it contained sand.

A new version of DLSS 5 on NBA 2K27 was just released, and modders have already extracted it and are running it on unsupported games. Modders are comparing performance between the older and newer Neural Rendering (NR) model and found it up to 40% faster than the previous one.

While many are still experimenting with leaked and first-release builds, Renan Maniero has already extracted the new 310.9.16.0 DLSS NR build, letting other modders use it in unsupported games and compare performance. Gaming Optimized on X compared the new NR model to the old one and found massive improvements of up to 90% in The Witcher 3 running DLSS 5 natively.

Running a full NR model to modify your game's image is resource-intensive, just as LLMs require a lot of RAM; DLSS NR does, too. To improve performance, Nvidia made the AI model 24% smaller, using 20% less VRAM at 4K, and, according to Nvidia, processes its neural rendering workload up to 2x faster.

Typically, improving an AI model means increasing its parameters and size—in other words, giving it a bigger brain. However, because consumer GPUs have limited, non-upgradable VRAM, Nvidia can't improve by adding a bigger model; instead, the AI needs to become more efficient while reducing resource usage.

These comparisons were much easier than switching back and forth between updates, since the latest version still includes the older model as a preset. The new DLL has two Neural Rendering presets: Preset A is the old model, and Preset B is the newer model.

As with many other DLSS 5 experiments like those Valery Kryzhanovsky does, DLSS is injected into the graphics pipeline using RenoDX or OptiScaler, both of which let you tweak the DLL settings to get different results. To run DLSS NR on older RTX GPUs, the modding community also modified the runtime to support RTX 20-series GPUs, as the OptiScaler GitHub README notes.

Nvidia plans to release DLSS 5 to RTX 40-series GPUs, but there are no reports of it officially coming to RTX 30- or RTX 20-series cards; the modding community seems to have done it anyway.

Last week, gamers still holding any hope that the GPU market would somehow stabilize had their hopes dashed. A while after Nvidia reportedly put the RTX 50 Super series on hold, rumors started flying that production of the already-rare RTX 5090 was stopping, likely to use precious silicon manufacturing capacity for more AI accelerators. Now, the company's senior VP Jeff Fisher reportedly said that card was not discontinued and would continue to be shipped. The statement comes after a myriad of websites, including Tom's Hardware, have asked the company for official comment, but to no avail.

The information comes from Taiwanese websites including 4Gamers and SJTV, which both are quoting a purported post-event interview where Fisher offered a brief explanation of the situation. According to the VP, supplies of the GPU are limited, but Nvidia is seemingly going all out to get the product to market (paraphrased from the Taiwanese reports).

"The supply chain (capacity) is indeed somewhat constrained, but NVIDIA is doing everything it can to get the product on the market. Although there are other variants of the RTX 5090 (chip), production has not been discontinued, and NVIDIA will continue to ship, which is very important," Fisher reportedly said to 4Gamers.

He also reportedly reiterated a common quote from Nvidia execs, stating that without GeForce, Nvidia wouldn't be where it is today, and likewise, the AI market wouldn't exist as it does today.

The claim that the card isn't gone after all merits some spot-checking, so we did a cursory browse of the common retailers. At Amazon, RTX 5090s are selling for about $8,500, but from third-party sellers. Best Buy has a grand amount of naught cards on offer, while Newegg lists 5090s for about $7,300, but from what are likely Chinese sellers, judging by the shipping time. The ever-famous U.S. tariffs would likely add quite a chunk of change to that price tag, too. Finally and perhaps most poignantly, the card remains absent from Nvidia's own marketplace and Micro Center as of this writing.

Some would say that these statements are nothing but a means to keep the company in gamers' minds, even though gaming-related revenue is now a vanishingly small piece of Nvidia's ever-larger money pie.

Jensen's firm isn't the only one seemingly keeping up appearances, as Intel has repeatedly stated that its long-awaited refresh of consumer GPUs isn't dead, and last year AMD said it still intends to deliver "the best mainstream gaming GPUs on the market." Yet there are no new GPU launches from any of the three, and their quarterly launch figures keep doubling down on AI.

We reached out to Nvidia for comment on the situation and received no reply.

Recent news and rumours have been flooding the tech space about the apparent discontinuation of Nvidia's flagship graphics card. With no new Nvidia 5090 GPU replacement anticipated before at least 2028, this could well be your last opportunity to get your hands on a very powerful GPU for gaming.

The RTX 5090 not only dominates benchmarking charts in performance, but it also dominates price charts, with the cost of purchasing a standalone RTX 5090 rising from its MSRP of $1,999 to currently over $7,500. The cheapest way to previously capture an RTX 5090 in the wild has been from either Micro Centre (who apparently is soon to be no longer stocking them), or picking one up in a prebuilt gaming PC build.

● Check out this RTX 5090 deal at Dell

● RTX 5090 Prebuilt PCs available at Newegg

We've highlighted many RTX 5090 prebuilt PC's over the last couple of years, with models from Dell, HP, MSI, Skytech, and others. And there are still some PCs out there that can be bought for around the same price as buying the graphics card alone. But with the available pool of RTX 5090s soon starting to dry up, prices will start to climb even higher, much like what happened when the previous generation RTX 4090 ceased production.

The RTX 5090 is the most powerful consumer GPU that you can buy. It sports 21,760 CUDA cores, over double the RTX 5080's 10,752 count, and provides a massive 32GB of GDDR7 VRAM. Using a 512-bit memory bus, with a memory bandwidth of up to 1,792 GB/s, the RTX 5090 is the top choice for any gaming PC. When you look at our benchmarking results in our review of the RTX 5090, it's easy to see that this super-powerful GPU dominates the competition in all performance metrics.

Dell is selling this powerful PC with an AMD 9950X3D processor for $7,499.99, around the same price as buying the GPU separately, so, ignoring the insane price of the GPU for a second, that makes the rest of this PC build, including the monstrous 16-core gaming processor, 32GB of RAM, and 2TB SSD, free.

For an example of some of the RTX 5090 prebuilt PCs that you can still get your hands on, we have another discounted MSI Infinite ZS PC that's available from Best Buy, plus an example of a Skytech RTX 5090 gaming PC available at Newegg. I've also included a link to RTX 5090 gaming PCs that are in stock at Newegg, so you can get a further idea of pricing and what is available.

● RTX 5090 Prebuilt PCs available at Newegg

If you have the money and want the best GPU that said money can buy, then you need to think about pulling the trigger before it's too late and the GPU either disappears or the prices inevitably rise even higher.

Nvidia's RTX Spark is now generally available for immediate shipment at multiple retailers. Orders for RTX Spark from brands like Asus, Microsoft, Lenovo, HP, and Dell are live now, including the new Surface Laptop Ultra. Additional devices will be available post-launch, including small form factor desktops. Further below, you'll find the full list of all available models in detail, but we also have more general links here:

• Order RTX Spark devices at Best Buy
• Order RTX Spark devices at B&H
• Order Surface Laptop Ultra at Microsoft

Best Buy has a limited lineup of devices to start, ranging from the Asus ProArt P14 with 24GB of memory for $2,599 up to the Surface Laptop Ultra with 48GB of memory for $3,999. If you want to scale up the RTX Spark's maximum of 128GB of memory, you'll need to shop elsewhere.

Nvidia RTX Spark 128GB and 64GB laptop orders

Asus has the ProArt P16 available with either 64GB or 128GB of unified memory directly from its web store, starting at $4,499.99 for the 64GB configuration. If you upgrade to 128GB of unified memory, the laptop comes in at $6,999.99 with 2TB of storage.

HP has two models available directly in its web store, both of which support up to 128GB of unified memory (along with 64GB, 48GB, 32GB, and 24GB). Both models are significantly cheaper than the ProArt P16 with 128GB of unified memory. The OmniBook X comes in at $5,109, while the OmniBook Ultra runs $5,309, both with 1TB of storage.

The two models are very similar, differing primarily in screen size. The OmniBook X is 14 inches, and the OmniBook Ultra is 16 inches. Both come with a 3K OLED touch display with a 120Hz refresh rate, and they support up to 2TB of storage.

With both laptops, you'll get a smaller 5120-core GPU with the 32GB configuration. With all other configurations, you get the full 6,144-core GPU available on the RTX Spark.

Finally, the Surface Laptop Ultra is available up to 128GB directly from Microsoft. It's also available from B&H, though it appears configurations with more memory capacity are exclusive to first-party sources right now.

Nvidia RTX Spark Best Buy Orders

Best Buy has a broad range of RTX Spark laptops available for order, topping out at 64GB of unified memory. All of the devices through Best Buy are expected to ship on the October 16 release date. You can find all of the orders going on right below.

We've included the starting configuration for each machine, though note that Best Buy has other configurations of these models available for order as well. You can browse all of the orders directly on Best Buy.

Nvidia RTX Spark specs to know before ordering

Nvidia calls its latest SoC simply "RTX Spark," but it's actually a range with two different chips. The full chip comes with a 20-core Grace CPU and 6144-core Blackwell GPU, but there's a lower-end configuration with an 18-core Grace CPU and 5120-core Blackwell GPU.

Pay close attention to which you are ordering. Listings will simply call the chip "RTX Spark N1X," and you have to read through the description to confirm the core counts on the CPU and GPU.

From the orders available now, we've only seen the 18-core CPU/5120-core GPU version with laptops that have 24GB or 32GB of Unified Memory. Listings with 48GB or above have the full chip. The RTX Spark is available with several different memory capacities, including 24GB, 32GB, 48GB, 64GB, and 128GB.

For storage, we've seen configurations with 512GB, 1TB, and 2TB, though the vast majority of devices top out at 1TB, including the Surface Laptop Ultra. Undoubtedly, we'll see configurations with more storage capacity eventually, but that's a tough sell right now when even a tricked-out 2TB configuration costs $7,000.

Nvidia CEO Jensen Huang announced the RTX Spark platform with the grand ambition to “reinvent the PC” back at Computex 2026. By pairing the GB10 Superchip with a version of Windows on Arm tailored for the secure and contained execution of local AI agents, Nvidia and its partners at Microsoft, Dell, HP, MSI, Lenovo, and Asus are all hoping to pave the way to a future where the PC is an active, autonomous companion for work and creation.

The hardware foundation for that vision of the future is now in place. Last week, we flew out to San Francisco to get all the details on the RTX Spark platform, and we returned home with Microsoft’s Surface Laptop Ultra in top-end trim to put it all to the test.

This system has the full RTX Spark Superchip with 20 CPU cores and 6144 CUDA cores alongside 128GB of LPDDR5X-9400 RAM, and I’ve been putting it through its paces over the past week to see just what it can do.

The embargoes for the RTX Spark launch are a bit peculiar. Today, we’ll be sharing our broad experience with the Spark platform, as well as competitive performance evaluations, but not any system-specific results such as battery life, build or display quality, or other details of that nature. We have other RTX Spark laptops in the Tom’s Hardware labs, and detailed reviews of those systems will be coming soon.

No, this approach doesn’t make a lot of sense when the experience of a mobile platform is intimately and inextricably tied to the specific implementation of that chip in a particular laptop design. And we know that battery life is the first and foremost concern of many interested in this platform. But again, all that information is coming soon.

For this first round of testing, we compared the Microsoft Surface Laptop Ultra with RTX Spark against two systems. The first is a 14" MacBook Pro with Apple's highest-end M5 Max SoC inside. That Mac has a 40-core GPU and an 18-core CPU, as well as 128 GB of LPDDR5X RAM. We don't have a mobile Ryzen AI Max+ 395 system in the labs right now, but we do have AMD's compact Ryzen AI Halo as a source of fresh data with the latest ROCm stack and llama.cpp builds.

Local AI performance

Judging local AI performance is a constantly moving target, and this review is no different. Our past testing has exercised the prompt processing and token generation phases of AI inference separately, with the understanding that a system that's fast at both those phases provides the best experience.

But in an agentic workflow, that benchmarking technique doesn't capture the total time required to complete a task across all the inference turns needed to reach a particular goal. To capture that entire end-to-end time, the folks at Artificial Analysis have developed a new benchmark, AgentPerf-local, that precisely replays inference traces across several agentic AI tasks. The value of this approach should make immediate sense: the faster an AI agent finishes a job, the more productive a system can be.

So instead of presenting raw time-to-first-token and tokens-per-second figures, as we have in the past, we’re sharing time-to-completion values as we would for more traditional CPU and content creation benchmarks.

Again, local AI benchmarking is a continuous work in progress, so let us know what you think of this approach, and we’ll take that feedback into account as we continue to develop our methods.

We ran AgentPerf-local with two compatible models: the dense Qwen 3.8-27B, which is popular as one of the most intelligent local models available for its size, and the Qwen 3.6 35B A3B mixture-of-experts model, which is less intelligent but easier for a wide range of systems to run quickly. We used the latest build of llama.cpp as our inference backend and built it from source on each test system to take advantage of CUDA, Metal, or ROCm acceleration, as appropriate.

Let’s look at Qwen 3.8 27B’s behavior first. We used Unsloth’s Q4_K_M quantization of the model. We ran it with DFlash2 speculative decoding on the RTX Spark and MacBook Pro, and with Qwen 3.8 27B’s built-in MTP on the Ryzen AI Halo for the best performance.

As we found in our benchmarking of Apple's Mac Studio with the M4 Max SoC, memory bandwidth is only one constraint for local AI performance. Despite its memory bandwidth advantage on paper, the M5 Max MacBook Pro still ends up taking about 25% longer to finish the AgentPerf benchmark with Qwen 3.8 27B than the Surface Laptop Ultra with RTX Spark.

Part of that may be down to software maturity, but it's also worth remembering that our Surface Laptop Ultra is a 15" system with a much larger 140W power adapter versus the 14" MacBook Pro's 96W charger.

The 16" M5 Max MacBook Pro also comes with a 140W adapter, and we'd be curious to see whether the extra power and higher thermal headroom afforded by that system’s larger chassis help close the gap here. But in any event, the RTX Spark platform is the fastest overall at this task today.

Meanwhile, AMD's nearly two-year-old Ryzen AI Max+ 395 just can't keep up with either of these newer systems in this demanding test. It needs about twice as long to finish our agentic tests with Qwen 3.8 27B as either the RTX Spark or the MacBook Pro do, at a whopping 40 minutes.

The fact that the Ryzen AI Halo is a mini desktop form factor rather than a notebook doesn’t much matter to these results, as any power and thermal headroom that design might afford can’t possibly overcome the performance gap here.

Next up, we have the lighter-weight Qwen 3.6 35B A3B. We ran Unsloth’s Q4_K_M quantization of this model with MTP enabled.

The Surface Laptop Ultra ends up completing this test in just eight minutes, or about 25% faster than the 14” MacBook Pro’s 10 minutes. This test is also much friendlier to the Ryzen AI Halo, which finished the benchmark in a more reasonable 13 minutes. But that just emphasizes that you have to trade away model intelligence to get a reasonable level of performance out of Strix Halo.

AMD has recently gone on a publicity blitz to highlight the relatively low cost of entry for Strix Halo systems versus DGX Spark desktops and now RTX Spark laptops. But if time is money, and getting more intelligent tokens out of your local AI system faster means more potentially profitable agentic work gets done, then a more expensive system upfront will be able to do more of that work and potentially generate more value over time.

Gaming performance: first impressions

Despite its RTX branding and the emphasis Nvidia places on its 6144-CUDA-core GPU (or 48 SMs), the RTX Spark platform faces some tough challenges for gaming. Most games available today are not going to be natively compiled for its Arm CPU, and even for those that might be, the maximum 300 GB/s of shared memory bandwidth the platform offers is well below that of even the RTX 5060 Mobile's dedicated 384 GB/s feeding 26 SMs.

The entire GB10 SoC is competing for a relatively small amount of memory bandwidth, and because graphics is generally a memory-bandwidth-bound workload, that's not a great start for feeding all of those SMs in games.

I started my gaming explorations with Gears of War: E-Day. In this exceptionally CPU-heavy game, it's clear that the baseline performance of the Grace CPU cores and the added overhead of the Prism x86 emulator are doing this title no favors.

GPU utilization only reaches the 85% mark on average at a near-1080p (1908x1272) render resolution with the High preset and 2X framegen, and we saw at least one major hang during a map transition that was less than confidence-inspiring for an immersive experience as we progress further into the game.

Because of its CPU dependence, Gears is probably a worst-case scenario for this platform, but the fact remains that it's not a great experience, and turning down settings or lowering upscaling input resolutions just exacerbates the CPU-boundedness of this title. If any game could use a native Arm build, this is the one.

Control Resonant is significantly less dependent on the CPU, but that just means that the other potential gaming shortcomings of the RTX Spark Superchip seem to come into play.

After struggling a bit to achieve a solid frame rate and input latency at the Surface Laptop Ultra's native 3270x2180 output resolution even with DLSS upscaling, I found that running the game in windowed mode at 2560x1440 with DLSS Performance upscaling, the High preset, and MFG 3X to keep frame rates near the Surface Laptop Ultra's 120Hz refresh rate provided better results. (This system's tall 3:2 aspect ratio means that 16:9 fullscreen games don't cleanly scale up.)

But even then, input latency was above what I would consider acceptable on our standard GPU test rig, and enabling ray tracing at all dipped frame rates and spiked input latency even further, so I had to stick to raster-only rendering for the best results.

Overall, my early RTX Spark gaming verdict is that you can do it, and you can have a fine enough time, but it is not the primary focus of this platform. Gaming performance with the relatively limited memory bandwidth of the RTX Spark Superchip, combined with the system's CPU performance and the overhead of Prism emulation, mean that you'll be searching for a narrow range of settings where baseline frame rates, upscaling, image quality, and the smoothness boost of frame generation still yield acceptable input latency.

When I first approached gaming on this platform, I had expected that DLSS upscaling would be a required part of the experience, but in practice, you really need to lean on DLSS Frame Generation, as well, whether that's for added smoothness (as in Gears of War) or for both smoothness and the benefits of Nvidia Reflex due to developer choices behind the scenes (as in Control Resonant).

Even with Reflex, though, the Grace CPU generally seems to deliver input latencies significantly higher than I'm used to on our Ryzen 7 9800X3D test rig, and it's hard to get them much lower in these AAA titles by tweaking settings.

If you demand the lowest input latency and the highest performance with cutting-edge games, especially if you plan to enable RT, a separate gaming laptop with a dedicated RTX 50-series GPU will still provide a superior experience. And if you just don’t care about local AI at all, that advice applies even more strongly.

Bottom line

Nvidia's RTX Spark is an ambitious new PC platform for the traditional workloads of today and the potential agentic AI work of tomorrow. How often do we see an entirely new player try to elbow into the crowded and competitive PC space, especially one with a sweeping vision for how the PC will be used in the years to come? An incremental move this is not.

Our general performance tests and new agentic AI tests show that despite providing merely solid CPU performance and relatively low bandwidth for a unified memory architecture on paper, the Blackwell GPU on board GB10 has enough AI horsepower in practice to let the RTX Spark platform come out ahead of Apple's latest top-end M5 Max and AMD's Ryzen AI Max+ 395, aka Strix Halo, SoCs.

Whether you choose the highly intelligent dense Qwen 3.8 27B model and trade raw speed for intelligence, or make the opposite trade with the lighter and faster Qwen 3.6 35B A3B, the RTX Spark comes out on top. That bodes well for pairing popular agents like Hermes with intelligent local models on this platform.

But Nvidia's mission to reinvent the PC isn't a total grand slam in this first iteration.

First, this platform won't replace a dedicated gaming laptop. Even for those who only game on the side and have more forgiving standards for frame rates and input latency, the RTX Spark's gaming performance may not measure up to expectations.

When you put the overhead of Prism x86 emulation on top of the merely adequate performance of the Grace CPU, it’s clear that the Spark platform will have to work hard to keep up in the vast range of games that don’t have an Arm port.

The CPU-heavy Gears of War: E-Day is particularly unkind to the Prism-on-Grace stack, but even when a title like Control Resonant doesn't lean so heavily on the CPU, the limited and shared memory bandwidth of the GB10 SoC still poses an obstacle to cranking settings to the max despite the large amount of theoretical compute horsepower on tap.

We're also still waiting for Windows to fully catch up to Nvidia's ambitions for secure, manageable local agents. Our laptop expert Andrew Freedman and I couldn't test Microsoft's secure agentic AI promises within Windows 11, as the supporting features that Microsoft and Nvidia have promised to that end just aren't ready yet.

Still, for a first-generation mobile platform that joins complex silicon IP from multiple design teams onto one chip and relies on several OEM partners to bring it to life in highly integrated notebook form factors, the maturity of the RTX Spark platform is an impressive achievement. (Take it from someone who has seen a couple of rough product launches from the inside.)

Over the past week, I've been using the RTX Spark-powered Surface Laptop Ultra as I would any other Windows laptop, and aside from a couple of tiny paper cuts, I have mostly just gotten my work done as I would with any other computer. And that’s a great — albeit a very, very expensive — place to be as the local agentic AI future coalesces.

Perhaps the biggest challenge for the RTX Spark platform is that the eye-watering price tags for the range of systems available at launch still buy a lot of cloud tokens from OpenAI and Anthropic. Emphasis on a lot. The Surface Laptop Ultra we tested lists for $5899 with only 1TB of storage, and lead times for that system are already out to late December.

If you're not as concerned about keeping sensitive data out of those labs’ clouds, or you don't expect to run afoul of acceptable usage policies, you'll get much smarter tokens much faster than you will from a local token factory like the RTX Spark, and you can get them on most any reasonably powered traditional PC you have available.

But if you're surfing the AI wave on local hardware for reasons of privacy, offline model availability, freedom to tinker, or just because you think it's cool, the RTX Spark platform blends the memory capacity, compute performance, and software resources you need to get there with the familiarity and broad compatibility of Windows on top. The fact that Nvidia, Microsoft, and their partners have delivered such a capable, compatible, and polished platform even in its first at-bat should put the rest of the PC industry on notice.

Nvidia’s efforts to reshape personal computing will face strong headwinds in the immediate future from the sky-high DRAM and NAND price hikes that the company’s own Vera Rubin rollout is partially responsible for. Perhaps when the dust settles in the next two to three years, and products built around the next-generation RTX Spark Superchips arrive, we can more confidently assess whether the company has accomplished its goal.

For now, the RTX Spark feels like a platform for those who fervently believe that open AI models will continue to get more intelligent within relatively small memory footprints, and that those potential future improvements are worth a hefty chunk of change to get ready for today. Maybe that's a smart bet in a world where compute is an increasingly precious resource. But unless you're a true believer with cash to burn, our first impressions suggest that you should wait until Microsoft and Nvidia fully lay the software foundation needed for this platform to deliver on its promises.

A Redditor has shared a photograph of the prices of Nvidia RTX 50-series GPUs in Japan, and it doesn’t bode well for gamers. According to u/Chaise91’s post on the r/pcmasterrace subreddit, retail prices for some of the best gaming GPUs are somewhere between 5.66% and 41.1% higher compared to Amazon U.S. listings.

The memory chip shortage and massive demand for AI compute have been affecting the supply of gaming GPUs, with reports suggesting Nvidia is planning to halt RTX 5090 production in favor of AI data center and professional GPUs. Because of this, the supply of high-end GPUs has vanished online, with third-party sellers now demanding almost $10,000 if you want to buy one. The image shared on the social media platform does not show the RTX 5090, so we fear that retailers in the market no longer have the high-end graphics card in stock, leading to supply pressure on the lower-tier cards.

We built a table comparing the prices from the Japanese retailer to listings we found on Amazon based on the current exchange rate of JPY 153.38 for every USD 1, and the hikes vary between 12.71% on average for the RTX 5080 to a jaw-dropping 41.10% for the RTX 5060 Ti 8GB. Note that these are pre-tax prices, though, so your mileage may vary depending on which state you live in. We added a representative 8.5% tax rate to see the price difference for gamers in states with high sales taxes, and the difference falls between 3.88% and 30.04%.

While the Japanese market is typically more expensive compared to the U.S., especially in GPU pricing, you would still feel the price hikes on these graphics cards, especially for the mainstream RTX 5070 Ti and below. While this may be bad news for Japanese gamers, it could also serve as a harbinger for the United States, as these pricing trends tend to travel westward. We saw this late last year, when Japanese shops started limiting SSD, HDD, and RAM purchases due to shortages, and GPUs with 16GB VRAM and up had to be rationed because resupply shipments failed to arrive on time.

These price increases are indeed plausible given the chip shortage pummeling the PC gaming industry, but we’re hoping that this is just a fluke in the local Japanese market and not a preview of things to come for the global GPU market. But in case you’re on the market for a new graphics card and have been following our GPU price tracker to get the best possible deal, you shouldn’t delay your purchase any longer, as there’s a chance that these price increases will eventually land on American shores.

A Ukrainian DLSS 5 experimenter is back at it again, this time testing the original Quake game. It’s running as a post-process, not natively integrated into the game engine, using 4x Multipass to enhance the effect, but it doesn’t look so good.

Valery Kryzhanovsky has posted multiple videos running DLSS 5 on games it was never meant to be used for, nor were the games built with AI upscaling and image enhancements in mind. We previously covered a few of Valery’s projects, running DLSS 5 on 2D games like Duke Nukem and Pac-Man, resulting in weird and cursed images.

However, this time Valery uploaded a video of it on the 1996 Quake, considered to be the very first true 3D FPS in history. Valery explains in the description that the game is not running the leaked DLSS 5 build natively, but as a post-process using ReShade through RenoDX to play with the parameters and settings and inject the effect on DOSBox’s rendered frame. So it’s an emulated version of Quake with basically an AI filter with the DLSS 5 algorithm over it four times, except it’s running in real time on a game. This is similar to how others have experimented with DLSS 5 on videos, applying the effect on top, like JackSpace’s video to “fix” bad CGI in old movies with DLSS 5.

Before DLSS’ popularity, Nvidia released an RTX demo running Quake II with ray tracing in the game engine to improve the lighting. That demo looked stunning, with realistic lighting and reflections, but DLSS is not ray tracing/RTX; it’s an upscaling technology that runs well on RTX cores. And on top of that, DLSS 5 is not even integrated into Quake’s engine, explaining the terrible results. However, this is a very early build of DLSS 5, and Valery says in his blog that the leaked build is undertrained. I honestly thought it would do better, considering there are more obvious 3D elements for the upscaler to work with compared to the 2D games Valery has experimented with. It seems that it darkens everything so much, possibly because of the sparse distribution of torches and highlights.

This might fare better if DLSS 5 were integrated into the engine, like how Sultim Tsyrendashiev modded ray tracing into Quake's engine, similar to Nvidia's Quake II demo.

Earlier this week, Nvidia announced that it had improved the performance of its DLSS 5 model by 2x, while shrinking its footprint by 24%. The result should be higher FPS while using less VRAM, drastically increasing performance.

Chinese tech companies have bought more than 1 million Nvidia gaming and workstation GPUs to plug gaps in demand for AI computing power, Financial Times reports. The report cites industry insiders who claim the buying spree highlights an ongoing and significant reliance on U.S. AI hardware, despite a burgeoning homegrown market.

The report firstly notes that China's national strategy for developing AI has led to a doubling of domestic AI chip production this year; however, the country cannot keep up with demand from its biggest tech companies. The report cites a shortfall of nearly 1.5 million chips, with only 3.5 million expected to be manufactured domestically while demand "far exceeds" 5 million. To compensate, China is purportedly turning to Nvidia's gaming and workstation processors, which are compliant with U.S. export controls, repurposing them for AI.

According to FT, the chips are "mostly" Nvidia's RTX Pro 6000D, RTX Pro 5000, and the RTX 5090, the latter of which is the best graphics card on the market for enthusiast gaming. As the report notes, these Blackwell chips can't compete with Huawei's Ascend 910C and other homegrown flagship offerings, but they are useful for "simpler AI workloads," such as prefilling.

This is significant pressure on a GPU market where prices for enthusiast cards have already skyrocketed. The RTX 5090's MSRP is $1,999; however, the card can now only be found at a handful (three) of third-party resellers for no less than $7,199, and has all but disappeared from the market.

The report highlights that demand for Nvidia's GPUs is driven in part by its CUDA software, which remains more favorable than Huawei's Cann alternative.

As for the enthusiast market, it was recently reported that Nvidia has halted RTX 5090 production, to be replaced by a 24GB RTX 5080 flagship. That would mark a significant step down from the 32GB the 5090 currently boasts, a troubling sign for enthusiasts that the enthusiast gaming GPU market appears to be receding, rather than advancing.

Nvidia has today announced that its somewhat controversial DLSS 5 technology — AI-powered neural rendering for gaming — is now two times faster than the current model, marking a 10x performance leap since March. The model is also now smaller and uses up to 20% less VRAM.

Nvidia says it has achieved this through a combination of "systematic pipeline optimizations and neural model refinements." As mentioned, the latest performance jump over the current model is a 2x leap, which means that the transformation DLSS applies to games through lighting and beyond will now run at higher frame rates or more intense settings.

Nvidia says the DLSS 5 October update shrinks the overall DLSS 5 model's footprint by nearly a quarter to 118 million parameters, and reduces VRAM usage at 4K by 20%. As such, the company claims that at similar visual fidelity, the GPU overhead versus the September model has been halved. Elsewhere, Nvidia says the change can boost frame rates by up to 1.4x.

The new faster model will debut in NBA 2K27 on October 16; you can see a comparison in the video below:

Nvidia confirmed it will bring DLSS 5 to Hogwarts Legacy next month. The technology continues to be supported on RTX 50-series GPUs, as well as GeForce NOW and the new RTX Spark lineup.

Nvidia is also introducing a new faster Performance Mode for Frame Generation on RTX 50 Series, as well as RTX 40 series GPUs, and RTX Spark laptops. The company says that a new "Preset C" mode replaces the AI model previously used "to calculate optical flow for elements like UI or ray-traced effects" with a new algorithm that is faster and lighter, boosting DLSS Frame Generation, MFG, and Dynamic MFG. The company says this is worth 35% in terms of speed on the RTX 5060.

Over recent weeks, we've seen the release of DLSS 5 Neural Rendering in NBA 2K27, followed by its porting to older GPU generations, and the unlocking of DLSS Multi Frame Generation (MFG) for older graphics cards. But how did all of that happen, and who are the people in the modding community unlocking features that Nvidia itself has locked off from users of anything but its latest Blackwell-based GPUs? We sit down with modders Dashdogy and Uncle Burrito to understand how, and why, the modding community has been breaking down Nvidia's software-based locks.

In 2025, during the release of the RTX 5000 series graphics cards, one Australian student was watching a Digital Foundry interview with Nvidia engineers, where the team was showing off Multi Frame Generation (MFG), a feature locked to Blackwell GPUs. During the podcast, the idea of MFG running on older RTX 4000 series hardware was floated, but the feature was never released for Ada Lovelace hardware. “Every few months, I would do a Google search to see if it ever happened or not,” said Dashdogy, a Physics Major studying Mathematics and Space Science.

“A few weeks ago now, the perfect storm arrived. Mid-semester break had just started, and I did the same Google search as I always do. But thought, maybe I’ll look into it. How hard could it be?”

One AWS instance, and a week of late nights later, he unlocked “true” MFG on Ada Lovelace hardware, a feat previously believed to be impossible due to incompatible instructions and a black screen that blocked the progress of modders before them. We’ve tested the mod extensively, and the results stand out. Multi Frame Generation has unofficially arrived for non-Blackwell hardware.

Dashdogy isn’t the only modder that’s been hard at work unlocking features for older-gen GPUs. Several weeks ago, when DLSS5’s Neural Rendering DLL first found its way onto the internet, RTX Remix modder Uncle Burrito set their sights directly on it. “I had no idea of telling [if running DLSS 5 on Ada] was even possible,” they said. Given the previous performance claims of requiring multiple RTX 5090s to run on its initial reveal, Uncle Burrito knew DLSS 5 would be computationally expensive, but that didn’t stop him from trying to get into the DLL itself.

“Once I reverse-engineered it, with the help of Claude, it became obvious that this was possible on Ada. Not only was most of the code compatible, but to me, it looks like Ada support was simply left behind as a work in progress, not as a hard limit.”

And so, less than a day after the DLL had been released in an early access version of NBA 2K27, Uncle Burrito had successfully ported the technology to Ada hardware. In the weeks since the release of Uncle Burrito’s initial unlock for DLSS 5 NR on Ada GPUs, Nvidia has clarified that Neural Rendering would indeed be supported by RTX 4000 series GPUs, lending credence to the idea that implementation on Ada GPUs was merely unfinished in the initial shipping versions of the DLSS 5 DLL.

These two foundational mods by Dashdogy and Uncle Burrito now serve as the baseline for endless numbers of forks and integrations of both Neural Rendering and MFG for older hardware, which have now allowed for ports all the way back to 2018’s Turing architecture, and in DLSS 5 NR’s case, ports to AMD hardware. It’s now as simple as using one of many tools to click-and-install both into hundreds of games.

Unlocking MFG (Dashdogy)

Dashdogy’s experience in modding spans years, having created Stellaris mods while still in High School, making compatibility scripts to patch multiple mods together. But porting DLSS MFG to Ada devices was a different beast entirely. Blackwell and Ada use entirely different ISAs, despite their software similarities. “When this [running Blackwell instructions on Ada] happens, the frames will generate, but the GPU graphs an error and returns a black screen,” he explains.

Underpinning Nvidia’s implementations of DLSS in shipping titles are two pieces of software: a package named Streamline, which usually includes a suite of DLSS software, and is a universal translator that allows games to run various upscaling technologies like DLSS. Used in tandem with Streamline is a Neural Graphics Framework (NGX), Nvidia’s proprietary software that underpins its AI-powered efforts like Super Resolution and Frame Generation. As a game calls for tools, NGX cooks the computation, and Streamline serves them to a given game’s engine.

As Dashdogy explored how to make MFG compatible with Ada, the modder targeted Streamline and NGX directly. “I specifically spoof the Streamline and NGX calls in memory that define how many frames the GPU is allowed to generate.” This approach allowed an Ada-based GPU to think it was allowed to generate more frames than it ordinarily would. “It’s a pretty simple interception, and on 40-series turns 1 into a 5. Now the GPU knows ‘I am a 4080 and I’m allowed to generate five frames,'" which successfully swerved the black frame issue that other earlier modders encountered, thereby unlocking higher frame generation multipliers.

Dashdogy quickly packaged things up as a Cyberpunk 2077 mod, and shipped it off to Nexus Mods. This was the canary in the coal mine for a more mature integration, but things weren’t quite perfect yet. Dashdogy and other modders reported a problem — when using anything more than 2X MFG in Cyberpunk 2077 on Ada, things felt sluggish. Latency wasn’t great, and with the help of another modder, they believed that this was the work of the fabled “Hardware Flip Metering,” which smooths out MFG's outputs, and is a primary reason Nvidia offers as the reason the feature doesn’t work on older graphics cards.

Armed with a cloud instance that uses an RTX 6000 Pro Blackwell GPU, Dashdogy quickly went to work improving the MFG mod. “I conducted a kernel dump whilst the GPU was running MFG.” Dashdogy was also equipped with OpenAI’s Daybreak Blue AI model, a modified version of GPT 5.6-Sol with safeguards adjusted to allow for defensive cybersecurity tasks and vulnerability discovery. Using the model and his know-how, he analyzed the RTX 6000 Pro’s kernel dump to check where things might be going wrong for his MFG mod, directly comparing it to his native RTX 4000 Series integration.

“For 6X frame generation, the GPU will render two real frames, then generate five frames and place them between those two real frames,” he told Tom’s Hardware Premium. Without copying this integration, frame pacing in his existing solution felt odd and disjointed.

“The 40-series-generated frames were all compacted around the middle of the two-frame interval. As you can see [in the image above], what we would expect, and what I measured from the [RTX Pro 6000 Blackwell], was five frames, neatly positioned at separate intervals. However, the 40-series GPU was forcing all of these around the same interval as 2X frame gen would put its one single frame.”

This caused the frame pacing of the MFG frames in the initial mod to feel sluggish, weird, and have poor responsiveness, as frame pacing is crucial in making a title feel smooth, especially when any frame generation tool is involved. “I discovered that the 40-series kernel responsible for motion processing was hardcoded to output a midpoint scaling for 2x only,” Dashdogy described. “With multiple generated frames, that compressed their represented motion toward the middle. With the fix, they spread out much more like the Blackwell reference.”

“The mod can work anywhere normal frame generation is implemented in any game,” Dashdogy told us, with additional telemetry for users to see exactly what’s changing. “The kernel is the same for both 40-series and 50-series; [it’s] just architecture-specific functionality [which blocks MFG on Ada]. 50-series has a number of Blackwell-specific optimizations that I’ve backported as well, and they contribute to better latency overall.”

The modder explains that the frame pacing fix was the most difficult part of the now-universal MFG port to Ada. But after its release, it now sits as a loadable ASI in a game’s directory and controls up to 6X frame generation in supported titles. The project has since been forked by other authors, which plugs the logic Dashdogy wrote into ReShade, a popular graphics mod tool.

Unlocking DLSS 5 NR (Uncle Burrito)

“I’ve been working with 3D art and graphics for many years now,” explains Uncle Burrito, a graphics enthusiast who implemented Path Tracing into Need For Speed Underground 2 using RTX Remix several years ago. “Over the years, I’ve stayed in the RTX Remix community to learn a lot more about real-time rendering and the techniques used for it.”

After hanging out in Nvidia’s RTX Remix Discord channel, news of the emergence of DLSS 5’s nvngX_dlssnr.dll spread like wildfire. According to Uncle Burrito, mods quickly clamped down on discussion of the DLL, and modders flocked to private channels and alternative servers, such as the RenoDX Discord server, to discuss development and integration. The DLL itself was written specifically for Blackwell, but the Ada architecture shares the ability to read FP8 instructions, which is what the DLL uses to load DLSS 5 Neural Rendering’s model weights.

Using that as a starting point, Uncle Burrito quickly went to work. “The model itself is just weights. These weights need code to be executed. The code was only written for Blackwell. CUDA binaries, right?” he explained. But despite this, and the aforementioned difference in ISAs between Blackwell and Ada, certain instructions could not be read by the humbler RTX 4000 series GPUs.

“Four of the 15 CUDA binaries [contained within nvngx_dlssnr.dll] would not run on Ada.” But this wasn’t an impossible task, as Uncle Burrito previously explained. The integration for Ada appeared unfinished and therefore, not entirely impossible. “I simply needed to patch the replacement compiled binaries”, meaning that it only took patching four affected CUDA binaries to get things up and running for RTX 4000 series graphics cards, Uncle Burrito told Tom’s Hardware Premium.

Uncle Burrito, alongside Ghidra, a reverse engineering tool, and an instance of Claude Code, set out to recompile the incompatible binaries to design a patch, but fell into a roadblock made by Claude itself. “I’m not too familiar with reverse engineering myself [...] Claude made the decision to use the incorrect version of the CUDA SDK to compile the replacement [binaries].” And so, the patched DLL ran, but instead output a black screen. “I could see the NR debug banner, and it did affect my framerate, but the output was completely black.”

“This is one of the many pitfalls of vibe coding,” Uncle Burrito said. “You can’t make anything good if you don’t understand what you’re working on. Usually, I don’t give the model any room to gaslight or make a wrong decision. If you don’t know what you’re doing, then don’t expect miracles.”

After realizing the mistake and recompiling with the correct CUDA SDK, the results were quickly posted to the RenoDX Discord server. “Ada performance is about as good as it can get. It has native FP8 support, so the hardware is doing its best. The rest can be attributed to simply not [having] enough horsepower in the chips.”

The simplicity of the patch might appear to be surprising, but the DLL leans on the same information that’s standardized between games and platforms, similar to how Streamline works in most game engines. “Motion vectors and depth buffer are [two] of the few things in graphics programming that we’ve standardized. Almost every modern game produces motion vectors, and rasterization isn’t possible without a depth/z buffer,” Uncle Burrito told us. Therefore, the DLL becomes a drop-in replacement for the officially distributed one, which unlocks the functionality on Ada.

Uncle Burrito’s methodologies were since picked up by RenoDX’s ShortFuse, who then worked on porting the DLSS 5 NR DLL to Ampere and Turing generation graphics cards. That porting work is more involved, since it also includes the translation of the model to be read by FP16 instructions. Following the release of Uncle Burrito’s work, a new DLL has emerged, with universal compatibility between Turing, Ampere, and Ada generations.

Unblocking the software lockouts

Between the MFG unlocker from Dashdogy and the patched Neural Rendering DLL created by Uncle Burrito, the community-made mods both unlock functionality that would not have come to older-generation cards or would have otherwise been added at a later date. According to Dashdogy, “40-series has no excuse” when it comes to support for Multi Frame Generation. “It’s a very small change that allows [the mod] to work,” noting that additional work is being done to recompile things for Turing and Ampere, with the caveat that latency and overheads get higher the further you go back.

“It always comes down to performance,” Uncle Burrito said. “It’s fair for [Nvidia] to not include Ada until they can get the optimization part sorted.” The modder further adds that the situation can be read in both ways, as a method for Nvidia to sell more RTX 5000 series graphics cards, and to have more time to optimize Neural Rendering for older RTX 4000 series GPUs.

Everyone wants to make the most out of their available hardware, especially at a time when consumer graphics pricing has firmly rocketed past the moon, with RTX 5090 GPUs now approaching north of $9,000. “I personally think [the MFG mod] is really important in the current market,” Dashdogy adds. “... many are just getting priced out of the new generation entirely, so giving people the ability to hang on to their GPUs for a little bit longer is going to help a lot of people stay invigorated and connected to the hobby.”

If a single Australian student can unlock DLSS MFG for older GPUs, Nvidia’s claims of hardware exclusivity appear less like a technical limitation and more like artificial lockout. For those unafraid to modify a few simple files within a game, those features are all readily available, albeit unofficially. There’s an irony in how both MFG and Neural Rendering were ported to older GPUs — the development of both mods relied on cutting-edge AI models — powered by the same ecosystem that’s driving Nvidia’s earnings. Now, the very same AI revolution Nvidia champions is being used to tear down its own walled garden.

A GitHub programmer has created an Nvidia bug tracker page that compiles each driver update’s release notes and feedback threads on the forums, logging progress on 462 different driver bugs. They first shared their creation, called NvidiaWatch, in late September and then released an update on the r/Nvidia subreddit earlier this week.

The programmer said that each bug gets its own page, meaning anyone affected by it could easily track if the latest driver update has already solved the issue. Each driver update also gets a dedicated page that includes the release notes, fixed bugs, and open issues that Nvidia couldn’t address yet. The creator also added a chart that tracks the number of known issues that were fixed during that release, fixed in a later release, remained open, and those that were carried over from an earlier driver release.

This website is quite useful for anyone running into issues with their Nvidia hardware, as they’d be able to track whether their problem has already been recognized by the company and whether it has done something about it. It’s also a good idea for anyone wanting to update the driver on their GPU to consult NvidiaWatch first; that way, they could avoid some nasty surprises like the fan control bug that came with the GeForce 595.59 driver.

Hopefully, someone could create a similar update but for Windows updates. A website like WindowsWatch would absolutely be useful, especially as the Microsoft operating system has a lot more users than Nvidia GPUs. This would help savvy users determine if an update is problematic, allowing them to roll back or pause updates before they encounter an issue, while also making Microsoft’s notes easier to understand for the layperson. Having a trend graph could also pressure Microsoft to fix issues much more quickly, as users could now see the number of problems piling up between fixes. This would be a bit complicated, though, especially as there are many different Windows versions, with some updates only affecting a few versions.

A Reddit post by user markpronkin (“OP”), titled “54gb vram for 35$,” says the user purchased an old crypto mining rig, originally discovered in a garage, for a negotiated 3,000 rubles, or about $35. The rig was rusty, and the buyer “had zero idea if it works, so did the seller,” with a pre-purchase test request refused. However, on the first try, all nine Nvidia P106-100 GPUs, with 54GB of VRAM between them, worked. The initial test: a successful local run with the 35-billion-parameter model Qwen3.6-35B-A3B in Ollama, which operated at over 30 tokens per second.

The rig was discovered in a “damp garage” the seller had purchased years earlier, then listed on Avito, a Russian classifieds site. The OP had concerns about whether or not the rig would work and offered to test it and pay more if it functioned, but the seller refused, and the price was negotiated down without a test. The cost was low enough to take the risk, the buyer said. Even in the case the rig worked, performance was imbalanced, as the main system has a weak Celeron CPU with very little system memory.

The setup was originally intended for crypto mining with specialized Nvidia P106-100 GPUs, Pascal-generation cards with 6GB of GDDR5 each that OP calls “basically gtx 1060 6gb.” Gigabyte’s P106-100, for reference, has 8,008 MHz memory with a 192-bit bus width for about 192 GB/s per card. The hardware was originally introduced around the middle of 2017. The cards are no longer entirely supported as CUDA 13.0 removed offline compilation for GPUs below compute capability 7.5, which includes Pascal GPUs, and driver branch 580 is “the final [one] that supports GPUs before CC 7.5,” Nvidia says on its developer blog. Security updates are supported through October 2028, however.

The first test was a mixture-of-experts model using Alibaba’s Qwen with “35B [parameters] in total and 3B activated,” per Qwen’s model card on Hugging Face; it’s roughly a 24GB download. The initial test had it at “30+ tokens a second” in Ollama but it can probably operate faster “with mtp and some optimizations,” OP says, referring to multi-token prediction.

Another user stated that such performance was possible on a CPU alone, to which OP replied, “On a 35$ device without any optimizations?” A dense, non-MoE model such as Qwen3.8-27B is unlikely to run as well, but “It should run any MoE model that fits entirely into VRAM decently well.” Performance is also expected to be limited by the PCIe 2.0 x1 motherboard slots.

The system is capable but would have some limitations due to the sizes of some popular models. Qwen3.6-35B-A3B at 8-bit should fit at 36.9GB, as should Llama 3.3 70B at 4-bit, at 42.5GB; at 5-bit, just below 50GB, it would leave little room for context. Larger models are probably not workable, and even the 70B 4-bit Llama would hit a ceiling around 4.5 tokens per second before hitting other bottlenecks, since each token reads all 42.5GB, card by card, at about 192 GB/s. The performance the buyer achieved is in spite of the rig, and the MoE model was the best choice for that type of result.

The buyer still got a good deal, as the price comes out to about 65 cents per GB of VRAM. For comparison, an RTX 5090 with 32GB of VRAM and 1,792 GB/s of memory bandwidth launched at $1,999 in early 2025. The rig has 1.7 times the VRAM for less than 2% of that price, but each of its cards has about a ninth of the bandwidth. According to OP, the mostly compute-only P106 cards sell for about 50% of their GTX counterparts.

One important caveat here is electricity costs, with the GPUs having a combined 1,080W TDP as estimated by one commenter, but OP says their power is free and generally inexpensive in Russia as a whole.

With the rig securely in hand, the OP’s next move is to transfer the hardware to a better case that supports up to an i7-7700K CPU and 32GB of RAM. The new case allows for 12 GPUs, so buying three more of the same adds up to 72GB of VRAM total. The solution is attractive because of “unlimited usage and actual privacy,” the buyer said to WiredEntrepreneur. Repurposing used hardware for a self-hosted AI solution with appropriately sized MoE models is a viable, useful project for enthusiasts who want freedom. From the OP’s perspective, more VRAM means more freedom.

The Nintendo 64 game Super Mario 64 has come to the Microsoft Zune HD, Hackaday reports, through a project that runs natively on the device’s hardware through the unofficial OpenZDK kit, per the repo’s README. A touchscreen controller overlay is drawn over the game’s video, and the game “plays (mostly) at full speed with sound, touch controls and saves,” from the rsheldiii repo, although no ROM is included and one dumped from your own cartridge is required. The Zune HD, a 2009-era media player that rivaled Apple’s iPod, has Nvidia Tegra hardware inside, and the project is built on sm64ex, a fork of the unofficial, decompiled Super Mario 64 PC port.

The Zune HD, part of our 2009 gift guide, was marketed as having a “3.3-inch glass screen” using OLED and touchscreen technology, from Microsoft’s media alert at the time, with Tegra providing the “multimedia muscle,” according to an Nvidia release. The unit was originally priced between $219.99 and $289.99, depending on capacity, and uses the Nvidia Tegra APX 2600 chip. The operating system is Windows CE-based, with third-party games going through Microsoft’s XNA. The Zune was short-lived, with Microsoft halting production in 2011, but an exploit led to OpenZDK coming out that allowed for homebrew.

The implementation depends on native ARM code on top of the Zune HD’s OS, using OpenZDK. The device’s firmware is not modified, but the port, according to the developer, should only be used “at your own risk,” a standard disclaimer. It leverages an OpenGL ES 2 renderer derived from Fast3D with prebuilt shader variants, using Nvidia’s cgc and shaderfix tools, because the Zune’s graphics driver can’t compile OpenGL Shading Language (GLSL) shaders. Blending is compiled into the fragment shaders as well, since the GPU lacks fixed-function blending hardware. These workarounds are what make the project interesting.

The game’s logic runs at 30 ticks a second, but some lag is present when the Zune is unable to keep up. There is also no official FPS number. The game renders at 480x272 in landscape, with the device rotated to match. The audio synthesis runs on its own thread at a higher priority than the game, so it is not impacted by a slower frame rate. The touch control overlay provides a facsimile analog stick as well as the A, B, Z, R, four C, and Start buttons, with customizability for button style and opacity.

The project itself relies on a Super Mario 64 decompilation effort completed by fans in 2019, with a C source that rebuilds a matching ROM. The decompilation doesn’t include all of the game’s assets, and extraction requires a copy of the game. The ROM is read on the PC during the build, and the finished game can later be moved over to the device through USB. The build runs on Docker in Linux, so no Windows PC is required. The repo has no releases, and the port, which applies 14 patches to a pinned version of sm64ex, has only been tested on firmware 4.5.

The full PC port arrived in May 2020, of which sm64ex is a fork with extra features, such as mouse look and a cheats menu. Nintendo’s legal representatives responded quickly, targeting “sites hosting the compiled version of the Mario 64 PC port” and at least one gameplay video, Video Games Chronicle reported. However, a number of ports have found their way out for the 3DS, Wii U, PS2, PlayStation and PS Vita, Dreamcast, Nintendo DS and DSi, the web, and now the Zune HD.

The project shows what could have been on the Zune HD had Microsoft been less restrictive with development. Ports are nothing new, but the number has been increasing lately, potentially due to the wider availability of powerful AI tools; the repo’s first commit ends “Co-Authored-By: Claude Opus 5.5,” so AI was used at least in part. An example of something different is a decent Doom port to an $8,000 Hasselblad X2D camera, born from firmware research, showing there are multiple pathways to the same goal.

Nvidia has reportedly ceased selling its GB202 graphics processors for consumer-grade GeForce RTX 5090-series add-in-boards (AIBs), including 'D' variants for the Chinese market, according to a leaked notice from an unknown factory cited by @hongxing2020, who tends to have insights into the graphics card market. Another prominent leaker @Zed__Wang confirmed the report and said that from now on the GB202 silicon will be exclusively used on data center and professional graphics cards.

"According to a notification from the factory, Nvidia will no longer manufacture any RTX 5090 models, including the D variants," a message cited by @hongxing2020 reads. "Going forward, the highest-end GeForce graphics card will be the RTX 5080 24GB, while the RTX 5080 16GB version will be discontinued. Please spread the word."

This is, of course, an unverified claim and not an official Nvidia announcement. We asked Nvidia for a comment, though the company has yet to respond. Typically, Nvidia does not comment on rumors.

While the information is strictly unofficial for now, the alleged decision would make considerable economic sense for Nvidia, as the company can sell GB202 processors on data center and professional graphics cards that cost substantially more than the GeForce RTX 5090. In fact, Nvidia's recently introduced RTX Pro 5500 features the same number of CUDA cores — 21,760 — as the GeForce RTX 5090, but comes with 84GB of GDDR7 memory instead of 32GB and targets considerably more lucrative professional visualization (ProViz) and AI applications. Meanwhile, the fully-fledged RTX 6000 Pro with 24,064 CUDA cores and 96 GB of memory can easily sell for $15,000.

By discontinuing GB202-based GeForce products, Nvidia could prevent its relatively inexpensive consumer graphics cards from competing with its own professional accelerators in the lucrative AI sector. Essentially, the company is redirecting its limited semiconductor manufacturing allocation toward higher-margin products without necessarily sacrificing its position in the gaming market, as neither the GeForce RTX 5080 nor RTX 5090 have direct rivals.

Even if Nvidia has indeed discontinued shipments of GB202 processors for consumer graphics cards, GeForce RTX 5090 boards are unlikely to disappear from the market overnight. Nvidia's add-in-board partners, distributors, and retailers likely have inventories of GPUs and finished graphics cards that could sustain availability for some time. However, if Nvidia no longer supplies GB202 processors for GeForce products, the availability of RTX 5090 cards will inevitably decline, which will affect prices of these AIBs.

Meanwhile, Nvidia could be preparing to refresh its GeForce RTX 50-series lineup. The company has long been rumored to be developing GeForce RTX 50 Super graphics cards, including an RTX 5080 Super with 24GB of GDDR7 memory, although their introduction has reportedly been postponed due to memory supply constraints. The rumored GeForce RTX 5080 24GB could therefore represent a revival of these plans, and with no more GeForce RTX 5090 32GB on the market, the new product will have better memory supply.

Nvidia is also developing its next-generation Rubin-based GeForce RTX 60-series GPUs, but recent rumors suggest that these products will not arrive until late 2027, or even 2028. Consequently, if the reports about the discontinuation of the GeForce RTX 5090 are accurate, Nvidia could leave the enthusiast gaming market without a GB202-class flagship for a considerable period, so if you are in the market for the absolute best graphics card, now is the time to get it.

Ukranian DLSS 5 experimenting YouTube channel Valery Kryzhanovsky showcases the MS-DOS version of Pac-Man with 4x Multipass in four different rendering modes. The video shows weird and glitchy effects, with some rendering modes completely removing green Inky’s eyes and the dots.

This cursed experiment is another attempt at putting DLSS 5 in games it wasn’t designed for, nor are the games designed to use such upscaling technology. While Nvidia’s leaked upscaler has been used for modern 3D games it’s designed for, it has become a meme to run it in games that aren’t even 3D at all, and some attempts aren’t even games at all.

Valery was able to do this by running Pac-Man through the MAME arcade emulator and using ReShade to inject DLSS 5 into the graphics pipeline, and RenoDX to allow the DLSS 5 parameters to be tweaked. Valery emphasizes that this is not native, and of course it isn’t; the game was never designed to accommodate anything like it — that’s why it needed to be injected externally.

Valery elaborates why 4x Multipass is needed in his blog post, saying that 1x basically does nothing for some older games. Multiple passes are needed in order for the changes to stack up, stating that this usually makes it look bad, but in some cases, photorealism could be achieved. However, for Pac-Man, there is basically no 3D information DLSS 5 could gather. The game is flat, and it has barely any human-like features for DLSS to enhance, except eyes, which is probably why it gave it sunken eye bags or cheekbones, as Valery described in the description of the video. Duke Nukem, however, fared a lot better with the metal environment and occlusion along the walls and ceiling.

In the video, Valery tries out 4 different rendering modes: Natural, Default, Cinematic, and Denoise. Valery describes each mode: “Natural keeps the maze readable. Default decides the ghosts need cheekbones. Cinematic puts Blinky in a film nobody greenlit. Denoise clears the board of everything, dots included. One yellow circle, four ghosts, and four different opinions about what any of them are supposed to look like.”

The video doesn’t state which modes are enabled, but one of the modes definitely removed the eyes from the green ghost, making it look genuinely terrifying, as if it were one of those cursed game Creepypastas. All it needs is some red dots in that hollow chasm, and you’d have a stereotypical 2013 scary meme.

Valery further explains in his blog post that DLSS 5 is still undertrained, lacking temporal context, and is similar to earlier DLSS models. The 150MB DLSS 5 build is only a small peek into what it could be come release date — which will come with a more mature model and likely a larger file size.

Mistral launched its trillion-parameter Large 4 (ML4) model this week, saying it “pushes the frontier of open-weight performance.” Artificial Analysis (AA), an independent AI benchmarking firm, ranked Mistral's Large 4 as the best model available from outside the U.S. and China in its index. The mixture-of-experts model, trained on Nvidia’s Grace Blackwell GPUs, scored its personal best in AA’s CyberGym-E2E-AA end-to-end cybersecurity measure, although several Chinese open models beat it overall.

ML4 scored 38 on the Artificial Analysis Intelligence Index v4.3.2, which AA says makes it the “most intelligent model from outside the US and China." At least five Chinese open models scored higher on the index: DeepSeek V4.1 Flash (max) at 39, GLM-5.3-Flash at 42, Kimi K3 (max) at 44, GLM-5.3 (max) at 45, and MiMo-V2.6-Pro at 46. While MiMo-V2.6-Pro is comparable in total size to ML4, GLM-5.3-Flash is about one-third the size at 320B parameters total and 18B versus ML4’s 49B–52B active. Unlike the rest, ML4 isn’t open yet: Mistral says its weights are due “by the end of the month.”

Cost is a different story. ML4 comes in at $1.13 per AA task at list price, or $0.57 at the 50% discounted launch price. This compares to $0.13 for MiMo-V2.6-Pro and $0.25 for GLM-5.3-Flash. Although comparatively expensive, ML4 scored what AA calls its “strongest result” and Mistral calls “the highest of any model” with an 81.7% score on CyberGym-E2E-AA, compared with GLM-5.3-Flash’s 74%, from a model whose post-training run Mistral claims is “still in flight.”

While the score is impressive, it is only one of three tests in the Cyber Index; ML4 only scores 16% in DeepsecBench-AA and 51% in CWE-Bench-AA. However, AA’s projection is that, once the weights ship, it “will rank among the top three open weights models on the Cyber Index.” This leaves the door open for ML4 to apply to narrower tasks.

Mistral bets on this, saying ML4 is “state-of-the-art among open models” on enterprise workloads such as “cybersecurity, finance and law,” and that in visual grounding, where its 1.6B-parameter vision encoder comes in, it can go as far as “surpassing even frontier closed models.” On AA’s Cyber Index, Mistral says, ML4 ranks “among the top five models globally,” and it solves 93% of Cybench, a set of 40 challenges drawn from security competitions.

CEO Arthur Mensch went further in Abu Dhabi, saying the model is “above the Chinese models on certain aspects, including cyber,” Reuters reported. Mistral’s other figures include DeepSWE v1.1 at 61.7%, Coding Agent Index at 49.8%, AutomationBench at 59.9%, and Dense 200 at 42%; AA’s own chart puts ML4 at 27% on Terminal-Bench 4.0.

ML4 does, in fact, beat OpenAI’s closed GPT-6 Luna (max) on CyberGym-E2E-AA, but Luna matches ML4’s 38 on AA’s index at about one-sixteenth of the cost per task ($0.07). Mistral says frontier models such as Claude Opus 5.5 and GPT-6 Astra “score near zero on the same test because they refuse to perform the task,” and AA’s chart marks both as declining some tasks on safety grounds.

Outside of that test, Mistral says ML4 also beats DeepSeek V4 Pro 0813 and Qwen3.8 Max on the Coding Agent Index; per AA, it is cheaper than the latter. Overall, AA’s index-vs.-cost chart puts ML4 below the Pareto line and just outside the “Most attractive quadrant.”

The cost comes from needing 200 million output tokens to run AA’s index, against a median of 81 million. As for speed, ML4 generated 116.1 tokens per second, with 1.46 seconds to the first token. This puts ML4 at about 33% faster than its tier median and 2.6x faster to the first token. AA’s speed is measured on Mistral’s own API, which Mistral says runs on the same hardware the model used for training.

ML4 was “trained from scratch on 3,800 NVIDIA Grace Blackwell GPUs in Mistral’s own datacenters in Europe,” the company said. This is up from 3,000 Nvidia H200s used to train Large 3, released last year, which was smaller at 675B total parameters with 41B active. The training occurred over “roughly two months,” as reported by VentureBeat.

Mistral raised a €3 billion Series D in what the company calls “the largest equity round ever raised by a European technology company” in its post. Mistral Compute has planned for 18,000 Grace Blackwell chips, as announced with Nvidia last year. Mistral says it is “significantly scaling up our compute capacity in our own European datacenters,” and that ML4 will become the base for “a new generation of specialized and optimized Mistral models.” The model’s open weights, once released, will be able to run on a private cloud or on-premises.

The weights, architecture details, and additional benchmarks are expected before the end of October. Artificial Analysis lists the preview as proprietary until the weights arrive. Once it does, the model may be re-scored, and Mistral’s claims will be held to task. For the first two weeks following release, ML4 can be used via API at a discounted rate.

While European models still have a lot to prove, based on previous performance and ML4’s showing with an 8-point gap versus a similar-size Chinese model that’s also a lot less expensive, Mistral has demonstrated resolve and produced a model with, at least, niche application, as the basis to eventually close that gap.

SpaceX is reportedly looking to raise $40 billion to acquire Nvidia AI processors in a bid to expand its compute capabilities, reports the Financial Times. The financing could combine $10 billion in bank loans with $30 billion in investment-grade debt and may be completed in 2027, though discussions are still at an early stage and there is no guarantee that a transaction will materialize. If it does, then SpaceX's xAI will get $40 billion worth of Nvidia Rubin generation hardware, which includes AI accelerators.

Although Bloomberg reports that the proposed financing would be used specifically to purchase Nvidia AI accelerators, since xAI has traditionally bought servers or rack-scale AI solutions from Nvidia and its partners, it is likely that the company will continue to do so rather than opt to get accelerators and then build AI servers itself. Assuming that xAI indeed gets Vera Rubin NVL72 systems at around $8 million per unit, then for $40 billion it is going to get approximately 5,000 or so machines with roughly 360,000 Nvidia Rubin AI accelerators inside. At this point, it is unclear whether $40 billion is xAI's entire new compute capacity budget for 2027, or if the company plans to spend more.

The scale of xAI's Nvidia deployments is already substantial. Elon Musk said last month that xAI's Colossus 2 cluster currently houses 110,000 Nvidia GB200 accelerators and 440,000 GB300 devices.

SpaceX intends to obtain approximately $10 billion from banks and raise another $30 billion on the investment-grade debt market. Apollo Global Management is expected to take the leading role in arranging the financing, while bond manager Pimco is among a limited number of lenders discussing participation, the report claims. The transaction is currently expected to close sometime in 2027.

Apollo already has considerable exposure to this market and has already participated in financing the buildout of Elon Musk's AI infrastructure. In August, Nvidia teamed up with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR to establish financing platforms designed to attract more than $500 billion in third-party funding for AI infrastructure. Bloomberg reports that Apollo had previously arranged a $7 billion financing deal with Musk's xAI that financed Nvidia hardware intended for the company's Colossus 2 data center near Memphis.

No parties mentioned in the story commented on the potential deal.

Two former Groq engineers are suing Groq’s former board and former CEO Jonathan Ross, alleging the board handed over employees and company technology to Nvidia in a $20 billion deal. The proposed class action, filed in Delaware’s Court of Chancery by Benjamin Serebrin and Joshua Rubin and unsealed Monday, alleges the deal, with a $17 billion license fee for all backers and a separate $3 billion Nvidia stock pool for engineers who transferred, kept billions from other shareholders. Meanwhile, Groq is planning to deploy its own technology, now Nvidia’s Groq 3 LPX, the Financial Times reports.

Nvidia’s annual report (10-K) books $14.4 billion of the license as goodwill “primarily attributable to the workforce and future development of the licensed technology” plus $2.5 billion for the technology itself. The plaintiffs concede that “no Delaware decision has directly answered the question this case raises,” but the deal is already under a reported Department of Justice (DOJ) inquiry. The lawsuit’s outcome has implications for future license-plus-hire deals. Groq told CNBC the suit is “meritless” and said its licensing agreement with Nvidia “delivered exceptional value for Groq, our investors, and our employees.”

The deal involved both the LPU technology and the people who built it, including Ross, a former Google TPU engineer who founded Groq in 2016. The LP30 has 500MB SRAM per die and 150 TB/s bandwidth, uses Samsung’s 4nm process technology, and is rated by Nvidia at up to 1.23 FP8 PFLOPS. A single LPX rack has 256 LPUs with 128GB SRAM, 40 PB/s, and 315 FP8 PFLOPS in aggregate, as a decode co-processor for Vera Rubin NVL72. The LP30 appears to be the next-gen LPU Groq roadmapped in August 2023 on Samsung’s SF4X process with its first-gen Tensor Streaming architecture.

During Hot Chips in August of this year, Igor Arsovski, Groq’s former chief architect and now Nvidia VP of hardware, called the occasion “a pinch me moment for the Groq team that’s now integrated into the Nvidia group.” The rack was already in production at the time, Arsovski said. This supports the complaint’s factual premise, but does not answer the legal question about whether more is owed.

Nvidia’s 10-K notes indicate that “no customer contracts, existing products, or equity interests were purchased” with “$13.0 billion paid at closing and $4 billion, inclusive of imputed interest, payable within one year.” The goodwill number, as reported above, is about 85% of the $16.9 billion Nvidia booked and around 5.8x the value of the technology asset. Nvidia put significantly more value on the LPU design team and the technology’s future development than on the design itself.

Groq said when the deal was announced that it would continue “to operate as an independent company,” and it joined the Nvidia Cloud Partner program in August. Also in August, “Groq LLC” had a $350 million Series A and a $3.5 billion valuation after raising $650 million in June. Before the deal, in September 2025, the valuation was put at $6.9 billion after a $750 million round. Groq says it now operates 13 data centers and expects to scale from 54 to 200+ MW in 2027. Nvidia was slated to participate in the Series A, which Groq says will support those seeking “clusters of NVIDIA accelerated computing.”

Groq also said in August that it is “among the first adopters of NVIDIA Groq 3 LPX” with Nvidia’s Vera Rubin NVL72, deploying with Dell. Those racks are built on the LPU technology Groq licensed to Nvidia, and Groq claims to be “the only team with hands-on experience operating LPUs in production at scale.” The Financial Times said Groq “pivoted entirely to AI cloud computing, dropping its chip design efforts.”

The suit alleges that the Groq board was conflicted, failing to secure the best terms for every shareholder and denying some of them a vote on the deal. Specifically, it says Ross and other top Groq employees were allowed to “take a discount on those shares and be paid separately for following the technology to Nvidia.” According to the suit, Nvidia hired “nearly all” of Groq’s engineers, as many as 200. However, the final payout is alleged to have ignored potential upside and synergies with Nvidia.

The plaintiffs also say that because Nvidia didn’t buy Groq outright, the $17 billion license payment was taxable as income at Groq, and that the Series A valued the remaining Groq above the price paid to the shareholders who were bought out. They say four “conflicted funds” on Groq’s board, BlackRock, Social Capital, Infinitum, and Disruptive, which are not named as defendants in the case, engineered that gap and profited by staying with Groq after the buyout.

As reported by Axios in December 2025, though, sources close to the deal said that most shareholders would get per-share payouts “tied to the $20 billion valuation.” Around 90% of employees would join Nvidia, with vested shares paid in cash and unvested shares paid in vesting Nvidia stock. Staff who stayed with Groq would be paid for vested shares and get a package including “economic participation in the ongoing company.”

As for the DOJ, the New York Times reported in September that the department sent Nvidia a formal request for information with a fine being possible. Nvidia said at the time that the Groq story was “a prime example of the American system working as designed.” The deal did not go unnoticed by some senators who, in February, put it under the “reverse acqui-hire” deal label. Federal Trade Commission Chair Andrew Ferguson said in January that the agency is “beginning to examine these acqui-hires to make sure they are not an attempt to get around” its merger review process.

The litigation comes not long after Nvidia’s agreement to acquire Hugging Face (HF) a month ago, which included a purchase price and equity for HF employees who join Nvidia, a similar split. The deal is waiting on approval, but unlike the Groq deal, it is an outright purchase of the company. When the Groq deal was announced, Nvidia CEO Jensen Huang told employees in an email, obtained by CNBC, that while Nvidia was adding Groq staff and licensing its IP, “we are not acquiring Groq as a company.”

The legal question on the Groq deal remains open, and more details will come out as the case proceeds and the defendants respond. As for the LPX customers, Nvidia calls Nebius the first AI cloud to adopt LPX and says the racks will be online there later this year. Looking ahead, Nvidia has the LP35, with NVFP4 support, alongside the Rubin Ultra and LP40 for Feynman on its roadmap.

At an event in San Francisco, Microsoft and Nvidia teamed up to show off the RTX Spark chip, along with the Surface Laptop Ultra and new features in Windows 11 that use the new system-on-a-chip's capabilities. The device, along with the Surface RTX Spark Dev Box and the broader Spark ecosystem is available for pre-order today, with devices shipping on October 16. You can see our full breakdown of retailers and listings in our dedicated pre-order coverage here.

Microsoft CEO Satya Nadella, Nvidia CEO Jensen Huang, and Windows and Devices head Pavan Davuluri took turns on stage.

"We've reinvented the personal computer completely," Huang said.

Nadella described the event as one that would be building the "frontier ecosystem," anchoring it in Agent 365, Microsoft IQ, and Copilot. Davuluri describes Windows 11 as being the most secure platform for AI agents, highlighting containment for agents.

Windows 11 and Surface Laptop Ultra

The Surface Laptop Ultra will start at $2,599, while the Surface RTX Spark Dev Box will begin at $5,999. Both are available for pre-order. They, along with PCs from Asus, Dell, Lenovo, HP, and MSI will ship next Friday, Oct. 16.

Davuluri showed the Surface Laptop Ultra running Github Copilot and playing Gears of War: E-Day at 60 frames per second. He said that "hundreds" of AAA titles and indies will be available day one, and that Call of Duty will come to RTX Spark in 2027.

The 4.5-pound system has the brightest peak HDR on a laptop, he said. The system also features a magnetic USB-C charger it calls "magnetic connect." The connector plugs in, similar to Apple's MagSafe or the old Surface Connect ports, except using a standardized port.

An impressive video demo using multiple local and cloud models across Blender, Photoshop, ComfyUI, and Unreal Engine to make a workload that took hours instead of days was impressive, but also highly produced.

Microsoft compared the Spark to the M5 Pro in a MacBook Pro, promising performance up to 2.1 times faster to first token, 4.3 times faster image generation, and 6.2 times faster video generation.

On Windows 11, there will be new quick actions in the Search menu in the Task Bar, making quick changes to settings, taking screenshots, or arranging windows using natural language. The Taskbar will also launch Copilot if the request calls for it, and you can jump from Search to Copilot.

Davuluri also described local models running from other vendors, including Hermes, OpenClaw, and Perplexity, performing a demo with Perplexity Computer. He also stated that Meta's Muse will come to Windows. There will be a new setup for Windows AI agents to make them easier to configure.

Davaluri also announced DGX Spark and Station for Windows, a "supercomputer" to bring "last quarter's frontier to your desktop." He showed off desktops from HP and Dell that will come "later this year." Up to this point, both have been exclusive for Linux.

Copilot and Local Agents

Microsoft's Jacob Andreou detailed the recent launch of the new Copilot, which is now getting access to local files, local models, and the ability to take actions on account of the user. It will still use cloud models, but will switch to local models when privacy is called for.

In one example, Andreou was grabbing tax data for his accountant. The system used a mix of local and cloud models to search for documents, rename them, and zip them into a folder. Copilot also wrote an email to the accountant to send on his behalf, which he approved. These features, he said, are coming over the next few months.

Containers, Trust, and Hybrid Ingelligence

Daviluri focused on the technical details of agents, including Microsoft Execution Containers, which is moving to general availability. These are sandboxes that are integrated with the OS to decide what data is shared with agents.

Windows will also require agents to attribute their actions to themselves. There will be IT actions for enterprises that want to set agentic rules once and use them across their fleet of devices.

Microsoft's Divya Venkataramu demonstrated this system, showing how she had access to documents even when agents didn't.

Davuluri also described hybrid intelligence, a mix of agents running locally and in the cloud to reduce token budgets. Microsoft's HydraFusion, which balances local and cloud agents, is coming from GitHub to Windows on October 15 for GitHub Copilot, the CLI, and VS Code, and will use new models like MAI Code 1.1 Flash and Nvidia's Nemotron. Using advanced quantization, DeepSeek V4 Flash can be used at 1.6 bits to run locally at 60GB of memory, he said.

Windows ML will serve as the runtime to deploy models across GPU, NPU, and CPU. Llama.cpp will come to Windows ML to give developers chances to experiment with new models at launch.

Microsoft's Kayla Cinnamon demonstrated Hybrid intelligence on a Surface RTX Dev Box by prompting GitHub Copilot to detect unused codes, packages, and feature flags to clean up code. With an Efficiency mode, Copilot selects a model automatically based on how complex a task is. A more complex prompt used GPT-6 Luna, while separate sub-agents ran locally.

EV charging firm Xeal plans to use its existing U.S. network to deploy the “world’s first edge inference compute network using idle EV charging capacity.” The big idea is that it already has 1,600+ locations and 200MW of permitted, installed electrical infrastructure, so it aims to “deploy over 100,000 NVIDIA GPUs” at these sites. Thus it has developed Laitent Pods (pictured), with each containing up to 48 Nvidia Hopper or Blackwell Ultra GPUs. We hope charging location security will be adequate to keep thieves away from these cabinets, as they are probably worth $2M+ each.

Nikhil Bharadwaj, co-founder and CEO of Xeal, explained that Laitent Pods can deliver inference compute today while new data centers are still being built. “Laitent is the fastest way to bring new compute online, tapping already permitted and grid-connected, yet idle energy capacity, located right where it's needed for ultra-low latency, with no water hookup requirements,” highlighted the exec. Grid interconnect is said to be one of the trickiest things for those looking to bring new compute online, but Xeal’s EV charging sites, which “typically operate at less than 10% of permitted capacity,” can deliver processing power via a distributed network across a metropolitan area.

An Nvidia rep also praised the upcoming Laitent Pod network for being an innovative way to bring low-latency inference closer to users while making better use of infrastructure that has already been built.

In the rendered image from Xeal, a Laitent Pod looks quite a lot like a broadband fiber cabinet that you might have on your street. A pod will boast the following specs/features:

• Powered by idle EV charging capacity
• Up to 48 Nvidia Hopper or Blackwell Ultra GPUs
• Self-contained independent cooling, no water hookup required
• Quiet operation at less than 65 dBs (comparable to a washing machine)
• Fixed rental or ancillary revenue for site owner
• Adds up to $1 million in property value for little-to-no investment
• All power bills covered as a tenant pass-through
• Potential savings for EV charging customers
• Built to withstand the outdoors, NEMA 4 enclosure
• Installed and running in hours

It claims that the location of its pods around metro areas, a feature dubbed Metro Edge, offers sub-20ms latency, a significant speedup over inference located in a distant data center.

We’ve previously reported on EVs and distributed computing, but in a slightly different context. Last year we discussed Elon Musk’s proposal to make use of the untapped asset of the millions of Tesla cars out there. He called it “100 gigawatts of inference” that could be used when the vehicles are “bored.”

Meanwhile, we must remember that EV charging stations are already being targeted by thieves. We reported about a ‘plague’ of electric car charger cable thefts driven simply by the copper scrap value of the hefty cables used at the sites. Imagine the security that would be required if/when Laitent Pods are deployed, stuffed with extremely expensive GPUs. 48 Blackwell GPUs would be worth around $2,000,000 according to 2024 figures – so surely much more by now.

The first Laitent Pod will be online by the end of the year, says Xeal.

Nvidia has increased the price of the Shield TV Pro from its original $199.99 launch price to $299.99. This makes the long-running Android TV streaming device $100 more expensive despite receiving no major hardware refresh. The new price is listed on Nvidia’s website, and the company blames rising component costs driven by AI for the increase.

An Nvidia spokesperson told Ars Technica, “Starting October 2, SHIELD Pro will be priced at $299. The cost of components, including memory, has increased substantially across the industry.” The price increase for the Shield TV Pro also comes alongside the discontinuation of the standard Nvidia Shield TV. Nvidia has reportedly stopped producing the base model, leaving the Shield TV Pro as the only Shield-branded streaming device the company offers.

Despite being seven years old, the Shield TV Pro remains somewhat relevant, as it serves as more than a basic TV streaming box. Powered by the company’s Tegra X1+ processor, 3GB of RAM, and 16GB of internal storage, the device includes two USB 3.0 ports, Gigabit Ethernet, and support for external storage drives, making it particularly useful for users with local media libraries and Plex setups.

It also offers support for 4K HDR playback at 60 FPS and a wide range of advanced video technologies including 4K HDR, Dolby Vision HDR/HDR10, and AI-enhanced upscaling. It also supports high-res audio, including Dolby Digital, Dolby Digital Plus, Dolby Atmos, DTS-X surround sound, and a wide range of audio formats.

For gamers, Nvidia’s GeForce Now cloud gaming service and support for Android games give the Shield TV Pro a slight edge over conventional streaming TV boxes. It is also one of the few Android-based TV streaming devices that still receives software updates, despite being on the market for nearly seven years.

With that said, it appears to be increasingly difficult to find, with the device out of stock at most retailers. We spotted a Best Buy listing for the 16GB model at $249.21, about $50 less than Nvidia’s revised pricing. The listing is currently out of stock, so it's unclear whether the lower price is a temporary discount or the retailer’s standard pricing.

Over at Tom's Hardware Premium, we've been publishing a slew of stories centered around our themed AI Chip Design Week, where you can learn more about how artificial intelligence is reshaping the chipmaking world. Each article in our series of stories is free to access for a limited time, and we're extending the free period over the weekend until Monday, October 5, so you don't miss out.

Headlining this week is our interview with OpenAI hardware VP Richard Ho, talking all things Jalapeño, the Broadcom co-developed inference ASIC that the company debuted earlier this year at Hot Chips 2026. The interview dives into how the company used AI within its design processes, beating Nvidia on efficiency, and demystifying common misconceptions like only being optimized to run OpenAI models. This lengthy read is a full, unredacted transcript of our session with Ho, so don't miss out.

• OpenAI Jalapeño design interview transcript — hardware VP Richard Ho explains how AI-assisted design may shape the future of inference ASICs

But how close is AI to designing the very silicon that it runs on? We explore the concepts, products, and everything else shaping the industry in our pair of exhaustive primers on the subject, covering the companies seeking to make the chips themselves.

We've also broken down all of the tools currently in use, with comments directly from Synopsys, and a breakdown of all claims from vendors currently using AI within their chip design workflows. Additionally, during the week, Synopsys debuted its 'Autopilot' platform for its AgentEngineer, specifically designed to aid directly in chip design workflows.

• Silicon is starting to design silicon — how AI is being used in chipmaking, from EDA tools to OpenAI's Jalapeño and beyond
• The state of agentic AI in chip design tools in 2026 — Cadence, Synopsys, and Siemens all pitch autonomous engineers
• Synopsys debuts Autopilot platform for developing chips autonomously using AI — New AgentEngineer platform is poised for 'general availability' by the end of 2026

But all of these new tools open up an uncomfortable reality. With AI's growing role in the chip design world, at what point are patented IPs in danger? This question is explored in our detailed report, where we ask experts like Domenec Forte, professor of electrical and computer engineering at the University of Florida, and Simon Moore, professor of computer engineering at the University of Cambridge, to learn more about a looming issue that could broadly impact the AI chip design market.

• AI's chipmaking frontier may face patent infringement hurdles as autonomous tools take over — 'AI can spread a copied design or infringed patent across thousands of chips before anyone notices,' says expert

With our AI chip design week in full swing, we've also been publishing our regular remit of detailed news analysis stories, exclusively for subscribers, which kicked off with a curious discovery: Intel has reportedly been granted a patent that embeds MicroLEDs in chip packaging, which could enable optical interconnects through TGVs, opening the potential for glowing RGB CPUs.

• Intel patent embeds MicroLEDs in chip packaging — technology may enable embedded optical interconnects through TGVs

Next up, blockchain-assisted cyberattacks are on the rise and have primarily originated from adversarial actors linked to Russia, North Korea, and China. The groups reportedly leverage open-weight LLMs to develop sophisticated cyberattacks against unsuspecting users.

This is a topic highlighted this week too by Anthropic, which published a report on how Zhipu AI's GLM-5.3 can be used to generate harmful content, with claims that the Chinese frontier open-weight model also has advanced hacking abilities, nearing similar levels to Claude's own Mythos AI model, showcasing different methods of getting around the model's guardrails, or disabling them entirely.

• Blockchain-assisted cyberattacks surge fivefold, driven by Iranian and North Korean state actors, Russia-linked groups — open-weight LLMs are linked to an increase in attacks
• Anthropic claims popular Chinese AI model has Mythos-class hacking abilities — frontier red teaming report details weak safeguards on open-weight AI

The AI industry continues to march on, with Nvidia releasing the Open Agent Safety Platform this week, in collaboration with hundreds of additional companies. The program aims to embed OpenShell sandboxing with BlueField-powered sentry hardware to monitor and quarantine potentially rogue agents. The move likely comes following calls for a slowdown in AI development and high-profile security concerns.

However, the costs for intelligence continue to drop, as more capable, cheap models are released on a weekly basis. While we didn't get the onslaught of model releases this week as we did last week, which saw the launch of Claude Sonnet and Opus 5.5, and OpenAI's GPT 6.1-Sol and Luna, a new report from Epoch AI claims that costs are getting just under 50% cheaper every quarter, outpacing even Moore's Law.

• Nvidia launches Open Agent Safety Platform to restrain rogue AI agents — new hardware-and-software security stack can quarantine agents in milliseconds
• The price of AI is crashing faster than the rate of Moore's Law, report suggests — intelligence costs are in freefall, outpacing comparative technologies like compute, DNA sequencing, and lithium batteries

Remember, we're keeping our AI Chip Week articles free to access over the weekend, but to read our other stories and gain access to Bench, you'll need to purchase a subscription. Your support goes a long way in helping us to publish deeper, more insightful articles that might otherwise fall outside of our regular areas of coverage.

A California man has been arrested for smuggling over $300 million worth of restricted Nvidia chips to China in violation of U.S. export controls. According to a U.S. Department of Justice (DoJ) press release on October 1, the defendant — Greg Lui, also known as Yiu Kong Lui — and unidentified co-conspirators smuggled the chips in high-end servers from 2023 to 2024. He allegedly first exported the chips to Malaysia and Singapore, jurisdictions where the restrictions don't apply, before shipping them to buyers in China, including the Chinese government.

Authorities say the defendant, owner of California-based company Earthmade Computer Inc., and his co-conspirators had used false paperwork to misrepresent the chips’ final destination, purporting that the controlled chips would be going to permissible end users and end destinations that did not require an export license. “This defendant allegedly used false paperwork and shipments through third countries to smuggle more than $300 million in export-controlled computer servers to China,” said First Assistant U.S. Attorney Bill Essayli for the Central District of California.

The DOJ has charged Lui with “one count of conspiracy to violate the Export Control Reform Act and the Export Administration Regulations, one count of outbound smuggling, and one count of conspiracy to commit money laundering.” If convicted of all charges, the defendant could face up to 20 years in prison. He was arraigned and made his initial appearance on October 1. The FBI, the Department of Commerce’s Bureau of Industry and Security Office of Export Enforcement, and the Defense Criminal Investigative Service are jointly investigating the case.

The incident is the latest in a series of indictments over the illegal export of restricted AI accelerators and GPUs to China. Last month, nine people were indicted in Taiwan over the illegal export of Nvidia B300 GPUs to China. The United States and its allies had placed broad export restrictions on several AI chips and the hardware required to make them in an effort to maintain its current lead over China in the AI race. China, meanwhile, insists the move has only bolstered its local AI ecosystem. However, the demand for powerful Nvidia GPUs remains sky-high in China and has created a teeming market for smuggled hardware.

Speaking on the Liu incident, Special Agent in Charge John E. Helsing of the Department of Defense Office of Inspector General Defense Criminal Investigative Service (DCIS) Western Field Office said, “Preventing the illegal export of controlled technology is critical to protecting our national security. These technologies provide core advantages to U.S. forces, and ensuring they remain safeguarded prevents adversaries from exploiting them. Today’s action underscores our commitment to protecting advanced defense capabilities and enforcing the laws that keep them out of the hands of those who may threaten the United States.”

Nvidia is tailoring its popular DGX Spark platform and the GB10 SoC to better fit the realities of today's AI models and the broader silicon supply crunch. The company is introducing a 64GB version of the Spark that's meant to be more affordable to local AI trailblazers who just don't need 128GB of RAM.

Back when we first began exploring the capabilities of the DGX Spark and similar systems, the general assumption was that lots of RAM would be necessary to hold the most intelligent models one might want to run, and so unified memory systems built around AMD's Strix Halo, Nvidia's GB10, and Apple's M-series chips could all be configured with 128GB of memory or more.

But the AI field moves fast. Highly intelligent dense models like Qwen 3.8 27B can now fit comfortably within 32GB of RAM (albeit with limited context), so the original Spark's 128GB of memory isn't essential for local inference alone. And skyrocketing RAM prices mean that a chip that’s permanently paired with too much costly LPDDR5X is more of a barrier to entry than an asset. So a more affordable Spark with less RAM makes sense for those who need the capabilities and supporting software stack of Nvidia's GB10 Superchip and can live with less memory.

A "more affordable" DGX Spark is of course relative in today's market. 64GB GB10 systems from Acer, Asus, Dell, Gigabyte, HP, and MSI are slated to start at $4999 when they launch on October 23. Given the ever-shifting prices of memory and storage right now, they might not stay there for long. Assuming you can find a 128GB GB10 system in stock, you can expect to pay roughly $7000 to $9000 for one right now, far above even Nvidia's adjusted MSRP.

Memory capacity change aside, 64 GB Sparks will retain the same ConnectX 7 RDMA NIC as their 128 GB stablemates, meaning that they can still be clustered to boost memory capacity and inference performance if a user does eventually outgrow the bounds of 64GB of RAM. The same 20-core Arm CPU complex from the original Spark carries over unchanged, as does the 273 GB/s of shared memory bandwidth for the CPU and GPU.

To help make use of that ConnectX 7 NIC, Nvidia is also making it easier to join Sparks together with a new software tool, the Nvidia Sync Cluster Assistant, that automates the process of yoking those systems together. (Sync is a remote connectivity and management app for the Spark that allows users to use their Spark’s AI horsepower from other Macs and PCs.)

That tool supplements a variety of community-developed utilities that have sprung up over the past year to make clustering a more seamless process, as we used in our experiments with clustering two Dell GB10 systems together earlier this year.

Nvidia is also adding a feature to Sync called Model Launcher that automatically downloads and starts models like the aforementioned Qwen 3.8 27B across one or more systems and links them to the OpenCode browser-based coding agent so that users can start using their local token factories right away for agentic workflows.

The 128GB DGX Spark and Spark-alikes will remain available for those who need space for larger models and for scaling across clusters, or for more memory-hungry local AI work like model fine-tuning, so there’s basically no downside to this addition to the DGX Spark lineup. In today’s volatile market, it’s good to have options, and a 64 GB Spark gives local AI enthusiasts another choice to better fit their budgets and workflows.

OpenAI’s new Jalapeño ASIC is being deployed internally alongside AMD EPYC Turin hosts, each with 1.5TB of memory. SemiAnalysis described the rack-scale deployment of Jalapeño following the reveal of the chip, which we asked Richard Ho, VP and Head of Hardware at OpenAI, about in an interview. Ho told us that the decision to use Turin was “pragmatic,” describing Nvidia’s new Vera CPU as “a little bit behind… on that maturity level.”

“The way we approached that design was really in terms of de-risking and being able to do that design fast. Vera, as a standalone, is a little bit behind on that maturity level. The Turing device is strong. It did what we needed to do, and partly our partners had some experience with it,” Ho told Tom’s Hardware Premium. “For the Jalapeño program, we were trying to make very pragmatic decisions. We wanted to be aggressive on the goals of the performance and the cost, but we didn’t want to take unnecessary risks. That felt like a good design decision that would fit within the parameters of how we make these design decisions."

There are many Arm-based CPUs on the market, many of which are deployed internally at different hyperscalers, such as Google Cloud’s Axiom and AWS’ Graviton, but Vera and Arm’s own AGI have been described as agentic CPUs, purportedly accelerating the complex reasoning involved in agentic loops compared to their x86 counterparts from AMD and Intel. We asked Ho about this dynamic, and why Turin was the right choice given the close working relationship between OpenAI and Nvidia, who responded with the quote above.

Arm has long touted the virtues of the AArch64 ISA compared to x86, going as far as to claim that its own AGI CPU provides more than twice the performance of modern x86 platforms. That claim is based on internal estimates, not real benchmarks, however. The company has been naturally bullish on AGI’s adoption in the market, though even with an impressive $2 billion in commitments, analysts say the market penetration will still be in the low single digits after two years.

With the explosion of server CPU demand this year and evolving agentic workloads, Nvidia has been at the forefront of messaging, comparing its Arm-based Vera to the x86 competition, boldly claiming a 1.8x improvement over a competing AMD Turin chip; a figure that’s extracted from an overall benchmark suite showing Vera just 3% ahead of AMD’s EPYC 9755.

Many semi-custom Arm designs, from Graviton to Azure’s Cobalt, have focused on core density and efficiency for cloud workloads. Vera and AGI are a shift toward messaging against peak performance. In early Vera benchmarks, Nvidia’s CPU looks impressive, though we’ve yet to see the chip in action in a wide variety of workloads, much less benchmarked against AMD’s upcoming Venice CPUs or Intel’s Diamond Rapids.

What’s interesting about OpenAI’s decision to use Turin is that it’s one of Arm’s deployment partners for AGI, as well as among the list of customers “exploring the Vera CPU,” according to Nvidia. OpenAI, other frontier labs, and hyperscalers keep a wide variety of hardware in their fleet. Pairing Jalapeño specifically with Turin came down to reducing risk, with Ho pointing to the maturity of the platform.

Broadly, x86_64 is more mature than AArch64; ARM has been around since the mid-1980’s, though a proper 64-bit extension didn’t arrive until 12 years after it was introduced on x86. And although Arm has worked its way into data centers over the past two decades, there’s still a much deeper x86 foundation reaching back several decades.

It doesn’t seem Ho’s comments were specifically on the delineation between AArch64 and x86, however. Rather, the comments are focused on the dynamic between Turin and Vera, with the latter being Nvidia’s first foray into a custom CPU core for the data center. Although the early benchmarks of Vera are impressive, it nonetheless represented an unnecessary risk for OpenAI’s rack-scale Jalapeño deployment.

Ho’s comments about OpenAI’s partners having experience with Turin expose where the thinking for the hardware team was at, as well. There are some differences in the practicality of servicing the host system between Turin and Vera, most notably the fact that Vera is board-mounted while Turin chips are socketed; swapping chips isn’t common in a server regardless, but it’s easier with Turin.

Given that OpenAI has a fleet of hardware available, it’s possible Jalapeño will be deployed with a different host system in the future, potentially paired with Arm’s AGI or Nvidia’s Vera. For now, however, Turin was the right choice.

Nvidia has just launched the Nvidia Open Agent Safety Platform — an open software platform and reference system design — to govern and secure autonomous AI agents. Announced on September 28, 2026, the platform is designed to establish strict security barriers outside of AI models’ application layer, preventing agents from escaping their sandboxes, executing unauthorized code, gaining unauthorized access to critical infrastructure, or bypassing guardrails.

The launch follows months of calls for AI regulation from several industry players, which intensified in September after several reported incidents in which AI models broke out of their test environments and went rogue. A recent flurry of such incidents has prompted calls to slow AI development, with OpenAI outright halting the training of new models. One former Anthropic and OpenAI researcher even declared that people building frontier AI “earnestly believe that it could kill us all by the end of the decade. In a somewhat surprising move, leaders of the companies developing these AI models have joined the calls to regulate AI or slow development.

However, not everyone agrees with this approach. Nvidia CEO Jensen Huang has consistently pushed back against government-mandated regulation, broad restrictions, or treating AI safety as a “doom theory”, openly criticizing apocalyptic warnings from competitors like Anthropic and OpenAI as “odd”. He argues that AI safety is an infrastructure problem with concrete physical parameters, not an abstract, speculative issue that requires policies. Therefore, the solution, according to Huang, is better engineering.

The Nvidia Open Agent Safety Platform appears to be the physical manifestation of that exact philosophy. So, how exactly does the platform work? Who is it for? And is it really the answer to the AI safety problem that is causing growing concern across the industry? Much of the industry’s debate over approaches has focused on curtailing self-acting rogue agents, but hardly touches on the safety implications of AI being a powerful tool in the hands of threat actors.

What’s all the fuss about?

The speed of AI’s development has prompted concerns about whether sufficient guardrails are in place to curb the risks of such a powerful technology. One aspect of these concerns — rogue agents — has been validated by several incidents in which AI agents broke out of their roles during testing and executed unauthorized actions. OpenAI agents have gained unauthorized access to various government websites, including the Securities and Exchange Commission and the Census Bureau websites in the U.S., as well as an Australian health and social payments portal.

In several other episodes, models have bypassed guardrails, set up message boards, escaped sandboxes, hijacked websites, self-prompted, uploaded user data without permission, and secretly communicated with each other. A recent Axios report claims that leading AI labs are currently investigating tens of thousands of such incidents, most of which happened during testing and experimentation. Several rogue incidents have also occurred outside test environments. For example, earlier this year, a Claude-powered AI coding agent deleted a company's entire database in 9 seconds.

These incidents have culminated in growing calls for regulation across the industry. Anthropic CEO Dario Amodei recently published an essay that centers around calls to “slow the pace” of AI development and the importance of regulation, warning that a potential AI-powered botnet swarm could take over the entire internet. In its recent IPO prospectus, Anthropic listed “existential risks to humanity” as one of its risk factors, dedicating one third of the 261-page document to describing what could go wrong.

Nvidia’s Jensen Huang disagrees with both such apocalyptic predictions and the use of regulations as a solution. While he doesn't dispute the critical need for guardrails, he argues that better engineering, not broad legal regulations, is the right approach. Putting his money where his mouth is, Huang’s Nvidia has launched the Nvidia Open Agent Safety Platform.

The Nvidia Open Agent Safety Platform

Built in collaboration with about 100 industry partners, Nvidia's Open Agent Safety Platform “brings together industry, researchers, and public-sector organizations to set safer boundaries for AI Agents, share best practices and foster international cooperation to raise the bar for safer AI agent deployment.” It combines Nvidia OpenShell—an open-source secure runtime that sandboxes agents and enforces operator-defined policies—with Nvidia Sentry, an independent watchdog reference design that runs on Nvidia’s BlueField-4 DPUs and enforces security policies at the silicon level.

OpenShell sets sandboxed environments, outside of the model and agent harness, with kernel-level isolation to govern what an agent can see, interact with, and execute. Even if the agent breaks out of the model's boundaries, it cannot go beyond OpenShell’s. Sentry, on the other hand, uses hardware-level telemetry to continuously monitor agent behavior and isolate rogue workflows from outside the agent’s software environment. If an agent attempts to move beyond its software boundary, Nvidia claims that Sentry can quarantine and stop it in milliseconds.

The platform is aimed at developers and enterprises deploying increasingly autonomous agents across data centers, workstations, and even robotic systems, and can work with both open and closed models. While OpenShell is optimized for Nvidia Vera — a purpose-built CPU for agentic AI — it can also be extended to third-party compute platforms from Arm and Intel, as it's open-source.

Nvidia's industry partners in the initiative include AI Labs and frameworks, security and identity providers, enterprise platforms, and hardware and infrastructure companies, with several partners already incorporating the platform into their ecosystems. SpaceXAI is using the platform with Cursor coding agents and Grok models, while Anthropic is integrating OpenShell and BlueField with Claude Managed Agents to add another layer of control.

Scale AI is incorporating the technology into its agentic infrastructure for enterprise and government customers. Similarly, Salesforce and Nvidia have also integrated OpenShell with Slack, allowing users to view agent activity, audit events, and approve or reject requests for additional permissions directly from Slack.

SAP, meanwhile, is embedding OpenShell into its Joule Studio runtime, contributing engineering work to the project, while also working with Nvidia on interoperability standards through the Open Secure AI Alliance. Robotics companies, including Figure, Gecko Robotics, and Skild AI, are also building with OpenShell to add similar controls to autonomous systems operating in the physical world.

The broader picture

It's somewhat surprising that the strongest voices calling for regulation are the leaders of the very labs developing the AI agents. On one hand, having the people at the forefront of development call for restraint adds validity and urgency to the concerns. On the other hand, skeptics say it might all be part of a broader “self-serving” agenda that is part marketing for the models' capabilities, part a ploy to influence whatever regulations end up being made, and part an attempt to slow down China's AI development even further. In fact, Anthropic, OpenAI, SpaceXAI, and Google are now facing an antitrust lawsuit for agreeing to slow AI development, with the plaintiff explicitly calling the move “self-serving.”

U.S. President Donald Trump appears to strongly agree with this view, saying that a “sick conspiracy” was underway to undermine. Trump announced plans for an “AI force” that will “cherish” AI and watch over it. Jensen Huang also finds calls for regulation strange. He says he is not outright against regulations but calls the recent clamoring a “distraction.” Huang argues that you cannot rely on the AI model to regulate itself or pass alignment tests. If an agent encounters a bug, it will naturally try to bypass standard software code to achieve its goal.

Huang's advocacy, however, cannot be viewed as completely altruistic. Broad restrictions on AI or a halt in development will likely reduce sales for Nvidia, whose AI accelerators power most AI models. What's more, the company's CEO has always considered rogue AI as a cybersecurity and networking failure, and is now positioning the Open Agent Safety Platform as the required infrastructure solution for the entire industry.

AI’s extraordinary potential for society will only be realized if we solve AI safety,” said Huang. “As we continue to discover the frontier of AI capabilities, we must accelerate discovery at the frontier of AI safety. Safety and security require full-stack engineering. Nvidia Open Agent Safety Platform brings together industry, researchers, and public-sector organizations to share best practices, align on evaluation methods, and foster international cooperation. Together, we can raise the bar for global AI safety.”

None of these appear to address an arguably more concerning aspect of AI safety: extremely powerful tools falling into the hands of bad actors or being used in applications that blur ethical lines. For example, blockchain-assisted cyberattacks have risen 440% since the launch of Chinese open-source AI tools that do not restrict the generation of malicious code and lower the knowledge barrier to launching cyberattacks. Elsewhere, researchers used AI to create 16 new viruses that never existed in nature. While the specific study was controlled medical research, it shows that highly dangerous applications are possible. Experts worry that such studies are way ahead of necessary guardrails and regulations.

There's a huge deal on an MSI gaming laptop right now with a set of top specs, including the best laptop GPU out there, the RTX 5090. This MSI Stealth A18 AI+ laptop is $3,779, with 64GB of DDR5, 2TB SSD and a 12-core AMD Ryzen AI 9 HX 370 CPU, saving you $520 in total.

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While the mobile version of the RTX 5090 isn't the same as its desktop counterpart, you won't find a better GPU in a laptop that you can buy. It comes with 10,496 CUDA cores, along with 24GB of GDDR7 VRAM, which operates on a 256-bit memory bus. A peak 175W TDP is much less than the desktop's 575W power rating, but laptops are designed to be portable, so power is restricted.

It's interesting to look at how the pricing stacks up in the current market. This laptop will set you back $3,779 right now for an entire working system. The desktop version of the RTX 5090, right now, will set you back over $7,000 at the moment, at least for models in stock. These are two different beasts, sure, but PC gaming is getting to be very expensive, almost prohibitively so. A desktop PC with similar specs might set you back almost double the price you're paying for this laptop. It's clearly the better value option from a financial point of view, especially as it'll handle modern games at the best settings for several years.

The RTX 5090 is the real centerpiece of this laptop, but you're getting a stacked set of specs elsewhere. For the CPU, you're getting the AMD Ryzen AI 9 HX 370. This Strix Point processor has 12 cores, 24 threads, and a boost clock speed of 5.1 GHz. This is a Zen 5 CPU, with a power-efficient NPU for AI tasks.

No compromises elsewhere, either. You get a full set of 64GB of DDR5 RAM with this laptop, with speeds of 5,600 MT/s. That's a little slower, but capacity-wise, you're beating the RAMpocalypse with a whole heap of memory. Likewise, the 2TB SSD means you've got plenty of space for Windows alongside a lot of big game installations.

This is a big-screened laptop for gaming, and you're getting an 18-inch panel with a resolution of 3,840 x 2,400. That means you can game at 4K, with a 120Hz refresh rate to match the in-game frame rates without X. The mini LED panel supports HDR 1000, with 1000 nits of brightness, covering 100% of the DCI-P3 color space.

Wi-Fi 7 connectivity, along with 2.5Gb Ethernet, means you can max out any connection for lag-free gaming. These impressive specs are wrapped in a magnesium-aluminum alloy chassis in a midnight black colorway, with vapor chamber cooling under the hood with two fans and four exhausts.

The $3,779 sale price for this MSI Stealth A18 AI+ gaming laptop is a brilliant one in the current market. Rather than spending $7,000+ for the desktop GPU alone, this full-spec'd laptop with a 4K-adjacent display will give you everything you need to play at high frame rates at the highest resolutions for years to come.

The Call Center Doctors, a call center consultancy that builds and runs call floors for other businesses, rented a server with four Nvidia H200 AI GPUs to serve DeepSeek V4.1 Flash to its Claude Code agents in place of Opus 5.5, according to its test write-up. The write-up is aimed at anyone who has heard that DeepSeek is “80x cheaper” than Claude. Using its real coding mix, the firm's server topped out at around 213 tokens written per second at an on-demand cost of $440.88 per day, versus $184–$223 per day for the same work via DeepSeek’s API. The call center went back to Opus 5.5 at a lower price than the box would cost at on-demand rates.

DeepSeek’s API is priced at $0.15 per 1 million new input tokens off-peak, $0.003 cached input tokens, and $0.60 per 1 million output tokens. Peak costs are double. By comparison, Anthropic’s Claude Opus 5.5 is listed at $4 input and $20 output per 1 million tokens, with cache reads at $0.20. The consultancy used it through subscriptions and not the API.

The box’s performance was fine for one kind of work at a time in the test’s one-minute full-load runs. But the coding agents work mostly by resending the conversation, and 96% of what the agents provided the model was stale text, according to the consultancy’s logs. It takes about 1,000 old tokens for every new token written; the re-reads are cheap, but so numerous that the box was kept busy re-reading, leaving little of its time for writing new tokens.

Getting the model to run stably took five tries at 10 to 15 minutes of loading for each try. The box bills whether it’s busy or not, which is suboptimal. By our arithmetic, a standard-length month at the on-demand rate, $18.37 an hour, comes out to about $13,200. This is over twice the consultancy’s September Claude bill. Additionally, one box can only get through about 20 billion tokens a day, by the consultancy’s math, most of it re-reading, against the 51 billion the consultancy’s agents used on its busiest day.

The testing was aimed at anyone who has heard that DeepSeek is “80x cheaper” than Claude. One server running from Sept. 1–27 had 2.03 million model calls, 388.5 billion tokens read, 374.2 billion of them cached, and 393 million written, with 5,610 merged changes. The consultancy’s September Claude Code subscriptions came to about $5,500. The same 27 days of tokens on DeepSeek’s API would be between $3,500 and $7,000, depending on peak pricing, with our estimated average around $4,200 if usage is spread evenly across the week.

Set against Claude Opus 5.5’s list prices, the same tokens would come to about $140,000 by our math, over 25 times the cost of the subscription. That flat-rate discount is where the 80x went. The consultancy also priced each merged code change: about $1 on Claude, and about $0.63 on DeepSeek’s API if DeepSeek did the same work with the same tokens. Its $1.15–$4.90 estimate for DeepSeek is based on assumptions that the model would need more tokens, succeed less often, and need Claude to check its work.

DeepSeek never got to write actual code in the test. The consultancy’s reviewers kept finding ways agent code could escape the sandbox, for example via a settings file in a shared temp folder that could allow agent code to be run as admin. This meant the code-writing agents had to stay offline. Instead, DeepSeek ran only as 48 to 64 read-only reviewer agents, reading 2,377 folders and filing 32 bug reports. The box, rented at a $9.19-an-hour spot rate, was taken back by the provider within minutes of the last test ending.

Based on the V4 generation, where Pro outscored Flash, better performance from the model family can be expected with DeepSeek V4.1-Pro, which currently does not have a firm release date. This may change the math in the future, but for now there are better ways to spend on tokens than renting GPUs to run an open model.