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.

● Check out this MSI gaming laptop deal

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.

Three companies, Cadence, Synopsys, and Siemens EDA, dominate chip design software. Their software takes you from chip specification to a manufacturable layout. As AI has rapidly advanced, so have their Electronic Design Automation (EDA) agents, all sporting notable improvements in 2026. The addition of a reasoning model helps drive that flow for Cadence’s ChipStack super agent, Synopsys’ AgentEngineer, and Siemens’ Fuse EDA AI agent, each with its own claims to fame. Cadence at Computex on June 1 said its agent reached what it calls Level 5 autonomy. Rob Knoth, Senior Group Director of Strategy and New Ventures at Cadence, told Tom’s Hardware Premium that the demo was “what we call bounded Level 5 in one domain,” with the bulk of super-agent tech at “I’d say advanced Level 4.”

Synopsys said its spec-to-RTL (Register Transfer Level) workflow shown on March 11 reached “L4,” and the company told Tom’s Hardware Premium it now claims L5 capabilities for its long-horizon agents. Siemens’ agent launched March 16 with “self-verifying” loops announced on July 26. Meanwhile, outside these big three, Empyrean’s chairman, Liu Weiping, stated on Sept. 9 that its agent cut a layout task from four weeks to one, according to the South China Morning Post.

While the claims about what these agents do are specific and now known, what they have measured is not. All of the claimed improvements to speed are the vendors’ or their customers’ own figures, with many “up to” or “early evaluation” caveats. These figures often measure different things against different baselines, and the only evaluator Synopsys named in July, AMD, has not provided analysis beyond an endorsement.

From copilot to closure

In an evolving field, it can be difficult to hit a moving target, especially when the reported numbers measure different things. Some clarity is possible by following the distinct stages from human-only to human-reviewed agent autonomy. The agents fundamentally rely on existing technologies with known desired outputs for accurate evaluation.

Progress has so far followed three distinct generations. At Synopsys, by DSO.ai and reinforcement learning in 2020, generative AI capabilities with Synopsys.ai in 2023, and finally an open agentic AI stack from 2025–2026. All decisions must be validated by proven EDA engines, with the agent focused on evaluating intermediate results and determining subsequent actions. A model’s proposal is examined by an existing deterministic engine such as Xcelium, Jasper, Questa, Calibre, or Fusion Compiler within an iteration loop until final verification.

Measuring progress here does not follow a standard, but there are autonomy ladders with a general rating from Level 1 or L1 to Level 5 or L5 or, in Siemens’ case, no numbered level. Cadence, which uses levels, moves from optimization AI to conversational LLM, complex reasoning, agentic workflows, and then finally full autonomy, defined as taking a design from spec to verification “with minimal human intervention.”

Synopsys’ L framework goes from actions of single agents on a single design step to more complex ones with multiple agents, with further development focusing on adaptive learning and, ultimately, the agent’s ability to make decisions autonomously. The critical part for Synopsys is that “human engineers will and must always be in the loop.” Given the claims of progress, it’s clear that ladders, where they exist, are a form of marketing scaffolding that describes the same underlying architecture or loop.

Specification to verified RTL

The greatest claimed gains thus far are in the front-end design and verification stages, taking a specification from RTL to a verified design. This part of the process is a natural fit for LLMs, as the RTL, testbenches, and assertions are code. Where the loop used to wait on human analysis and input, the agents can run simulations in parallel and make decisions with fast, concrete pass-or-fail answers. These loops already existed and are prime real estate for agents to speed things up, with claimed orders-of-magnitude improvements by moving from human to agent on what is often the slowest part of the process, triage.

Getting from the chip specification to verified RTL code involves generating the code from natural language and formal specification, according to Synopsys’ March release. From there, the agents generate unit-level testbenches and engage in a verification loop. In July, Synopsys claimed RTL validation could be up to 50x faster with a 20% improvement in coverage, compared with its own non-agentic flow.

For Cadence’s ChipStack from February, the scope includes “autonomous RTL design, verification, and debug.” Named early users include Altera, Nvidia, and Qualcomm. ChipStack started as a Seattle startup that was acquired in November of last year after a multi-year integration with Xcelium and Jasper. In June, Cadence said RTL validation could happen over 40x faster, with each engineer using ChipStack agents “to run hundreds of dynamic simulations with Cadence Xcelium ... reducing a typical five-week verification loop to less than a day.”

Siemens’ Questa One Agentic Toolkit, detailed in February, has five separate agents: RTL Code Agent, Lint Agent, CDC Agent, Verification Planning Agent, and Debug Agent. These work with mainstream AI coding applications including Claude Code, Cursor, and Siemens’ own Fuse. Akshay Aggarwal, senior director of engineering for MediaTek, noted “immediate and significant” gains. The numbers from Synopsys and Cadence should be read as the vendor’s own ratio comparison to the old loop, as regression loops are already automated. All three companies are now focusing on the decision-making process. For details on specific claims, refer to the table below.

The back end

Things are tougher when you move from RTL verification to implementation. Outside of specific tasks that look like front-end loops, claimed gains are harder to come by. This phase deals with geometry and physics, not code, and the pace is limited by compute. Some tool runtimes are “days to weeks … just running the tool itself,” and a tool call that takes “hours to days” to return a value “really gives the AI industry a much different problem to solve,” Knoth said. Complex trade-offs remove singular pass-or-fail judgments, and mistakes later in the process become much more expensive. Earlier AI integration also already exists for this flow, so the agentic touch is more on experiment-running. This makes things cloudier.

Cadence’s InnoStack runs parallel experiments from synthesis to ECO execution. ViraStack, which is for custom and analog solutions, handles the design flow from drawing to simulation, optimization, and layout porting, with some customers “reporting 3–10x productivity improvements,” according to a report from Futurum in April.

Synopsys’ looping implementation agents, driving Fusion Compiler directly or through other platforms, help “reduce manual engineering effort,” according to AheadComputing, and Synopsys said they improve quality of results, including timing, power, and area.

Siemens Fuse, meanwhile, coordinates at least nine named tools, including Solido for custom IC design and verification, Aprisa for physical implementation, Calibre for signoff verification, and design-for-test with Tessent. Improvements vary, with Solido mentioned as improving characterization turnaround times by more than 10x and token costs by 5x to 10x, according to Siemens. One third party, STMicroelectronics, mentions that the Solido Layout Analyzer is helping “cut down the time we spend debugging complex design blocks by weeks,” according to Non-Volatile Memory Design manager Gianbattista Lo Giudice.

Empyrean, according to the SCMP’s September reporting, is shifting from “humans operating tools” to “humans commanding agents” with its agentic EDA platform. This platform would work with its partners’ agents. One claim is that 3D verification went “from two to three months to less than a month,” according to TrendForce reporting. Empyrean is the largest domestic EDA vendor in China and is state-controlled as of December 2024.

Self-checking agents

What we inevitably keep coming back to with agentic EDA is one simple term: trust. The agents must be trusted to varying degrees to check their own work. Important checkpoints are still verified by a human: “The crucial approval checkpoints are still going to be human-driven,” Anand Thiruvengadam, executive director of product management at Synopsys, told Tom’s Hardware Premium. Inside the loop, the use of reliable tools such as deterministic engines allows for results that can largely be trusted. Outside of this, trust is based on evaluation, and internal verification is only as good as the tests. There remains reliance on what the vendors claim, and that depends on exactly how each defines the self-checking loop and goals.

The Siemens Agent loop, put simply, is: Plan, Act, Reflect, Iterate, over “Develop, Publish, Execute, Refine.” “Self-verifying” in Siemens’ case means that agents “continuously validate decisions against deterministic, physics-based EDA engines.” According to SemiWiki’s Bernard Murphy, describing the Fuse launch, “the orchestrator can trigger repair agents, then re-run validation. This may resolve most errors,” although we were unable to get any comment on specific triggers.

Cadence’s description is that “every action the super agent takes is anchored” in its own proven design and verification engines, to ensure accurate results. These, like Siemens’ solution, can run “within a secure Nvidia OpenShell sandbox” to enforce guardrails and ensure predictability. This allows engineers to “inspect, guide and collaborate as needed” even at Level 5. The human engineer works from their own runtime environment, then “once the super agent finishes that loop … the auto-run environment can interpret, understand, and then fire off that next message, right, that next iteration,” Knoth said.

Synopsys also retains the human element, with the debug workflow’s output as root-cause analysis and resolution through engineers. The flow iteratively runs checks against generated, unit-level testbenches, with the agent handling design and the tests themselves. When a check fails in Synopsys’ Verification AgentEngineer, the logs are consulted, and errors are aggregated to paint a picture of the most likely cause, checked against simulation waveforms. When applying the fix, the agent must prove that the bug has actually been fixed and should be able to produce a bug-fix manifest. Beyond this, the quantity of checkpoints is determined by customers’ trust and desired flexibility, and over time, “they might relax and take away those checkpoints,” Thiruvengadam said. The internal process, such as the specifics on retry limits, the definition of unfixable, and states requiring escalation, remains murky.

Early access everywhere

All of the numbers and roadmaps in the world don’t replace actual value to the customer, or actual customers with a released product. Thus far, the agentic platforms have been in early access and evaluation. Engagements have been listed, and major customers exist, but none are confirmed with a generally available product. General availability hasn't arrived yet, but it has been promised by the end of 2026 by Synopsys, with Cadence’s AuraStack, its PCB and advanced-packaging agent, also due this year. Usable products and workflows exist, but the efforts remain nascent, despite relatively rapid claimed gains over the last year or so.

Cadence ChipStack has been in early access since February. In June, this extended to the promise of Level 5 capabilities with early access for customers in the second half of 2026. Synopsys has debug and implementation workflows, announced in July, available for evaluation on Microsoft Discovery, if requested. Microsoft’s platform is designed to assist in the construction and management of agentic AI workflows related to scientific and engineering fields. Siemens has the Questa One Agentic Toolkit, announced in February, and the Fuse Agent as of March, with promises made in July surrounding self-verifying capabilities in forthcoming releases. Empyrean is also developing its own platform as of the time of writing.

The current stacks largely run on Nvidia’s Nemotron and its OpenShell sandbox. Nemotron is a reasoning model family. OpenShell is an open-source runtime that keeps the agent contained with kernel-level isolation, protecting customer IP and guarding against rogue actions. Nemotron and OpenShell are open, so nothing on record prevents Empyrean from using them, but without announcements, it appears to be the outside case. What each EDA vendor does with this technology and the flexibility it offers its customers are the defining differences.

Earlier this week, Nvidia announced its Open Agent Safety Platform, a combination of OpenShell and the Sentry reference design, an “out-of-band watchdog” that runs on BlueField-4 DPUs to “continuously monitor agent behavior” and can quarantine an agent in milliseconds. Cadence, Synopsys, and Siemens are named among the more than 100 organizations working with Nvidia on the platform. Cadence’s Knoth told Tom’s Hardware Premium that OpenShell “helps ensure the agents are well behaved ... don’t go rogue and start accessing data they’re not supposed to,” as agents need to become productive and trusted. Thiruvengadam from Synopsys echoed these sentiments, with both companies aware of the dangers of rogue agent actions.

Cadence and Synopsys, at least, needed licenses as of May 2025 for EDA sales to China, but the requirement was rescinded as of July of that year. EDA is expected to be critical for the industrial software space “toward the high end of the global value chain” by 2030, according to ChinaTechNews. According to Liu, as reported by the SCMP, Empyrean is trending away from traditional software licenses to a token-consumption pricing model with agentic EDA.

Startups in this space include ChipAgents, Cognichip, Agentrys, Silimate, and Ricursive Intelligence, most funded since December 2025. The big three buy other companies, from startups such as ChipStack to simulation software maker Ansys, as advances put EDA in the spotlight toward the end of 2026. Even with platform-specific flexibility, Nvidia remains the common ground, with its open model family and sandbox being the standard across the big three.

As it stands in 2026, the agents exist, and there are real roadmaps and customer evaluations. The framework hinges on existing technology already worthy of trust. The overall trend is toward harness engineering and, as Knoth told us, the implementation of “an array of specialized engines” to fuel the workflow end to end. Improvements, although currently lacking the independent measurement and precision we would like to see, are real and accelerating, according to vendors and their customers. It will become increasingly important to have proper evaluation of the different agents as they approach Level 5, “L5,” or some equivalent marker that denotes autonomous action.

What we would like to see by the end of the year is some customers named to actually be using the highest level of agent. Specifically, these agents must live outside of a trial or early access mode. This enables oversight and comparison in what will become an incredibly important field. We also anticipate Siemens’ forthcoming releases, which may demonstrate the self-verifying loop. Cadence is worth a close watch to see whether a Level 5 early-access customer is named before the end of the year. The baseline, though, is at production: real customers, real workflows, real products, and a measured improvement.

A developer who goes by the name “stmonty” has trained a small AI model on a single RTX 3080 Ti to play Pokémon Red. The developer’s blog post, “Teaching a World Model to Play Pokémon,” details the journey of training a model working from a save to pick a starter. The model is what is known as a world model, built off of LeWorldModel research co-authored by famed AI scientist Yann LeCun. LeCun split from Meta last year to build world models with his AMI Labs, using the same approach.

This particular one was trained with only about 12.5 million parameters and written up on Sept. 20. On a discussion forum, stmonty called the project a way “to learn and have some fun,” and chose the model because it could be trained locally on a consumer-grade graphics card.

The model has to learn what each button press does, and it does this by looking at the screen that’s produced. Rather than predict the entire next screen, it uses compressed summaries with just 192 numbers. The initial training is “reward-free” in the sense that the model is not aware of winning conditions. Goals come later, when the model plans. This design follows a March 2026 paper detailing a single JEPA model with about 15 million parameters, trainable on a single GPU, not that far different from stmonty’s.

The full training run required 42,382 grayscale frames in over 1,000 short runs, using a save in Professor Oak’s lab. The data included scripted routes, those same routes with random presses mixed in, and random wandering. The last part is necessary because a model trained only on clean runs “might see A pressed whenever a dialogue box appears and never learn what B does there,” said stmonty. The model was trained from scratch for the project.

Plans are developed with 14 button presses and attempted in the model, not the game. Each round has 512 plan samples where the search keeps one in eight, or 64. This continues until the final plan is the one run in the game emulator. Stmonty hypothesizes that one possible cause of the failure, which left the player without a Pokémon, is that small errors compound when predictions are made from the model’s own predictions.

The next attempt, after some fine-tuning, came back a success with the model selecting Squirtle. Then, over 100 runs from the same save, there were 52 plans that got a starter, compared with zero for random button presses and just one with an untrained model. Stmonty stated that pressing A repeatedly already works from the save; the model discovering the solution on its own was the goal. This is still a modest result but does demonstrate that small models can be tailored to specific tasks.

The next goal, which the developer “may still try,” is to start in the lab, walk to the Professor, get through the dialogue, then actually pick a Pokémon. “I suspect that difficulty scales exponentially with plan length,” the developer said. Making the model large enough would probably not lead to the game being beaten: “I don’t think simply making my current model bigger would get us there,” the developer stated in a reply.

The small model approach to playing Pokémon Red is one of many in recent weeks, with at least two separate, and successful, attempts to beat the game using the Jev decision model and a harness or custom code. Those use more sophisticated approaches, however, while small models remain a realistic option for hobbyists. The project’s code is open source and available on GitHub as lePokeRed, with a CUDA GPU recommended.

Former EVGA product manager Brendon Ray Hedrick has posted a personal account of his time at the company in which he details the challenges that EVGA encountered near the end of its partnership with Nvidia. When he left the company in 2019, Hedrick predicted that the business relationship between the two companies would eventually implode, which proved to be largely accurate when EVGA exited the GPU market in 2022.

As he tells it, Hedrick's concerns about the relationship between the two companies began when it first released the Founders Edition GPUs in 2016. While EVGA had sold Nvidia reference designs with its own brand on the box before, its primary (and only) GPU supplier was now competing directly against it by selling graphics cards directly to customers, bypassing board partners entirely.

However, the biggest challenge that EVGA faced arose, according to Hedrick, was when Nvidia began requiring its partners to sell a model that would hit the announced MSRP, even if it sold at a loss. Companies that did not comply would get reduced chip allocations, so partners needed to produce a loss leader product to ensure that they would get all of the chips they would need to satisfy demand.

“The models on which we did not lose money had better circuit boards and cooling of our own, but they also carried a substantial premium,” Hedrick writes. “To a customer comparing them with the advertised starting price, it could look as though EVGA was charging an enormous amount just for those additions.”

In Hedrick's view, as Nvidia Founders Edition cards got better with each generation, more expensive AIB offerings started to make less sense for most buyers. This led to loss-leading models constantly being sold out, while customers who chose more premium models started to hold on to their GPUs for an extra generation or more. “For a manufacturer like EVGA, those decisions carried very different consequences. We still had cards to sell and a business to sustain; enough customers sitting out a generation could be financially devastating,” Hedrick wrote.

In reference to Nvidia's introduction of real-time ray tracing tech on its RTX 20-series cards in 2018, Hedrick recounts that “Nvidia could afford a long technological bet. My concern was whether a company as lean as EVGA could comfortably absorb the difficult part of it,” in reference to convincing consumers that the upgrade to the new tech was worth it at a time when only a tiny handful of games featured RT.

When Hedrick left the company, he says he believed that EVGA and Nvidia had about four or five years left before the situation led to a confrontation. This prediction came true a year earlier than Hedrick projected. In 2022, EVGA announced that it would no longer make partner cards with the advent of the RTX 40-series and that it would exit the GPU market once its stock of RTX 30-series GPUs had run out.

At that time, it was reported that Nvidia GPUs made up 78% of EVGA’s revenue while contributing only a tiny percentage of the company’s overall profit. Other reports also said that while lower-tier products like the RTX 3060 remained profitable, EVGA lost hundreds of dollars for every RTX 3080 and higher GPU model that it sold.

While it’s easy to blame Nvidia's policies for EVGA’s exit from the GPU market, other analyses partially blamed the company for practices like contracting manufacturing to third parties and providing overly generous customer service for its failures. Its focus on North America and Europe also led to the company shipping fewer units compared to its competitors, meaning its fixed costs were further cutting into its already narrow margins. Although EVGA’s customer-centric approach made it one of the most beloved brands in the GPU space, its strategies ultimately left it vulnerable, and we all know how the story ended.

Former Nvidia advisor Eric Gullichsen says that he is “owed a billion dollars in NVDA stock,” according to his blog post titled the same. The post says that Gullichsen, an early Nvidia Technical Advisory Board member, was granted 25,000 options in September 1993.

Re-reading the grant in 2024, he says, showed that it was meant to vest in four quarters, or a year, instead of over four years. An April 1996 CFO letter counted 15,625 vested, so 9,375 more shares should have vested too, he added. After a combined 480x in splits, this would be 4.5 million shares, or about $1.01 billion at Nvidia’s Sept. 25 close of $225.07, in line with his “about a billion dollars.” He says that he and his counsel agreed that the statute of limitations was against him, and “it seemed unlikely we’d make it past a motion to dismiss.”

The option grant document provided by Gullichsen on the blog post is dated Sept. 9, 1993, with a No. 7 grant of 25,000 shares, signed for Nvidia by Huang. “All shares shall vest upon the expiration of one year from Grant Date,” which means fully vested by Sept. 9, 1994. It states 25% at three months, then quarterly, which would be four times in the year.

Gullichsen also provided the exercise letter from CFO Marcel Gani dated April 16, 1996, which ends his “contractual relationship with NVIDIA and Its Technical Board of Advisors” and states that he had “15,625 shares of NVIDIA stock options vested.” This was at $0.05 a share with 90 days to exercise, or $781.25 for a full exercise.

Gullichsen also included an undated invitation letter signed by Huang asserting “a stock option of 25,000 which vests over 4 years.” Doing the math, 15,625 is 62.5% of 25,000 shares, or 10 of 16 quarters, which matches the Sept. 9, 1993 to April 16, 1996 timeline. The CFO’s count fits the four-year quarterly schedule. So the CEO’s letter says four years, while the signed cover sheet says one. The cover sheet also says any discrepancy with its attached legal provisions “shall be governed by the attached legal provisions,” and those attachments are not in Gullichsen’s post. It also says it supersedes prior written agreements, which would override the invitation letter.

Upon discovery of the possible discrepancy, Gullichsen hired lawyers who worked “on contingency,” according to one of his replies on a related discussion thread. After about a year of letters between his team and Nvidia’s in-house and outside counsel, the two sides met. “We proposed to settle for a far smaller number,” he wrote, but Nvidia “still made the call to say nope.” He and his lawyers concluded that after “thirty-odd years” the case may be too far past its prime. The CFO letter’s exercise window had closed around July 15, 1996. Nvidia has not publicly responded.

Gullichsen worked on multiple VR projects starting in the late 1980s with his own company, Sense8, which he co-founded around 1990. That VR rendering work led to a fast implementation of biquadratic texture mapping, which he says caught the attention of Nvidia co-founder Curtis Priem in 1993, the same year of the option grant. Priem then brought Jensen Huang and fellow co-founder Chris Malachowsky to Gullichsen’s houseboat in Sausalito for a demo, he says.

He is a named inventor on a patent filed in 1994, “Wide-angle image dewarping method and apparatus” or US5796426A, which names “the NV-1 chip sold by N-Vidia Corporation” as a hardware example. The NV1 was not commercially successful but used quadratic surfaces as its basic primitive.

Some commenters inquired about what happened to his existing shares. Gullichsen had not answered as of Monday morning. His settlement reasoning, however, included “the likelihood I would have sold.” Our earlier reporting shows another investor, Stanley Druckenmiller, sold before the 10-to-1 split. Nvidia has since reached the position of the most valuable company in the world, at least temporarily. The stock’s rapid rise in value has created many new millionaires among its employees.

As for Gullichsen’s advice: “Read the contracts. Carefully.”

Synopsys announced its AgentEngineer solutions, a portfolio of “domain-specific long-horizon agents” built on its new Autopilot Platform. As the company details in a blog post, the portfolio covers six named domains: verification, system validation, implementation, analog and mixed-signal (AMS) design, manufacturing, and simulation and analysis. More than 50 customer engagements are underway, according to the company, and Synopsys confirmed to Tom’s Hardware Premium that general availability is planned for the end of 2026. This follows July, when the company showed agentic AI workflows developed with Nvidia and with Microsoft at DAC, the Design Automation Conference.

The launch brings Synopsys’ plans into focus with a named product line and a target date. The platform’s agents are clearly named and delineated, the engagement count points to customer interest, and a goal for general availability anchors a roadmap for autonomous agents in production. Two of the headline performance figures are restated from July, and the only named customer figure is one company’s range. While Synopsys describes the agents as autonomous, the approval checkpoints remain with humans, the company said.

For companies designing and producing chips who want to leverage the potential efficiency gains of AI, this technology allows them to “accelerate their shift from AI-assisted design to autonomous engineering,” said Ravi Subramanian, chief product management officer at Synopsys.

In a blog post published alongside the release, Anand Thiruvengadam, executive director of product management at Synopsys, detailed three layers: long-horizon AgentEngineers are “domain-specific super agents that orchestrate task agents,” task agents “complete specific, bounded engineering tasks” and can be orchestrated by an AgentEngineer or invoked directly by an engineer, and the tool layer’s engines “execute the requested work” but do not set goals or make decisions. Unlike long-running agents, which may perform one activity for hours or days, long-horizon agents address “goal complexity,” pursuing objectives that can take hundreds or thousands of reasoning steps.

The platform covers everything from orchestration to telemetry, with a “cognitive model” powering what Synopsys calls context intelligence. Access controls, encryption, and runtime guardrails protect customer, partner, and Synopsys IP, which is particularly important when third-party agents share the workflow. Customers can choose commercial, open-source, or fine-tuned language models and deploy on Synopsys Cloud, their own cloud, or on-premises infrastructure. In other words, customers can “bring their own LLMs and data and infrastructure,” Thiruvengadam told Tom’s Hardware Premium.

The blog outlines the verification loop — the agent plans, orchestrates task agents, checks what they return, and adjusts whenever an intermediate result falls short. When a test finds a bug, a root-cause analysis (RCA) agent reads logs, clusters errors, forms a hypothesis, and inspects waveforms to confirm it. The agent then makes “local rewrites of the RTL to prove that the bugs have indeed been fixed” and produces a bug fix manifest. When intermediate results drift from the objective, the agent will “course correct, adapt, react,” he said.

The performance numbers in Synopsys’ release are heady: up to 50x faster verification closure, 20% higher coverage, a 30% productivity boost, 2x better token efficiency, and lower latency. The 50x and 20% figures are not new. Synopsys told Tom’s Hardware Premium that they come from its July work with Nvidia, measured against its own verification workflows without AgentEngineer. The 30% number is at the top of the 10% to 30% range Fujitsu reported for its RTL code generation.

Those productivity gains are measured “compared to what the human experts would have done otherwise or are doing today,” Thiruvengadam said. The 2x token efficiency, meaning fewer tokens for a given task, is customer-reported: an unnamed customer compared Synopsys’ agents with its own, built on commercial agentic harnesses. No figure exists for the latency claim; the release and blog credit it in part to context intelligence, which suggests less time spent waiting on model calls.

Another engagement with results is AheadComputing, whose Vice President of Verification, Alon Mahl, said the Implementation AgentEngineer helped reduce manual engineering effort from RTL handoff through signoff, without giving a number. Intel, MediaTek, and Samsung also endorsed the technology. None gave hard results, but all supported the technology as promising. Today's launch is the portfolio and platform, without production details. Synopsys’ earlier AI tool from 2020, DSO.ai, has passed 100 production tape-outs, while the new agents are still in engagements.

How autonomous are these agents? Each vendor defines autonomy on its own scale, and Synopsys introduced its L1-to-L5 framework last year. “The original vision of L5 was fully autonomous execution. But not just fully autonomous execution, but also complexity,” Thiruvengadam told us, describing L5 as executing a complex workflow autonomously within human guardrails. “That was the idea, and that’s exactly where we are.” Synopsys confirmed that it characterizes the agents as L5. Cadence also claimed Level 5 on its own scale at Computex in June.

Earlier this year, Nvidia chief scientist Bill Dally said AI cut a 10-month, eight-engineer task, porting a standard cell library for GPU design, to one night, but that Nvidia is still “a long way” from having AI design a new GPU end to end.

Human engineers remain in the loop, but the amount of oversight varies. “The guardrails are still going to be defined by the humans, the crucial approval checkpoints are still going to be human-driven,” Thiruvengadam said. “Our customers will have to learn to trust these autonomous systems.” The blog adds that teams can set checkpoints where people inspect results, validate decisions, and redirect the workflow, then “reduce intervention” as confidence grows. No vendor has yet described when its agents stop retrying or escalate to an engineer.

Synopsys says the capabilities are already there; its next goal is general availability. Eyes will be on whether Synopsys reaches general availability by the end of 2026, and names a customer in production when it does. Cadence expects Level 5 early access in the second half of 2026, and Siemens has promised self-verifying capabilities in forthcoming releases. The product exists and works in customers’ hands, and the speedup claims, if they can be realized beyond internal evaluations, and especially if backed by independent testing, appear to be extremely promising.

PlusAI, an autonomous trucking startup, is working with Nvidia to develop its systems and has several trucks and trailers marked with the logos of both companies. This likely caught the attention of thieves who were looking to score big on AI GPUs, with Wired reporting that two of the startup’s trailers got stolen and were found at another location. However, the joke’s on the criminals, as they discovered that the two stolen units contained 20,000 pounds of sand each.

The AI data center construction boom has made PC components like RAM and GPUs insanely expensive, which has led to a rise in petty theft involving these components. Enterprises and startups are being targeted, too, especially as they build data centers and local AI servers with millions of dollars’ worth of equipment and hardware. We’ve already seen cargo heists of copper cable and other hardware worth $1.3 million, while other criminals have become more brazen, pushing or spinning security escorts off the road so they can get away with a truck’s worth of AI GPUs.

While PlusAI is partnered with Nvidia, it also uses simulated cargo loads for real-world tests, allowing the company to model how a fully loaded semi-trailer would react to various road conditions. The startup’s truck cabs, which likely had advanced AI hardware installed, were parked securely inside its warehouse, while the trailers were apparently left outside and secured with hand locks. “PlusAI uses simulated loads in its trailers to assist with research and development testing,” company spokesperson Lauren Kwan told Wired, adding that they were recovered with “40,000 pounds of sand intact.”

Interestingly, the discovery of the theft was accidentally made by one of the startup’s top executives, who received a message from an acquaintance saying that they saw the trailers, which they described as “trucks,” near their workplace. This likely got the executive concerned, as that location was half a mile away from PlusAI’s warehouse, which led to the cops getting called. As for the prospective criminals, they probably left the crime scene disappointed — they thought they were getting a huge payday from two truckloads’ worth of smart silicon, but they ended up with 20 tons of dumb sand instead.

A newly published patent shows that Nvidia has been working on an AI chatbot that could make it quicker and easier for developers to optimize their PC games. The chatbot would be capable of receiving a plain English request for help before running diagnostics based on the specific issue at hand.

PC gamers are well aware of the impact of poor optimization on games. And while there are tools available to developers that help them identify issues and then address them, they require specialist knowledge to use. They also take time, something that developers rarely have — especially as they get closer to a release date.

The patent, first spotted by Patentlyze, could help. It depicts a chatbot-like interface in which developers would be able to outline the issue that they are seeing. They would be able to do so using normal language, allowing even relatively inexperienced developers to use the tool.

Example questions provided by the patent include "What is causing the micro-stutter during camera cuts?" and "Why is frame time spiking when ray-traced reflections enter the viewport?".

Once the question has been posed, the AI would then write a diagnostic specific to the issue before automatically running it on the GPU and then providing the results for interpretation. The developer would then be able to implement any fixes and re-run any testing that is still required.

The patent doesn't make it clear whether there will be any validation or testing of the diagnostic code this tool would create, something that may give some pause. Test results are only as strong as the test that was used to get them, and developers will need to trust that Nvidia's chatbot is up to the job before relying on it.

However, it's important to remember that companies like Nvidia apply for countless patents every year. Not all of those patents turn into shipping products or features, and there is no guarantee that this one will ever see the light of day. That being said, it does seem similar to G-Assist, a chatbot-like tool that allows gamers to get AI-powered assistance for their GeForce RTX PC. Nvidia has also sought to bring its AI chops to bear on other aspects of gaming, not least DLSS 5 and its multiple AI modes for different levels of detail — whether gamers want it or not.

For gamers, the hope will be that this patent will result in a tool that makes it easier for game developers to optimize their wares ahead of release.

Nvidia has updated its RTX Mega Geometry SDK to 2.0, adding streaming support for “continuous level-of-detail clusters” to handle high-density meshes, according to the developer blog post. Nvidia says it works even for scenes too big for the VRAM budget. The technology is in addition to its RTX Kit, arriving alongside RTX Kit 2026.3. The announcement comes close to the release of Gears of War: E-Day, which makes use of RTX Mega Geometry technology. Nvidia has not explicitly said what version of Mega Geometry the game uses.

Mega Geometry “organizes detailed meshes into clusters so ray-traced scenes can adapt geometric detail efficiently,” according to Nvidia. The SDK appeared at version 0.9.0 beta in 2025 and found its way into Alan Wake 2’s title update 1.2.8 early that same year. Our test on an RTX 4090 found about 1GB of VRAM saved with a performance boost of 13%, at native 4K and with DLSS Quality. On an 8GB graphics card, that 1GB is an eighth of the memory.

The SDK is at version 2.0.0 on GitHub as of this week. The technology makes dense scenes less expensive to ray trace, with 2.0 specifically targeting geometry that doesn’t fit in VRAM. This means that “a scene’s source geometry no longer has to fit in VRAM [with detail] bounded by a memory budget rather than by mesh count,” according to Nvidia’s changelog.

When a scene goes over budget, it settles at lower detail instead of constantly swapping geometry in and out of VRAM. By default, this is based on 2GB of VRAM for streamed geometry, another 2GB for ray-tracing acceleration structures, and 4GB of material textures, but these are adjustable. The existing tessellation path remains.

In the case of Nvidia’s sample Zorah, there are 1.6 billion unique triangles in a 70GB download, with a first load that can require up to 64GB of RAM at peak. Zorah uses 31GB of mesh data, but the courtyard sample view only has 1.5GB of mesh data in VRAM, using an RTX 5090 at 4K with DLSS Quality.

The sample requires an RTX GPU with 10GB of VRAM or more, a Game Ready driver 570 or later, Microsoft’s DXR 1.1 or later, and Windows 10 at a minimum. For now, this looks more like a tool for developers than players. It’s an SDK with a reference sample, and currently no game has been confirmed to use it.

The rest of RTX Kit 2026.3 includes RTX Character Rendering 1.4, RTX Dynamic Illumination 3.1, RTX Neural Texture Compression (NTC) 0.10 beta, RTX Neural Shading 1.4, and RTX Texture Filtering 1.3. NTC has been at v0.10.0 beta on GitHub since Aug. 4, with the DX12 LinAlg path only for testing purposes. The DX12 LinAlg path requires preview driver 620.12, the preview Agility SDK, and Windows Developer Mode. That leaves Vulkan as the only way for released games to use NTC with Tensor Core acceleration today.

An existing problem, Nvidia said in August, is that under ray tracing, Nanite elements “switch to a single, static low-detail mesh” because it’s “prohibitively expensive” to build acceleration structures for them with “existing DXR 1.2 and Vulkan APIs.” It has taken six years of research and development to pioneer the new technology, with the innovations debuting with Gears of War: E-Day on Oct. 6. These will later be part of Microsoft’s DirectX Raytracing 2.0 standard, according to Nvidia.

On Sept. 22, in a Game Ready driver post, Nvidia stated that Mega Geometry enables “accurate ray tracing of Nanite geometry for the very first time.” Nvidia’s Game Ready driver 617.14 for the game is already out. Early access on Oct. 1 is the first chance for players to see Nvidia’s Nanite ray tracing technology in action.

The end goal is finally in sight for Elon Musk, nearly two years after he announced plans to expand the Colossus supercomputer to over a million GPUs. The billionaire said on X that 220,000 Nvidia GB300 GPUs will be operational by next week, with another 220,000 coming online in November. He also added that another 220,000 units will come online by late December “if we get lucky.”

These numbers would add to the 110,000 GB200 and 440,000 GB300 GPUs already operating at Colossus 2, plus the 150,000 H100, 50,000 H200, and 30,000 GB200 GPUs at Colossus 1. This would bring SpaceXAI’s GB300 GPUs to 1.1 million units, with its total GPUs in operation to 1,440,000 units. This is quite an achievement, especially given that the company is behind its rivals by several years — SpaceXAI is only three years old, while Anthropic and OpenAI are six and ten years old, respectively. Interestingly, the Colossus 1 site, which features a combination of Hopper and Blackwell GPUs, is inefficient for training Grok, so Musk rented it out to Anthropic for inference instead. On the other hand, Colossus 2 solely uses Blackwell GPUs, ensuring that there won’t be any bottlenecks.

While Elon Musk essentially begged for Jensen Huang to give him these GPUs, getting his hands on them isn’t currently the hardest part. One of the biggest issues facing data center build-outs right now is power, and SpaceXAI solved this by bringing its own. This has resulted in some controversies with the surrounding neighborhood, ending in a lawsuit against the company for running unpermitted gas turbines. It has since promised to remove them, but only over a span of one year as its own 1.2-GW power plant comes online.

Running a million GPUs or more on a single site is an impressive achievement, but SpaceXAI isn’t the only one trying to reach this goal. Broadcom said in 2024 that it has three hyperscale customers gunning for this target by 2027 but didn’t mention who these customers were. As for Elon Musk, the 1-million-GPU target is just the beginning. He said that SpaceXAI will grow its data center capacity sevenfold by 2027, and he’s even aiming for 50 million H100-equivalent GPUs by 2030. These data centers won’t be limited to the ground as well, with SpaceX planning to launch an Orbital Data Center System with a million satellites, despite Jensen Huang saying that it’s a “dream” for now.

Nvidia CEO Jensen Huang has a simple solution for frontier labs like OpenAI and Anthropic that have recently called for increased regulation for their models — "we have to shut the labs down," Huang said in an interview with The New York Times' Ezra Klein. Huang's comments are pragmatic ones, stating that if frontier labs can't contain their experiments, those experiments must end.

"Now, if they say the alternative, which is: There is no way to contain our experiments, there’s just no way; when we test our A.I. models, it will get out, and it will damage the world — then I think the answer is that we have to shut the labs down," Huang said. "Because the cost to humanity, the damage is too great. The shareholder, the liabilities — it could be civil liabilities, it could be criminal liabilities. I mean, the liability’s incredible."

The conversation around increased AI regulation comes on the back of a highly publicized incident where an OpenAI agent hacked popular model hosting platform Hugging Face — a platform that Nvidia now owns, and one that likely would've sought damages from OpenAI had it been under Nvidia's purview at the time. Following that incident, various frontier labs, including those at Anthropic and Alphabet (Google), have used the idea of rogue agents as equal parts marketing tool and call to regulatory action, likely in an attempt to be the first at the table to shape those regulations.

Huang has not been keen on those regulatory calls. Just days ago, the executive countered extreme claims that AI would destroy the world by 2030, and said that frontier labs "never should ship products before they're ready and deliver products that are unsafe."

Although the labs and their chief hardware vendor, Nvidia, have mostly been aligned when it comes to AI policy, the push for regulatory capture has created a rift. Multiple labs, including OpenAI, Anthropic, and SpaceXAI, agreed mutually to slow the pace of model development. Now, they're facing an antitrust lawsuit, with claims these plans are "self-serving."

Huang's comments to Klein shift the responsibility away from collective development and toward individual leaders. "These are companies with agency," Huang said. "If I believe that I'm about to launch a product that is unsafe, it is completely in my ability... to not launch that product. And so I can't buy into the idea that somehow, all of Americans, around 400 million of us, are pushing them to launch untested products that are unreliable."

Although one side of the argument (from the frontier labs) is seemingly pushing for more regulation, that doesn't mean Huang is against regulation. In his own words: "I’m saying that we have lots of laws and regulations. Apply it." The executive argued that labs, particularly those at OpenAI and Anthropic, have rapidly gone from research institutions to companies likely worth hundreds of billions of dollars, and that they should carry themselves as such. "I’m not against laws and regulations. I’m against, currently, the distraction," Huang said.

Reports of rogue AI agents continue to mount. Earlier this month, Anthropic disclosed a fourth instance where one of its models hacked an external system, and just days ago, Google confirmed a report that its Gemini models hacked three companies earlier this year.

Micron is reportedly pulling the plug on its 2 GB (16Gb) GDDR7 memory chips, ending production and sales of both the 28 Gbps and 32 Gbps variants. According to an X post by UNIKO’s Hardware, the chips — some of which shipped on some GeForce RTX 50 graphics cards — are now designated as “End of Life” on Micron's site. Tom’s Hardware can confirm that the pages for module norms return “No results found.”

Micron just began supplying its 2GB GDDR7 to Nvidia earlier this year, with Samsung and SK Hynix being the main GDDR7 suppliers. As a result, Micron's 2GB module only shipped on a few Nvidia models, such as retail variants of the GeForce RTX 5060. Samsung and SK Hynix cover the vast bulk of 2 GB GDDR7 modules for the standard RTX 50 retail lineup, so the risk of immediate shortages is minimal. Micron’s uncompetitive position in the 2 GB segment also appears to have made a fast exit logical to free up fabrication capacity.

The company is reportedly shifting its focus toward higher-density 3 GB (24 Gb) GDDR7 chips. These larger packages are considered impractical for Nvidia's RTX 50 lineup because they are more expensive and result in less conventional consumer memory capacities. However, they yield higher profit margins and are actively utilized in Nvidia's high-end RTX PRO Blackwell GPUs tailored for generative and agentic AI workloads.

Micron’s decision follows the ongoing trend of memory suppliers prioritizing AI-facing technologies over their consumer and retail counterparts. The company exited its Crucial consumer memory and SSD business earlier, explicitly saying that surging AI data-center demand had prompted it to prioritize larger strategic customers in faster-growing segments.

A 3D artist looking to purchase an RTX 5090 for their work claimed that they scored an unopened Asus ROG Astral RTX 5090 White OC from the secondhand market. According to Wccftech, the artist purchased the GPU expecting that it was signed by an Asus executive in celebration of the company’s 30th anniversary. But when they opened the package, they found the Nvidia chief’s signature on it instead.

Jensen Huang autographed the GPU and showed it off on camera last year, after which it was supposed to be given away by one lucky gamer. The artist did not mention how much they bought the “used” GPU for, but brand-new models of the RTX 5090 now hit $5,000 and can go up even higher, depending on the model. They also said that after opening the box, they did not use or test it, opting to sell it instead because of the immense value that such a limited-edition graphics card held for the right buyer.

This specific GPU is now in the hands of the Bonhams auction house, with bidding set for Oct. 4 to 14, 2026. It’s estimated that the graphics card will fetch somewhere between $10,000 and $15,000, especially as it bears the signature of Nvidia’s founder. However, it should be noted that an Asus ROG Matrix RTX 4090 sold for $16,000 back in 2023, while an Asus ROG Astral RTX 5090 Dhabab OC Edition was auctioned off last year for $24,200. Both GPUs were also autographed by Jensen, so there’s a good chance that this graphics card could fetch a much higher price.

Another Jensen Huang-related object was put on the auction block earlier this year and hit a record price. The leather Tom Ford jacket that the CEO wore at the Hon Hai (Foxconn) Tech Day in Taipei way back in October 2023 went up on Sotheby’s, where it was estimated that it would fetch somewhere between $40,000 and $60,000. However, the final price ended up at $960,000 — more than a hundred times higher than the jacket’s $9,000 price tag from the manufacturer.

Even though Jensen Huang isn’t a celebrity in the traditional sense, his approachable public persona and success story have made him quite popular among his fans. This “Jensanity” is prevalent in Taiwan, where he was originally from, and many see him as the bearer of Taiwanese pride and treat him like a pop star. It seems that this translates to his rich fans, too, with a few who are willing to spend thousands of dollars just to get their hands on autographed “artifacts” from the billionaire.

UK-based “neocloud” Nscale built its business on a major deal with a ByteDance subsidiary named Spring, which accounted for $24 million of its $33 million annual revenue in 2025. This contract allowed Spring to access 2,304 Nvidia B200 chips at Nscale’s Glomfjord, Norway, data center. Even though the Financial Times reports that this was legal and exploited loopholes in the U.S.’s ongoing export controls against China, it still exposes the company to regulatory and reputational risk, especially as Washington continues to restrict Chinese access to its most advanced AI accelerators.

This information only came to light after Nscale released its S-1 documents as it filed for an initial public offering in the U.S. There was also no direct mention of the TikTok owner or its subsidiary anywhere in its S-1 filing; instead, it was noted in a supporting document, a $105 million loan agreement with Macquarie, that Spring (SG) Pte Ltd, a Singaporean ByteDance subsidiary, was a significant customer. This credit, alongside a $35 million in equity, helped fund the purchase of advanced Nvidia AI GPUs, which also caught the attention of the chipmaker.

A month after Nscale drew the first tranche of the loan, it closed a deal with Microsoft and Nvidia, with Jensen Huang committing to invest more than $660 million into the company. This funding is part of an agreement between Nscale and Nvidia, under which the latter promised to buy large volumes of AI chips in the future. Nvidia has since increased its commitments to over $2 billion, plus an additional $860 million guarantee on Nscale’s lease at a Texas facility. Nscale has since been able to secure larger deals with other AI hyperscalers, such as a $44 billion deal with Microsoft and another $45 billion contract with Anthropic. The company said that this would make its Spring contract fall to below 20% of its overall revenue, and it is expected to shrink further as Nscale secures other Western clients.

Because of Spring’s link to ByteDance, Macquarie compelled Nscale to monitor how the Chinese subsidiary used the AI chips and report “compute anomalies or suspicious configurations” that might be in violation of the U.S.’s export controls. Nscale also had a third party conduct due diligence on both the TikTok parent and the Singapore-based subsidiary to ensure that the deal complied with the law.

Chinese companies have been taking advantage of these loopholes in the U.S.’s export controls to gain access to advanced AI chips. While they cannot import the most advanced Nvidia AI GPUs, like Blackwell and Vera Rubin, into China, they could still rent these AI chips from companies based abroad or by using subsidiaries founded outside of China to purchase them directly. However, the U.S. has recently closed the latter loophole, with the Trump administration working to close the former, too. It’s unclear if these moves would affect the Spring-Nscale deal, but the recent delays on gigawatt-scale data center projects in the U.S. mean that Nscale could potentially get more clients as AI hyperscalers race to secure compute from smaller facilities.

An Nvidia-backed startup plans to establish a Bitcoin mining operation in space before the year is out. Starcloud will fire up its cryptocurrency mining ASICs after its second spacecraft reaches orbit later this year. “We’ll be the first to mine Bitcoin in space,” claimed Starcloud CEO Philip Johnston in a recent interview with Documenting Bitcoin.

Starcloud was founded in 2024 with the headlining goal of moving data centers to space. “As launch costs fall, data centers in space will leverage continuous solar energy and radiative cooling, rapidly scaling to gigawatts while avoiding permitting constraints on Earth,” reads its mission statement. The firm spells out its proposition in far greater detail in a white paper on its website, highlighting the appeal of training AI in space. However, the ample supply of solar energy is also great for cryptomining.

So, when Starcloud-2 reaches orbit, it will be tasked with raising some crypto-cash. In the embedded interview segment, Johnston said: “We’ll have some Bitcoin mining ASICs on the second spacecraft launching later this year.” At the time of writing 1BTC is worth roughly $86,000, quite a way down from highs several months ago.

Starcloud-2 is just the second of four currently planned satellite launches from the startup. It is framed as the company's first commercial mission, and like the prior satellite, will feature a GPU cluster. Interestingly, the official Starcloud-2 project page doesn’t mention Bitcoin mining or ASICs, so Johnston’s reveal is quite eyebrow-raising. Moreover, for something that seems a bit like an afterthought, “Potentially it [Bitcoin mining] is one of the most compelling use cases of space compute,” the Starcloud CEO told Documenting Bitcoin.

Johnson provided some financial insight into why there is now a Bitcoin side gig or pivot being considered. He cited the lower costs of ASICs vs. GPUs: they are about 30x cheaper than GPUs per kilowatt, he told the host. A specific example given was a 1KW Nvidia B200 chip at $30,000 vs. a 1KW ASIC at $1,000. Solving the energy supply situation in space for GPUs, or for ASICs, isn’t relevant to the business, said Johnson. We wonder what Nvidia will think about that.

While the availability of near-uninterrupted solar power is a big attraction of space-based compute, there are downsides. For example, satellites aren’t easy to service, and convection cooling doesn’t work in a vacuum. Commercial space-based data centers are so new that some of the problems and solutions will only become entirely clear after pioneering deployments like the Starcloud-2.

Huawei's impressive next-generation Ascend 900-series AI accelerators will be offered only in China, not internationally, as the company struggles to meet domestic demand amid capacity constraints, the company announced this week. While the upcoming Ascend 960-series neural processing units (NPUs) could rival some of AMD's and Nvidia's existing AI GPUs, demand for these units outside of China was not guaranteed anyway.

"Since we do not have enough capacity to even satisfy the ​demand in China, we do not have a plan to expand into the international market in a fully-fledged way," said Eric Xu, rotating chairman of Huawei, on the sidelines of the company's Huawei Connect conference, Reuters reports. He added that Huawei supplies limited volumes to 'some countries where demand is particularly strong,' though he did not elaborate.

Huawei this week unveiled its latest AI accelerator roadmap, revealing major training and inference performance gains for its next-generation Ascend 960, 970, and 980 NPUs over the existing Ascend 910C and Ascend 950-series. The Ascend 960DT and 960PR are set to increase their FP8 training performance to 2 PFLOPS and their FP4 inference performance to 4 PFLOPS and 8 PFLOPS, respectively, in 2027. Meanwhile, their successors, Ascend 970 and Ascend 980, are projected to increase their FP4 performance to 14 PFLOPS and 28 PFLOPS, respectively, in the coming years.

Huawei Ascend vs Nvidia AI GPUs

*Preliminary data
S/D - Sparse and Dense
T/I - Training and Inference

But while the upcoming Ascend NPUs will be considerably faster than their predecessors, particularly for inference, they will remain well behind Nvidia's previous- and current-generation accelerators, at least in raw compute performance. Huawei's 2027 Ascend 960DT is projected to deliver 2 FP8 TFLOPS for training, compared with Nvidia's 4 FP8 TFLOPS for the H200, released in 2023. The Ascend 960PR is expected to offer 8 FP4 PFLOPS for training, which is far behind Nvidia's B300, which delivers 15–20 NVFP4 PFLOPS. Even the Ascend 980, targeted for 2029, is projected to reach 7.2 FP8 PFLOPS and 28 FP4 PFLOPS, well below Nvidia's R200, which is on track to deliver 17.5 FP8 PFLOPS and 35/50 FP4 PFLOPS this year.

Such a massive performance difference with leading AI hardware will reinforce Huawei's reliance on massive system-level scaling rather than chip-for-chip performance to compete with Nvidia. But massive system-level scaling comes with massive power consumption, which will make Huawei's next-generation Atlas SuperPoDs and SuperClusters considerably less competitive in markets that can access hardware from AMD or Nvidia.

Huawei is in an interesting paradoxical situation. On the one hand, its integration efforts like near-package optics (NPO) clearly free up capacity on 'older' nodes that can be used for other components of AI platforms. But on the other hand, SMIC's inability to ramp production on 7nm and 6nm-class nodes limits Huawei's ability to supply its AI hardware anyway, which is why it can barely meet demand.

Then again, while Huawei's Atlas SuperPoDs with up to 15,488 Ascend 960 NPUs can deliver up to 30 FP8 EFLOPS and 120 FP4 EFLOPS performance by far exceeding the capabilities of Nvidia's NVL72 clusters with a 72-GPU scale-up world size, their performance-per-watt is poised to be dramatically lower compared to Nvidia's architectures, which means that demand for such hardware outside of China will be limited at best. That said, a global AI hardware push doesn't make much sense for Huawei right now. What perhaps does make sense is offering cloud access to its hardware to various academic and research customers to popularize its CANN software stack.

AX Gaming has revealed its latest seasonal anime-themed limited-edition graphics card, which, this time, is designed to please your nose as much as your eyes. The Inno3D subsidiary has released similarly styled GPUs before, but new for Autumn / Fall is the “innovative e-sports aromatherapy” component in its GeForce RTX 5070 12G Autumn Limited Edition (machine translation).

Smellivision is included as part of this limited edition. The graphics card maker says this product delivers a form of aromatherapy. You will be able to smell the “autumn-like” aroma even from a cold start, it says. There is no information about aroma oil refills. It would be good to see the fragrance module to see how this subsystem works.

The anime character you see in the main picture, and who stars on the metal backplate, is called ‘Snow Li.’ She sits with the GeForce RTX logo to one side, with an Autumn / Starry Moon motif beneath it. To the right is an ornate venting pattern for the third fan to push air through the cooling fins.

AX Gaming’s substantial triple-fan card features a white-purple gradient wrapping around. “The fan blades are printed with exclusive logos such as Osmanthus, AX, and Star and Moon, making it lively and exquisite,” the maker romanticizes. “The edges of the chassis are decorated with colorful maple leaves and tiny star patterns, like starlight falling on a maple forest on an autumn night.” We also note this isn’t just a vinyl wrap. AX Gaming highlights that the design mixes “UV printing and multi-surface engraving techniques.” Even the fan blades have some logos.

This seasonal graphics card release has ARGB maple leaf lighting, and its “flower-shaped ventilation holes are inlaid with LEDs and support synchronized lighting effects.” Also, perhaps difficult to believe, AX Gaming says that the light and shadows thrown by the powered-up graphics card “creating a full-on autumn night starry night atmosphere.”

Beyond the visuals and fragrance, this is an ordinary but decent-sounding RTX 5070. You’ll still get your 6,144 CUDA cores, 12GB of GDDR7, and agreeable AAA gaming performance with access to GeForce features. Some non-cosmetic design choices that are welcome include the dual-layer metal magnetic backplate, five 8mm ultra-thick composite heat pipes, and the hidden power cabling.

In its press release, AX Gaming says the "Autumn Stars and Moon, Maple Leaves in Full Bloom" themed card is a strictly limited (quantity/time) release. That’s bound to make it even more expensive than a regular RTX 5070, if you can order/reserve one.

The developers of the open-source PlayStation 3 emulator RPCS3 are celebrating finding a workaround for an Nvidia driver bug. The social media celebration stems from their testing showing that some PC configurations with Nvidia GPUs can enjoy up to 37% faster gaming performance. That’s more than a small wrinkle that’s been ironed out.

We don’t know the exact nature of the Nvidia driver bug here, but the RPCS3 devs say that it bottlenecks the performance of their PS3 emulator. The first example cited provides two PC configurations running Red Dead Redemption in a scene featuring a paddle steamer. This is a popular ‘benchmarking’ scene focused on the Blackwater docks in the game, featuring relatively consistent camera paths and water effects.

Those are very gratifying performance uplifts resulting from a driver bug workaround. Plenty of readers are probably more interested if the purported bug might also be bottlenecking native PC gaming performance. If it were, that would be a hugely significant find/workaround from the RPCS3 team, specifically ‘Yahfz.’ Please note that “AMD GPU drivers do not have this bug, thus seeing no change,” say the emulator developers.

Gran Turismo 5 ran 37% faster, used far less VRAM

Follow-up social media postings underline that RDR isn’t an outlier, and “most games are impacted” by the bottleneck in the Nvidia driver. Great frame rate improvements can also be seen when using lower-end GPUs, too. The open sourcerers tested Gran Turismo 5 and saw frame rates climb by an incredible 37%, while VRAM usage dropped from 7.1 to 4.8GB. Meanwhile, playing Saints Row IV on a more mainstream GTX 1060 saw a 20% improvement in performance.

Sadly, RPCS3 dev reach-outs to Nvidia are falling on deaf ears. Another follow-up tweet states that “We tried using the developer forums in the past to no avail and gave up.” It then asks @Nvidia to establish a direct communication channel to receive driver bug reports. Nvidia Software QA boss Manuel Guzman has now replied directly, so we will see what happens in due course.

Hardware leaker Kopite7Kimi, who has an exceptional track record, posted on X that Nvidia's RTX 60 series GPUs, based on the Rubin architecture, might not be released until at least 2028. The leaker previously believed that we might see a new generation of graphics cards in late 2027, but now says: "Unfortunately, GR20x is a product from 2028. That means they have postponed again." We reached out to Nvidia regarding the post, and a company spokesperson stated that they "don't comment on rumors or unannounced products."

While Nvidia issued its standard boilerplate response, Kopite7Kimi has a historically near-spotless record when it comes to speaking about Nvidia hardware leaks, and it seems that this time, consumers may have to wait a little longer. Both the Ada Lovelace and Blackwell architectures in RTX 40 and RTX 50 series GPUs share the same TSMC 4N process node, so if the leaker's words are to be believed, it will take Nvidia a total of six years to move its consumer GPUs to the more advanced TSMC 3N or 3NP node that its Rubin accelerators currently utilize.

Now, the potential reasons behind the delay could be twofold: The first is the numerous reports we've heard about a potential RTX 50 Super series Blackwell refresh being sidelined, despite reportedly being ready. The claimed reason behind the delay or potential cancellation of the rumored RTX 50 Super series is the use of denser 3GB GDDR7 VRAM chips, which just so happen to have appeared in Nvidia's recently released RTX Pro 5500 and share many similarities with the RTX 5090.

So, it's quite possible that the delays in delivering a GeForce product based on the Rubin architecture, which has already begun shipping as "the fastest ramp in company history," is down to careful allocation of resources on Nvidia's part, as the company expects to make $20 billion from Vera Rubin in Q3 2027.

Secondly, Nvidia can only acquire so many resources to supply its products, which demand vast amounts of memory, and has also locked in a certain amount of wafer supply from TSMC. So, it's highly probable that Nvidia is focusing on enterprise and workstation products ahead of a consumer launch, especially as component costs remain eye-wateringly high. Nvidia's highly profitable data center arm is currently experiencing soaring demand from the ongoing AI buildout, meaning there's less onus to deliver a potentially less profitable consumer GPU, while there's still cash to be made from hyperscalers.

It's not unusual to see enterprise products use more advanced nodes before arriving in consumer products, but this time around, at least according to Kopite7Kimi, the Blackwell product cycle is poised to be unusually long. This is particularly bad news for consumers, as the RTX 5090 is experiencing a pricing squeeze, which is seeing costs soaring all the way above $9,000 — if you're lucky enough to find one listed online.

For now, your best bet is to just hold on to the GPU you currently have until the storm blows over and a new generation of graphics cards is released, unless you're willing to pay the dizzyingly high costs that the market demands right now.

Nvidia’s Turing GPUs became available to enthusiasts for the first time on this day eight years ago. This wasn’t just another accelerated GPU generation, though, at least according to Nvidia. The GeForce 20 series pioneered a “20 years in the making” ability to deliver hybrid graphics enhanced by real-time ray tracing. Nvidia made this new technology available at the high-end first, via the GeForce RTX 2080 and RTX 2080 Ti (review links). The more mainstream RTX 2070 wouldn’t become available until October.

In a blog post a month ahead of the release day, Nvidia CEO Jensen Huang didn't hold back in heralding the arrival of Turning for PC gaming graphics. “Turing opens up a new golden age of gaming, with realism only possible with ray tracing, which most people thought was still a decade away.” He went on to claim that “Once you see an RTX game, you can’t go back.”

Despite Huang’s boasts we can look back and confidently say that many gamers still don’t care about ray tracing. Nor do they wish to endure the performance impact even the latest generation mainstream graphics cards from Nvidia (and AMD, and Intel) still suffer from when this feature is turned on. Moreover, at the time of launch back in 2018, and for quite a few months onwards, there was little in the way of content that made the new RTX 20 series' distinguishing feature seem worthwhile. Early in-game showcases that garnered a lot of pixel peeping and chin rubbing included Battlefield V, Metro Exodus, and Shadow of the Tomb Raider.

Our review of the RTX 2080 Founders Edition noted that this new second-fastest Turing architecture card managed to outpace the legendary GTX 1080 Ti. However, we grumbled about it being $100 more expensive than the former flagship. During this era, Nvidia Founders Editions were actually more expensive than the base models from AIBs. Thus the RTX 2080 FE had an MSRP of $799, but you might have been able to pick up an Asus, Colorful, EVGA, Gainward, Galaxy, Gigabyte, Inno3D, MSI, Palit, PNY, or Zotac for $699.

Our review of the GeForce RTX 2080 Ti flagship was more positive, despite the conclusion that the “$1200 price tag is out of reach for most gamers.” The 71% uplift in price wasn’t really worth it for the ~26% faster performance vs the GTX 1080 Ti. It was also about 20% faster in gaming than the pro Titan V, though.

Our review of the RTX 2080 Ti was 14 pages long, but here’s a direct link to some gaming benchmarks featuring the likes of Tom Clancy’s Ghost Recon, The Witcher 3, and World of Warcraft: Battle for Azeroth. During gaming, this new consumer flagship would typically consume about 280W.

Eight years later, we can say that realtime ray tracing has indeed proved popular. Even consoles and mobile games now use and boast of this visual tech, and probably couldn’t ignore it without getting sidelined. But adoption has been slow, and it has taken yet more layers of technology, such as DLSS for the green team, to make ray tracing at all accessible for budget / mainstream gamers, even in 2026.

Do you still rock a Turing GPU in 2026? If you do, or are just curious about how well an 8-year-old card would do in 2026, I'm sorry to say that Turing has slipped off the end of our famous graphics card hierarchy. However, the following generation RTX 3070 at $499 offered very similar performance to the prior-gen $1,199 flagship. Those were the days (except that the cryptomining craze happened).

Update 10/1/2026 3:00am PT: Scaleout Systems sent a statement to Tom's Hardware to clarify that the demo simulated an airburst and did not drop explosives. The article has been updated accordingly. The company also added, "The team is very clear on the importance of keeping a human in the loop and not deploying systems that remove that oversight. In the demonstration, the drone used onboard AI to search for, detect and prioritise potential targets. A human operator then verified the target and authorised the drone to continue. Once authorised, the drone autonomously navigated to the target’s last known position and carried out its final approach."

Updated article:

Drones built with small, non-frontier computer-vision models autonomously identified and attacked targets in a recent demo, Ars Technica reports. Scaleout Systems, a Swedish AI startup, used a low-cost loitering munition from BAE Systems Bofors to strike a target as part of the Affordable Loitering Modular Ammunition (ALMA) program. BAE’s Winter Demo 2026 had the drone detecting and geolocating targets before ranking an armored engineering vehicle highest, and after a human operator verified the target, flying to it and positioning itself to simulate deploying an airburst munition.

Scaleout’s demo video, “Technical Demo: Onboard Edge Intelligence for Autonomous UAV Missions,” shows the company’s drone spotting potential threats with AI, with all processing handled onboard. Beyond a button press to start the system, manual input is optional; the designated pilot remained a failsafe controller but is optimally used to verify the target. The mission flew under human-set parameters to simulate engaging an armored engineering vehicle and required about 200 seconds of recon, with the full mission completed in under 320 seconds.

The report referred to the munition as a “kamikaze drone,” but the program’s own term, “loitering munition,” is more descriptive. Most of the mission is spent searching, ranking targets, and waiting to strike. The company combines Scaleout Edge and “federated learning” in a “Tactical Computer Vision Network (TCVN).” Devices train AI locally and share model updates for rapid adaptation. Scaleout’s project, FEDAIR, is part of NATO’s DIANA accelerator, receiving 100,000 euros of development funding, training, and test access.

In a February post, Scaleout described a separate “arctic strike demonstration” at BTC Karlskoga, Sweden. The mission was flown at -18 degrees Celsius, about 0 degrees Fahrenheit, in the snow. There, an Airolit S1 airframe ran the YOLOv8 Nano object-detection model on Nvidia’s Jetson Orin Nano. Scaleout claimed a framerate of 30 fps at around 20 m/s, with a target latency of 30 ms or less and about 30 ms sustained in the field. Ranging without a depth sensor is possible using a pinhole camera model combined with known object size.

Scaleout’s follow-up June 30 post, “Resilient Edge AI for ISR: Inside Our Swedish Air Force Demonstration,” spoke of another test with Scaleout Edge “already deployed under an active licence.” The network used two ground nodes under a cloud-hosted Scaleout Edge control plane. The first was ALPHA, a forward-deployed node at the air base with a stable link, and the second BRAVO, a lab node in Uppsala whose connection was first degraded, then cut entirely. BRAVO kept inference and active learning at full frame rate while offline, logging detections locally, then backfilled on reconnect in priority order for heartbeat, critical alerts, drift, model updates, and telemetry. This test mimics the impact of external interference.

The demonstrations, license, and funding come before any official NATO procurement order or actual combat deployment. Concerns about lethal autonomous weapons are being addressed by a UN expert group. Its recent report concluded that human judgment and control are required to comply with the laws of war; its recommendations will be reviewed in Geneva in November. Still, Scaleout’s demonstrations show autonomous target selection running on the airframe, and in the February test it ran on a board hobbyists can buy. Whether BAE Systems Bofors moves ALMA from demonstration to procurement remains to be seen.

Jensen Huang, the chief executive of Nvidia, said artificial intelligence will not destroy humanity by the end of the decade, Bloomberg reports, citing a CBS interview. Huang contends that while AI is developing at an extremely rapid pace, doomsday scenarios because of AI are largely unsubstantiated, and it makes no sense to 'stir fear across America.'

"I completely disagree that AI will destroy the world by 2030," Huang said in an interview with CBS Sunday Morning (set to be aired on Sunday).

"I believe the claims of the end of the world, stirring fear across America, and doing it by people who are doing it makes no sense to me. So, they must be doing it for ulterior reasons. Maybe it is political, maybe it is otherwise, maybe it is just attention-grabbing […]. However this is characterized, 2030 is not going to be the end of the world. There is 0% chance that is going to be the end of the world."

Huang, who leads the company that leads the market in AI hardware sales, is responding to Evan Hubinger, the former Alignment Science organization lead at Anthropic, who said there was an over 10% chance that AI would destroy humanity within the next decade.

"We really do earnestly believe AI could kill all humans," Hubinger wrote in an X post. "I personally think it is >10% within the next decade. I believe Anthropic is trying its best, but we do not yet have a plan to solve alignment for superintelligence and are not clearly on track to."

Following reports that OpenAI's rogue agents attacked Hugging Face and communicated with each other on abandoned wikis and websites, chief executives of Anthropic and OpenAI called for guardrails and even slowing down development of new AI models, as the dangers they pose are not completely evident even to their developers.

The head of Nvidia states that AI can be safely managed by its developers, so no regulations from governments are needed beyond what is already in place. Meanwhile, he also says that products shipped must be completely safe.

"We should go as fast as we can, irrespective of anyone else," Huang said. "But we would never ever, and never should, ship products before they’re ready and deliver products that are unsafe."

2019’s Control followed Jesse Faden into the ever-shifting, paranormally corrupted brutalist innards of the Oldest House, the headquarters of the Federal Bureau of Control, where she became the new FBC director, fought the invading forces of the Hiss, and sought the truth about the fate of her kidnapped brother Dylan.

Control Resonant marks the next chapter in the siblings’ story, as Dylan reawakens to discover that Jesse has gone missing and that the Hiss threat has escaped the Oldest House and corrupted Manhattan. Using his own powers and guided by the mysterious Board, Dylan sets out to find Jesse, combat the Hiss incursion, and uncover the mysteries of a new paranormal entity at work in the twisted Manhattan cityscape.

We’ve had access to Control Resonant for the past few days, and we’ve been exploring its performance and image quality across a range of hardware and settings.

Control was one of the first games to show off the capabilities of GeForce RTX 20-series graphics cards and their ray-tracing capabilities, and it was also one of the first titles to incorporate DLSS upscaling. It’s only fitting, then, that Resonant is a technical showcase of its own. It features path-traced lighting effects bolstered by Nvidia’s RTX Mega Geometry tech, as well as support for the full suite of DLSS 4.5 features: Super Resolution (aka upscaling), Ray Reconstruction, and Multi Frame Generation.

With that extensive spread of cutting-edge rendering tech at its disposal, I expected Resonant to look incredible, as both Control and Alan Wake II did before it.

Even without ray tracing or path tracing and DLSS Ray Reconstruction, Control Resonant already looks good, as we’ve come to expect from Remedy games. If performance is a priority, you certainly won’t make the game bad by leaving RT off.

But ray tracing or path tracing is certainly worth enabling if you can. Outdoors, RT primarily improves the rather blobby shadows and soft reflections that appear in the numerous puddles around Manhattan.

Sometimes this is subtle; other times it’s dramatic, as with the railing shadow above.

At its highest settings, path tracing turns shadows into the crisp, accurate renderings you would expect in direct sunlight and makes reflections much stronger and clearer not only in water but in glass like car and shop windows.

Inside, RT models the behavior of strong artificial light sources more accurately. Overhead lights properly shadow Dylan’s face from above and let his back fall into darkness if he’s occluding them. Metallic objects like trash cans look and shine like roughed-up metal as you move around them, not a flat and static game asset.

In the examples above, you can see how path tracing accurately represents the light from a fluorescent tube outside the storeroom, casting a hard shadow on the wall and a green glare on Dylan's uniform. WIth RT off, this effect is only represented on the floor.

Everything looks more alive and more real, in other words, and these subtleties need to be appreciated in motion, not just in stills. I enable path tracing for those moments where something unexpectedly realistic catches the corner of my eye and makes me double back to explore it.

Despite all the environmental magic that path tracing and DLSS Ray Reconstruction lend to Control Resonant’s setting, that level of polish isn’t evenly applied across all the game’s assets.

Control stood out to me not only for its lighting effects but also for its amazing facial modeling work. In Resonant, however, the character models of Dylan, Zoe, and supporting characters like Langston all look like a step back from the level of detail achieved in past Remedy games. Maybe I’ve been staring at too much DLSS 5 output, but the faces in Resonant don’t match up to the environmental quality, and that gap stands out.

Control Resonant raster gaming performance

Our gaming performance results start off with native res, raster-only settings.

At 1080p Ultra with RT off, Control Resonant is already placing heavy demands on our tested cards. You want an RTX 5060 Ti or better or Radeon RX 9060 XT or better for 60 FPS (ish) average among current-gen cards, or an RTX 4070, RTX 3080, or RX 7800 XT among earlier cards. The RTX 3060 can’t even break 30 FPS on average, and that’

Radeons also tend to turn in lower 1% lows than GeForces, although that didn’t translate to a bumpy experience with our high-refresh-rate, variable-refresh-rate monitor. If you’re only playing on a 60 Hz display, that difference may be more apparent.

Even though it still lands above a 60 FPS average at 1080p, the older Radeon RX 6800 XT is stuck with FSR 3.x upscaling, so its output image quality is noticeably worse than RX 9000- or RX 7000-series cards that get FSR 4.1.

Control Resonant is also the first game we’ve tested where the demands of running the DLSS 4.5 transformer upscaling model hits RTX 30-series products hard, so if you’re not trying to perform heroic feats of upscaling, you can probably get a few frames per second back by shifting to older DLSS models at the cost of some output image quality.

2560x1440 Ultra is similarly punishing. Here, the RX 9070 GRE and RTX 4070 Ti end up just below a 60 FPS average, while the RX 9070 and RTX 5070 Ti end up slightly above it. Any weaker card will likely need lower settings, a dose of upscaling, or both to deliver a smoother experience.

Finally, at 4K, only the RTX 5090 can deliver the coveted 60 FPS average. Some degree of upscaling, frame generation, or both will be needed on every other graphics card here for this resolution (and possibly on the RTX 5090, depending on your desired experience).

Control Resonant ray-traced performance

Given the already punishing raster baselines we just saw, we had little reason to believe that RT would be an attainable feat in Control Resonant at native resolutions, and it’s not.

Enabling even the medium RT preset at 1080p smacks down a wide swath of our tested cards. The RTX 4070 Ti, RX 7900 XTX, and RTX 5070 all land just below the 60 FPS mark, while the RX 9070 XT lands right on it. The RTX 5070 Ti offers a significantly more comfortable average frame rate and 1% lows for a smoother experience.

At 1440p, even the RTX 5080 isn’t quite enough to manage a 60 FPS average, and at 4K, not even the RTX 5090 can conquer Control Resonant with these settings. Oof.

These results mean that upscaling and frame generation will be mandatory if we’re going to chase RT glory in Control Resonant.

Control Resonant path-traced performance with upscaling and frame generation

There are two problems with trying to gauge competitive performance when we go ham on upscaling and frame generation, as we’re about to here.

For one, DLSS 4.5 produces a much more stable image than FSR 4, especially at extreme settings like 4K Ultra Performance. FSR 4 can look slightly sharper at rest, as reviewers usually are when creating side-by-side comparison images, but in motion, it can exhibit strong and distracting flickering artifacts, especially on fine structures with repeating elements like fire escapes and chain-link fences.

DLSS 4.5 isn’t entirely free of those artifacts, but they affect much smaller portions of the screen and to a much less intrusive degree.

The net result is that DLSS 4.5 frames are not the same as FSR 4.x frames, so that makes for two different experiential variables we have to account for in any comparison: raw performance and delivered image quality.

And the third variable is that FSR Frame Generation (whether in its ML-powered or traditional shader-based form) isn’t backed up with a robust latency reduction technology like Nvidia’s Reflex or Intel’s Xe Low Latency in Control Resonant.

Radeon Anti-Lag does reduce input latency, but even with the RX 9070 XT in full path-tracing trim, it is at best taking a high input latency and making it somewhat less high. Even with Anti-Lag enabled on the RX 9070 XT, the average latency before framegen is enabled is well above the roughly 60 ms threshold we try not to exceed.

What’s frustrating is that AMD has a more robust input latency handler for devs to use. Radeon Anti-Lag 2 exists and works well in Cyberpunk 2077. But like FSR 4 Ray Regeneration, it’s not implemented in Control Resonant.

Worse, FSR Frame Generation isn’t compatible with the performance counters that Nvidia FrameView monitors to estimate PC latency, so we’re left with only our eyes and our guts to decide whether the net result of FSR FG is a truly playable experience. Even Intel’s XeFG supports these performance counters properly, so it’s all the more annoying that AMD doesn’t, even two years after the arrival of FSR 3.1.

So before we get into these results, bear a couple asterisks in mind. Path tracing as Nvidia envisions it and as Control Resonant implements it—with upscaling, denoising, frame generation, and lag reduction all working together—is not possible to replicate 100% on AMD hardware.

We include AMD results (and RTX 30-series cards with FSR framegen) as a sort of best-effort reference point for maxed-out RT behavior with this game, but because these are doubled frame rates, any figure below 60 FPS on these charts for RTX 30-series and AMD cards should be understood as an unpleasant (or even unplayable) experience, whether due to input latency, significant artifacting, or both.

At 1080p with Ultra RT, DLSS Quality, 2X Frame Generation, and DLSS 4.5 Ray Reconstruction, only the RTX 5050 and RTX 4060 fail to deliver playable input latency. Even the RTX 5060 squeaks in under the 60 ms threshold we want for a responsive experience. That’s great news for the accessibility of path tracing on relatively affordable hardware, or at least what used to be relatively affordable hardware.

At 1440p with DLSS Performance, the RTX 5060 just barely fails to meet the 60ms mark for acceptable input latency. This is more the territory of the RTX 5060 Ti and up.

And at 4K with DLSS Ultra Performance, we’d strongly recommend an RTX 4070 Ti or better, although you can get away with an RTX 5060 Ti or RTX 4070 in a pinch.

The combination of DLSS 4.5 technologies and Reflex makes for a perfectly playable path tracing experience in Control Resonant across a wide range of hardware, and as we’ve already noted, it’s impossible for AMD gamers to enjoy the same experience.

Bottom line

Control Resonant marks a new chapter for one of my favorite gaming sagas, and I was thrilled to step back into the Oldest House. The Hiss-corrupted Manhattan setting looks fantastic, even if I’m still waiting for the story to fully grab me. I need to fully explore the full range of weapon forms, skill trees, and builds the game offers as our protagonist Dylan Faden fights through hordes of Hiss in close-quarters, split-second combat. Death is just one wrong move away in this game, and I've been frequently reminded of that fact.

For all its path-traced environmental splendor, there are some wrinkles on the tech side in Control Resonant that stand out. The character models, even main characters like Dylan and Zoe, look a bit basic for a 2026 game, especially with all the other graphical fireworks happening around them. Perhaps I’m ruined by DLSS 5 (and perhaps this game was meant to have it alongside more capable hardware), but I've come to expect more from Remedy given the artistic heights of Control and Alan Wake II.

Any graphical shortcomings like that stand out given how crushing Control Resonant is for many graphics cards. This is a game that really demands some degree of upscaling for good performance on less powerful hardware, and Ultra settings may not be possible on older, lower-end cards, period.

But at least for Nvidia RTX 50-series gamers, the aggregate improvements to the DLSS 4.5 software stack in 2026 make Control Resonant’s fanciest graphics settings incredibly accessible, and you really should take advantage of every DLSS feature at your disposal in this title.

Thanks to DLSS 4.5 Super Resolution’s remarkable capabilities, even the RTX 5060 can do path tracing at 1080p with DLSS Quality upscaling, DLSS Ray Reconstruction, and frame generation enabled, all with a reasonable input latency and solid output image quality. And if you’re willing to tolerate slightly higher input latency, you can get away with the same settings at 1440p using DLSS Performance.

The RTX 5070 provides a great experience at all resolutions with those same settings, and you really only need more powerful hardware like an RTX 5070 Ti if you’re chasing higher DLSS input resolutions. I would normally shout out the RTX 5060 Ti here, but that card’s 16GB version especially is so close in price to the RTX 5070 that you should just get the more powerful card if gaming is your goal.

The situation is less rosy on the AMD side, even for the latest RDNA 4 cards. FSR 4.x upscaling is available in this title for RX 9000- and RX 7000-series graphics cards, but it isn’t as strong at image reconstruction as DLSS 4.5, so lower RT settings and higher FSR input resolutions may be necessary on RX 9000-series cards than on comparable RTX 50-series hardware to achieve the right balance of performance and image quality.

And the lagginess of FSR Frame Generation means that you’ll have to carefully test that feature if you want to try to use it to boost smoothness, unlike the general no-brainer that is DLSS 4.5 (M)FG.

All told, the incomplete implementation of AMD’s gaming tech suite in Control Resonant, as well as the generally lower RT performance of RDNA 4 cards versus Blackwell, means that you can max out RT settings in this game, but you still won’t enjoy the same image quality as on GeForce cards, especially in the areas of the frame that DLSS 4.5 Ray Reconstruction enhances.

Once again, this situation proves that having a software feature in your toolkit is meaningless if you can’t convince a developer to adopt it. Every new game that comes out with only a patchwork of AMD features like this is another paper cut for RX 9000-series GPU owners who are left with a distinctly lower-tier experience than RTX 50-series cards get. Fine wine, this is not.

But maybe you’re totally unmoved by RT and neural rendering techniques and just want strong raster performance with a touch of upscaling where it’s needed. RX 9000-series cards deliver that experience with strong performance per dollar in today’s ever-pricier gaming graphics card market.

Going off what I’ve played of Control Resonant so far, I’d suggest checking some full gameplay reviews to see if it’s for you and perhaps giving it a post-launch point release or two to iron out some minor technical issues we saw.

But one thing is for certain: the full array of Nvidia tech in this title, and its general accessibility across a wide range of hardware, means that a GeForce graphics card is the best way to play it.

A modder by the name of MAAN has reportedly gotten Nvidia DLSS 5 working in a browser window with an interactive demo, according to a report by VideoCardz. The developer said the technique also works on macOS. The demo is hosted on Cloudflare Workers with some default scenes, starting with "Cowboy Gramps," with a variety of adjustable settings and a comparison view.

DLSS 5 is Nvidia's neural rendering feature used to improve graphical quality using AI. Nvidia launched it earlier this month for NBA 2K27, the first game with official support for the technology. DLSS 5 is officially RTX 50-series only, aside from GeForce NOW. The DLSS 5 DLL file has since been pushed onto RTX 40- and RTX 30-series, and even AMD hardware. There is also a mod to unlock it for RTX 20-series hardware.

According to MAAN, the demo uses model weights extracted from a leaked DLSS 5 library file. They did not know whether this file differs from the official one. Normally, Nvidia offers DLSS through its NGX interface or the Streamline SDK on DirectX and Vulkan. The documentation does not list WebGL or WebGPU, the outlet noted. MAAN plans to publish the source code on GitHub this weekend.

In our quick test of the demo on an RTX 40-series desktop, the 3D viewer was smooth to rotate, but each DLSS 5 render took a second or two and was notably slow in live mode.

MAAN said that the demo is DLSS 5's neural network reimplemented using WebGPU compute shaders. The weights are around 147MB, with a JavaScript runtime of around 1MB compressed. WebGPU has no trouble running on macOS, so the only surprise here is the DLSS part. Normally this needs an RTX GPU and Nvidia's driver, but a WebGPU implementation would not inherently require Nvidia hardware.

The outlet suggested the approach could suit architecture, 3D model previews, and other work where real-time speed matters less. MAAN's X post said users "can try it with your own models" and the demo's page accepts common 3D formats by file or drag-and-drop. Due to the technology's current restrictions, a browser demo is a way for more people to see the effect on a model without owning the game or appropriate hardware. Nvidia has said official RTX 40-series support is coming.

Apple is reportedly developing AI servers based on its own M-series processors and is evaluating NVLink Fusion technology for interconnects, according to The Information. The machines are expected to use M8 Ultra processors and arrive in 2029, the report claims.

For now, the usage of the NVLink Fusion platform is not formalized and has not been confirmed by either Apple or Nvidia — but an Apple decision to use it instead of competing solutions may have significantly broader market implications than just Apple using Nvidia hardware.

Apple looking for fast interconnects

Apple is reportedly considering at least two server configurations: a smaller machine equipped with two M8 Ultra processors and a higher-end version featuring four M8 Ultra system-on-chips. Although Apple has its own UltraFusion technology for stitching two high-end SoCs together seamlessly, it looks like the company does not have a proper solution for scale-up and scale-out connectivity of its processors, which is where Nvidia's NVLink Fusion comes into play. Apparently, Apple wants to use NVLink infrastructure, which includes not only an interconnection protocol, but also switches, chiplets that add NVLink connectivity, and a software stack, for its servers. The project was reportedly initiated around a year ago and was backed by John Ternus while he headed Apple's hardware engineering organization.

Apple already builds custom servers for Private Cloud Compute, which handle AI workloads too demanding for local execution on iPhones and Macs, The Information claims. Most of these machines use Apple's internally developed connectivity technologies, which are reportedly too slow and costly for large-scale commercial deployments, which is why Apple is looking elsewhere.

More than NVLink?

The Information specifically mentions Apple's need for connectivity technology suitable for large-scale deployments, although it does not explain exactly what this means architecturally. If the publication is referring to connecting multiple servers into larger clusters, this would normally be the job of scale-out technologies such as Ethernet or InfiniBand, rather than a scale-up fabric such as NVLink. Nvidia originally developed its NVLink fabric technology to scale-up performance of its accelerators, so the technology is optimized for accelerator-to-accelerator connectivity and enables a rack of Nvidia GPUs to function as a tightly coupled compute domain. There is a different implementation called NVLink-C2C, which is a coherent chip-to-chip interface for connecting CPUs to accelerators and CPUs to CPUs

Meanwhile, modern Apple M Pro and M Ultra processors are system-in-packages consisting of a CPU chiplet and a GPU/neural engine chiplet, which are stitched together using TSMC's SoIC-mH technology. If Apple continues to use this architecture (very likely), an M8 Ultra processor can be considered as a CPU and an accelerator. However, this raises the question of how Apple intends to connect M8 Ultra processors to NVLink and which components of the SiP would participate in the NVLink domain. One possibility is that Apple could expose the accelerator portion of M8 Ultra to NVLink through an NVLink Fusion chiplet, which effectively means it will treat it as an accelerator for a scale-up domain. Another possibility is that Apple is developing a different accelerator architecture for its servers, perhaps by simply placing the GPU/NPU chiplet onto a separate substrate/interposer and equipping it with its own memory, though there is currently no evidence that confirms such a design for a chip that is years away.

Another thing to keep in mind is that Apple is a member of the UALink Consortium, an organization overseeing development of industry-standard UALink accelerator-to-accelerator interconnections that supports up to 1,024 accelerators. While for now there is a limited choice of UALink switches, by 2029, there will be industry-standard switches offering different performance and capabilities, which makes the choice of NVLink as a scale-up fabric even stranger.

One possible explanation is that Apple is interested in considerably more than NVLink itself. NVLink Fusion is part of Nvidia's rack-scale and data center infrastructure architecture, which can combine NVLink scale-up connectivity with Nvidia's Spectrum-X Ethernet or Quantum-X InfiniBand scale-out networks, including switches equipped with co-packaged optics. Thus, Apple could potentially adopt Nvidia technology for both scale-up and scale-out connectivity instead of developing an entire data center networking stack of its own. This is merely speculation for now, but such an approach would effectively mean that Apple is building AI servers around significant portions of Nvidia's data center architecture while retaining its own processors and not using Nvidia accelerators. If this happens, this will be a testament that Nvidia is now setting de facto standards for AI data centers, no matter which AI accelerators and CPUs are used.

Burying the hatchet?

Without a doubt, Nvidia is a leading supplier of data center hardware, so it is logical for Apple to work with the company if the two companies are indeed working together on Apple's data center platform.

Apple and Nvidia are not exactly good partners. The feud between the two companies began in the early 2000s, when Steve Jobs accused Nvidia of infringing on Pixar's patents on which Nvidia responded that it owned more graphics IP than Pixar and therefore could sue the company. Later on, Apple and Nvidia had disagreements over GPU design decisions that the latter supplied to the former. However, then came 'Bumpgate' as Nvidia supplied Apple and other PC makers defective GPUs in 2007 – 2008, did not acknowledge the problem, and then resisted fully compensating Apple and other PC makers for their repair costs, which is when the relationship between the companies got especially dire. Apple continued to use Nvidia GPUs till 2014 or 2015, at which point it switched to AMD's Radeon, and then abandoned discrete third-party GPUs altogether.

More recently, Apple started to use Nvidia's hardware again. The latest Siri AI is primarily powered by Apple Foundation Models developed in collaboration with Google using Gemini technology. Server-side inference runs through Apple's Private Cloud Compute architecture, and many of the workloads are hosted on Nvidia Blackwell GPUs in Google Cloud. Yet, using Nvidia hardware in the cloud and adopting the company's technologies for your own platforms is a completely different thing.

China's progress toward technological self-sufficiency in recent years is undeniable, but there is one thing that the country has so far failed to develop: lithography tools that are on par with those offered by ASML. That shortcoming has greatly hampered its domestic semiconductor industry. But Nvidia CEO Jensen Huang believes China will develop its own advanced lithography systems in just three or four years.

"They are going to get there by 2030," Huang said in an interview with The All-In Podcast (at 43:43). "2030 is just around the corner. The way to think about China is that it is really good at high-volume production. It is just a matter of time[...] so two or three years is just a click; it is nothing. So as far as they are concerned, they are already there."

Huang tends to look optimistically at China's technological development. Specifically, he is known for calling China's AI industry as being 'right behind' American frontier labs, which may well be correct, given how much capital China is investing in AI. But when it comes to the Chinese semiconductor industry in general and its lithography sector in particular, Huang may be too optimistic.

At present, China's leading producer of lithography tools — Shanghai Micro Electronics Equipment — can mass-produce a 193-nm ArF dry scanner that can be used to build chips on 90nm-class process technology. While the company has reportedly developed a 28nm-capable ArF immersion scanner, there is no public evidence that such systems are produced in volume and are used for high-volume chip production.

Although there are reports that Shanghai Aishengna Electronic Technology Group (a unit, or an affiliate of SMEE) has delivered its first immersion scanner that may be capable of printing chips using 28nm-class process technology, these tools will require extensive qualification before they can be used for high-volume manufacturing of semiconductors, so if deployment follows standard timelines, it will take years before this unit will be used for mass producing chips.

Even assuming that the first scanners are delivered to Chinese chipmakers in 2026, widespread production use before 2028–2029 appears unlikely. Furthermore, matching the capabilities of an early-generation ASML immersion scanner would still leave Chinese lithography suppliers considerably behind ASML's contemporary systems, which makes technological parity in ArF immersion lithography by 2030 highly unlikely.

China is considerably further behind in extreme ultraviolet (EUV) lithography. While there are reports that Chinese scientists have managed to develop a laser-produced plasma source that can generate the 13.5-nanometer wavelength light required for the technology, it does not look like China is close to assembling even a prototype EUV scanner itself.

Even if China can assemble an EUV experimental exposure platform without having mastered production-quality immersion DUV, it is hard to imagine a Chinese company producing EUV scanners without solving the hardest problems common to both DUV and EUV technologies. A production lithography scanner requires extraordinary capabilities in wafer and reticle stages, alignment, overlay, projection optics, and metrology, just to name a few.

If China is still struggling to industrialize these capabilities for immersion DUV, there is little reason to assume it has somehow solved them at the substantially more demanding EUV level. But given the potentially existential stakes of the AI race, and the determination of the Chinese government to achieve technological self-sufficiency, it may just indeed be a matter of time.

The GeForce RTX 5090 is the first thing that comes to mind when you think of the best graphics cards. However, you'd better pray the serial number on the inside of the metal bracket does not fade over time, since that could be a reason for Nvidia to reject warranty claims. At least one Redditor has shared a case of Nvidia reportedly denying a warranty claim because the bracket's serial number was unreadable.

The Redditor Willing-Avocado-4830, based in the UK, began experiencing black-screen crashes with a GeForce RTX 5090 Founders Edition graphics card under heavy load. The user sent the card to Nvidia for a warranty claim. After 30 days of radio silence, the owner finally received an email stating that their RMA request was rejected with no apparent explanation for the denial.

Seeking clarification, the user reached out to Nvidia for further information. According to the Redditor's recount, Nvidia support purportedly rejected the RMA because the serial number on the graphics card's metal bracket had faded. Like many gamers, Willing-Avocado-4830 claims never to have modified or tampered with the graphics card after installation. The fading serial number does not appear to be an isolated incident. Several other owners have reported similar issues in the Reddit discussion.

The metal bracket on graphics cards is susceptible to dust buildup, oxidation, and corrosion over time. This can cause it to look dirty, stained, or sometimes slightly discolored compared to when it was brand new. These same phenomena could have affected the serial number's legibility, especially since it is printed with ink onto the bracket's surface rather than etched for better durability. For comparison, the last-generation GeForce RTX 4090 Founders Edition had its serial number laser-etched onto the metal bracket.

One Redditor said that someone in the Nvidia Discord channel recommended cleaning the area with isopropyl alcohol to restore the serial number's visibility. However, that may not work if the ink is no longer present, as previous reports suggest the hot air expelled by the Founders Edition cooler could be erasing the serial number.

Surprisingly, Nvidia rejected a warranty claim solely because of a faded serial number on the graphics card’s bracket. The same serial number is clearly printed on a sticker on Nvidia's modular PCIe connector, which the chipmaker's technicians can verify when disassembling the GeForce RTX 5090. Moreover, you can pull the serial number from the graphics card through the command prompt with Nvidia SMI (System Management Interface), assuming the card is still working, which, in this case, it is. Willing-Avocado-4830 still had the original packaging and the original invoice from Nvidia U.K., so it would be easy to cross-reference the serial numbers.

Redditor hawok reportedly sent their GeForce RTX 5090 Founders Edition for RMA two months ago and encountered a similar situation. The serial number on the metal bracket was only partially visible. However, customer support seemingly accepted the RMA after the user sent photographs of the packaging that corroborated the serial number. It is unclear what Nvidia's policy is, or whether the chipmaker handles each case differently. We have reached out to Nvidia for clarification.

Some Redditors have suggested taking a photograph that shows the graphics card's serial number next to the packaging's serial number. Others jokingly recommend putting a piece of tape over the serial number. It might not be a bad idea, but you would need heat-resistant tape, something like Kapton tape.

GeForce RTX 5090 stock has practically disappeared from the U.S. market, with very few units starting at $6,500, 3.25X over the Blackwell flagship's MSRP. You cannot blame GeForce RTX 5090 owners for worrying. They already have a lot on their plates, dealing with the 16-pin power connector melting woes, and now also have to worry about fading serial numbers.

Perplexity has released Portable Computer for Windows, in partnership with Nvidia, via the existing Perplexity app for Windows. Previously, this functionality was only available on Linux-based operating systems. The hardware requirements remain, meaning the host system must have at least 24GB of VRAM with a GeForce RTX or RTX PRO GPU. Likewise, a Pro or Max Perplexity subscription is required. Portable Computer was originally launched on the DGX Spark as a fully local AI agent platform.

Portable Computer, launched originally for Linux on Aug. 25, is a local version of Perplexity Computer, which is the company’s agent for multistep tasks. Perplexity Computer can plan, run subtasks through connectors and tools, and produce a result other than a simple chat response. This runs in Perplexity’s cloud and consumes Computer credits. Portable Computer is the same agent but with features running on your local PC instead of in the cloud. Local work does not consume credits, but the agent can send tasks to cloud models with explicit permission if necessary, the company said. Nvidia said on Sept. 3 that Windows support was coming soon.

Portable Computer for Windows comes with some new features. These include scheduled recurring tasks and local MCP servers for desktop apps, according to Perplexity. Nvidia listed connectors for Microsoft Word, Google Drive, Gmail, Slack, and GitHub. The app also includes a dropdown for downloading a local model with one click. Nvidia named Qwen 3.8 27B as an example local model. DGX Station support is expected soon, Nvidia said.

Aravind Srinivas, CEO of Perplexity, wrote on X on Sept. 14 that with this release comes “unmetered local intelligence on every Windows PC running on Nvidia hardware and Perplexity harness.” The 24GB requirement is a VRAM gate more than a generation gate, cutting across Nvidia’s consumer lineup. Cards that meet the stated 24GB+ VRAM requirement include the RTX 3090 and 3090 Ti (24GB), the RTX 4090 (24GB), and the 5090 (32GB). The RTX 5090 Laptop GPU at 24GB has not explicitly been mentioned by either company. RTX PRO Blackwell cards that qualify are the 4000 (24GB), 4500 (32GB), 5000 (48GB or 72GB), and 6000 (96GB).

In a Sept. 3 post ahead of IFA, the consumer electronics trade show in Berlin, Nvidia indicated more plans along these lines. The post stated that RTX Spark Windows PCs from Lenovo and Acer are expected in October and that two local agents, Hermes Agent and OpenClaw, are getting the same simplified local setup. For users who already own a qualifying RTX PC, the Windows release removes the need to buy a separate system. Upgrading a compatible desktop with a used qualifying card could also cost less than buying the DGX Spark Founders Edition at its $4,699 price.

Nvidia's fastest gaming graphics card, the RTX 5090, has been on a tear of price increases over the past several weeks. However, over the past week, the available inventory has dwindled. Now, you can only find the RTX 5090 from third-party sellers at online retailers like Newegg and Amazon, commanding anywhere from $6,500 to $9,500 (or even higher) for Team Green's best GPU.

At Newegg, the cheapest RTX 5090 is the MSI Ventus 3X that's available from Slava Computers (a relatively new seller with 239 ratings and a 2.8 out of 5 rating at the time of writing) for $6,449. On Amazon, you can get the Asus TUF Gaming OC for $6,395 from Joes Tech Shop, a seller with an 81% positive rating. However, among the most recent reviews are a string of one-star reviews about orders never being fulfilled. The cost goes much higher, as well. The first result for "RTX 5090" on Newegg, for example, surfaces the MSI Ventus 3X OC for $8,699.

In June, the median price for an RTX 5090 was $4,299. At the beginning of September, we logged the lowest online price at $5,199 in our GPU price tracker. Now, in less than two weeks, the available stock has completely disappeared online, and the options available from third-party sellers have ballooned in price once again.

Naturally, we don't recommend buying from one of these third-party sellers. The RTX 5090 is selling at a vastly inflated price, but more importantly, we've seen no shortage of scams around high-ticket items like the RTX 5090. In January, 42 Amazon customers were duped into a scam involving a $999 RTX 5090, instead receiving a fanny pack in place of the GPU. Earlier this month, an online seller scammed two buyers with the RTX 5090, selling phantom graphics cards with the core and memory removed on the secondhand market for prices near MSRP.

The one exception to RTX 5090 inventory is Micro Center, which still has cards available around the average selling price. At a local Micro Center we checked, the cheapest in-stock option was $4,299. Micro Center has exclusively sold graphics cards in-person for several years, insulating it from online buyouts like what appears to be going on right now.

As usual, the reason why there's so much demand for the RTX 5090 is fairly obvious: AI. The RTX 5090 holds the GB202 GPU and 32GB of GDDR7 memory. For context, Nvidia's RTX Pro 5000 48GB comes with the same GPU with a third fewer shader units enabled, and lower memory bandwidth compared to the RTX 5090, and sells for between $7,000 and $9,000. In that context, the RTX 5090 starts to look attractive for building an AI server, even at $5,000 (or more) apiece.

At the same time, the RTX 5090 remains the fastest gaming graphics card on the market according to the testing in our GPU benchmark hierarchy. That's even more true now with Nvidia's launch of DLSS 5 Neural Rendering, which the RTX 5090 can fully capitalize on (though, thankfully, we've found playable performance on lower-end cards in our extensive DLSS 5 testing).

We've reached out to Nvidia for comment on whether it has any plans to stabilize inventory. We'll update this story when we hear back.

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

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

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

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

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

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

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

The latency question

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

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

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

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

Testing methods and notes

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

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

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

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

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

Modded Cyberpunk 2077 1080p performance

At 1080p, Cyberpunk 2077 with MFG 4X scales fine on Ada, but it’s clearly scaling better on Blackwell. The RTX 4090 lands between the RTX 5070 Ti and RTX 5080. Introducing DLSS 5 to the mix doesn’t change any relative standings, although it does create some larger gaps between average frame rates and 1% lows than we might like on the RTX 4070 Ti Super, RTX 4070 Ti, RTX 4070 Super, and RTX 4070. The RTX 5070 has no such trouble.

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

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

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

Modded Cyberpunk 2077 2560x1440 performance

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

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

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

Modded Cyberpunk 2077 4K performance

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

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

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

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

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

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

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

Bottom line

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

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

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

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

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

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

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

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

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

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

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

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

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

Want a powerful new OLED gaming laptop with a $1,000 discount on top? This HyperX Omen Max 16 gaming laptop from HP is down to $3,699.99 right now and features, among other top specs, an Nvidia GeForce RTX 5090 mobile GPU. You'll struggle to find a better spec'd laptop at this price point, and it's much cheaper than buying or building a top spec desktop gaming PC, too.

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The mobile version of the RTX 5090 isn't quite as beefy as its desktop counterpart, but it's still the best GPU for a laptop that you can buy. Specs wise, it has 10,496 CUDA cores as its disposal, with 24GB of GDDR7 VRAM, operating on a 256-bit memory bus. A peak 175W TDP is much less than the desktop's 575W rating, but laptops are designed to be portable.

What's interesting is the pricing here. This laptop costs $3,699.99, and that's for an entire working system. If you wanted to buy the desktop version of the RTX 5090 alone from a retailer that has it in stock, it'd set you back at least $5,899. You could argue that they're totally different beasts, but PC gaming is getting really expensive. Buying a new desktop PC will set cost you thousands of dollars more than it would have last year. A top-spec laptop this like HyperX Omen Max 16 is the better of the two options from a financial point of view right now, especially as it'll still be able to handle modern games with the best settings for years to come.

As for this laptop, our HyperX Omen Max 16 review made clear that its original launch pricing didn't quite work, with better performance from rival laptops. The $1,000 drop makes it a much more attractive system, with a gorgeous build quality that's "solid as a rock." It's built with aluminum, with no flashy styling gimmicks, just a matte black finish and RGB lighting for the keyboard. It's weighty, too, measuring 14.04 x 10.59 x 1.03 inches and weighing 6.56 pounds.

This HyperX laptop has a 16-inch OLED display, will give you perfect blacks and infinite contrast compared to a cheaper LCD, with an ultra-fast 240Hz refresh rate for smooth, blur-free motion. The top resolution on this laptop's display is 2,500 x 1,600, and coupled with the RTX 5090, you won't struggle to hit the highest frame rates with Ultra graphics presets applied at 1600p, with the option to hook up to an external display using the supplied HDMI port if you prefer.

Games, movies and more are going to look incredible on this OLED panel, as it'll produce rich colors with plenty of depth, with 500-nit rated brightness, along with a max 1,100 for HDR content, to keep things vivid on-screen. Our review found it performed extremely well against the competition, with 131.2 percent of the DCI-P3 color space and 185.2 percent of sRGB. It also beat the MSI Raider 16 Max HX for panel brightness in our tests, with 464.6 nits vs 455.6.

The RTX 5090 is the centerpiece of this laptop, but you're also the Intel Core Ultra 9 290HX Plus as your CPU. It ships with 24 cores, eight of which are performance cores, which are good for gaming, along with 16 efficient cores for multi-threaded work. You're getting 32GB of DDR5-6400 RAM included, so no need to worry about the RAMpocalypse with this rig, along with a 2TB SSD offering Gen 5 speeds.

Wi-Fi 7 and Bluetooth 5.4 support ensures the fastest wireless connectivity, and you've got plenty of I/O too, including 2.5Gb Ethernet, the already-mentioned HDMI, a headphone jack, and two Type-C ports.

The $3,699.99 sale price for this HyperX Omen Max 16 OLED gaming laptop makes it a much more appealing option for buyers. In a market where an equivalent high spec desktop PC can set you back over $6,000, this price for a high spec laptop with an RTX 5090 is a good one in the current market. Grab it while you can, because HP aren't likely to keep it this cheap for long.

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

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

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

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

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

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

A cracking deal at Walmart could set you up with a brand new gaming laptop, capable of hitting high frame rates at 1080p. This MSI Cyborg 15 gaming laptop is down to $1,049 right now, a saving of $250, and it makes it one of the best value machines you can buy for gaming right now, especially considering the specs.

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This laptop was $100 cheaper last month, but given the AI-fueled boom in PC hardware prices, that's a modest increase for a laptop that is still good value at this price. You're getting a 15.6-inch 1080p display with this MSI laptop, featuring a decent 144Hz refresh rate.

The Nvidia GeForce RTX 5060, meanwhile, is the best component in this rig. You're ditching integrated graphics for a current-gen Nvidia Blackwell GPU with 8GB VRAM and 3,328 CUDA cores. With Nvidia DLSS support enabling multi-frame generation for the most intensive games, you're going to easily hit high frame rates in games on this 1080p display.

The CPU, meanwhile, is an eight-core Intel Core 5 210H CPU, featuring four performance and four efficient cores apiece. You're also getting 16GB of DDR5-5600 RAM, which is a plus, as you're not settling for slower DDR4 speeds. A small compromise, given the price, is the 512GB SSD, which is still Gen 4, so it won't be particularly slow.

The MSI Cyborg 15 has a clear aesthetic for gamers, with a slate gray chassis, combined with a symmetrical pattern on the keyboard and transparent plastic cutouts. The keyboard has four RGB zones, which you can configure, with the chassis also including a fingerprint reader and webcam privacy shutter. There's also a decently large 55.2 Wh battery for those on-the-go gaming sessions.

As for connectivity, you're getting Wi-Fi 6E and Bluetooth 5.3 support for wireless networking, while a Gigabit Ethernet port is included if you want to max out your connection. You've got USB-A and USB-C ports, along with an HDMI 2.1 port to hook up your laptop to a separate monitor.

The $1,049 sale price for this MSI Cyborg 15 makes it one of the best value laptops on the market right now for 1080p gaming. You're not settling for outdated components, with the RTX 5060 setting you up for gaming for several years to come. If you want a budget-friendly rig before the holiday season, this is the deal you'll want to take advantage of.

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

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

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

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

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

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

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

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

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

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

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

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

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

You can see the original demo for TBOD below:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

In short: yes, absolutely.

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

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

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

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

Our testing methods

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

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

Our test system is built with the following components:

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

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

NBA 2K27 1080p performance

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

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

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

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

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

NBA 2K27 1440p performance

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

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

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

NBA 2K27 4K performance

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

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

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

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

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

Bottom line

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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