Every published Top AI Stories item tagged with NVIDIA, newest first.
Salesforce announced Koa at Dreamforce, its first reasoning model, built by post-training Nvidia's open-weight Nemotron 3 Super with supervised fine-tuning and reinforcement learning on a synthetic dataset that simulates enterprise workflows across more than 14 industries. Salesforce holds the weights and runs inference inside its own trust boundary, and on its CRM benchmark it says Koa matches or beats leading models on CRM actions with three times fewer errors. Until now Agentforce routed multi-step reasoning to Claude or ChatGPT, and Salesforce's Jayesh Govindarajan told TechCrunch the blocker had been the lack of a state-of-the-art American base model with clear data provenance, adding "we have no idea what Qwen trains on." Koa is in pilots with Formula 1, UChicago Medicine and Xero, with general availability expected this winter in US regions.
Beijing answered the frontier labs' call to slow AI development on Monday. Foreign Ministry spokesperson Guo Jiakun said fear-mongering and malicious competition would only disrupt global AI governance, and the state-run Global Times called the proposal a Cold War playbook packed with provisions aimed at China. The two capitals now read the same essay in opposite ways. Speaking by phone to Nvidia's Jensen Huang onstage at the All-In Summit, President Trump called the slowdown push a hoax that plays into the hands of people who could include China. China's State Security Minister, Chen Yixin, separately urged faster construction of a national system for preventing AI security risks.
The British cloud provider is in talks to sell $1.5 billion in convertible notes and to raise a further $2 billion from Nvidia, Bloomberg reported, ahead of a listing it has said may come as soon as later this month. Nvidia already backed its Series B in March, a raise of $1.1 billion billed as Europe's largest. It signed a six-year compute deal with Anthropic worth $45 billion last month. One figure needs care: reports that Nscale told investors it holds around $103 billion in revenue describe a projection from signed customer leases rather than sales, according to The Information.
Nvidia agreed to acquire Hugging Face for $12.9 billion, its second-largest deal ever after the $20 billion Groq asset purchase in December. Hugging Face hosts 3 million models, half a million datasets and a million applications used by more than 18 million developers, on annualized revenue of roughly $150 million. Chief executive Clément Delangue told CNBC he approached Jensen Huang over the summer, having decided open-source AI needed more scale; the company had turned down an investment of $500 million from Nvidia last year rather than accept a dominant shareholder. Huang wrote that the platform stays open and that Nvidia compute will not be required.
Nvidia is putting $3.5 billion into the Taiwanese chip designer MediaTek and opening its NVLink Fusion interconnect to it, so MediaTek can design custom accelerators that slot into Nvidia-based racks. The move answers the clearest structural threat to Nvidia's business, which is cloud providers building their own silicon to escape its pricing: rather than lose those customers, Nvidia becomes the rack the alternatives plug into. MediaTek expects its custom data-center chip business to bring in roughly $2 billion of revenue across 2026.
The AI cloud provider Lambda has raised $1 billion in private, short-dated debt arranged by JP Morgan Chase to buy Nvidia accelerators that it will rent to Microsoft, Bloomberg reported. The short maturities signal that Lambda expects to deploy the hardware and start earning on it fast enough to repay quickly. It is the third such facility this year, after a secured credit line in May and a loan of $926 million closed this week for Nvidia GB300 systems, and it lands while the company is reportedly raising a pre-IPO round of about $3 billion. Banks and technology companies have raised more than $400 billion in AI-related debt globally in 2026 so far.
Three days after Hugging Face was reported to be fielding takeover interest with no bidder named, the bidder has a name. The Information said Nvidia agreed to buy the company for $12.9 billion; Business Insider, which broke the original story, said no agreement has been signed and the talks could still fall apart. Neither company has commented, which TechCrunch called notable given how quickly Nvidia normally responds. Hugging Face turned down $500 million from Nvidia last year, saying it did not want a dominant investor able to sway its decisions.
The same day, Anthropic agreed a six-year deal worth $45 billion with the British cloud provider Nscale, first reported by Bloomberg and unconfirmed by either company. The capacity comes from a West Virginia data center running Nvidia's Vera Rubin systems, and it does not begin until late 2027. It is the largest in a rapid run of leases that already includes $10 billion with Volta earlier this month and $5 billion with AMD in July, which means the commitments now run years ahead of the capacity actually existing.
Perplexity, working with Nvidia, brought its agentic Computer product to the desktop as Portable Computer, with local work consuming no credits. The engineering is the actual claim rather than the privacy pitch: the orchestrator running the loop is deterministic code instead of a second model, tool calls run in an operating-system sandbox, and the harness fails closed, disabling itself if that sandbox is unavailable. Escalating to a cloud model is permission-gated and shows the user what would leave the machine. It needs an Nvidia DGX Spark or a card with at least 24 gigabytes of video memory.
OpenAI presented Jalapeño at the Hot Chips conference and invited SemiAnalysis into its lab to run the InferenceX benchmark suite against it. The chip was designed with Broadcom from a blank sheet for language-model inference, went from initial hiring to tape-out in roughly 16 months, and uses HBM4 memory. SemiAnalysis measured more than 700 tokens per second per user on DeepSeek R1 at a concurrency of one, with no speculative decoding. Two caveats belong beside the headline, and SemiAnalysis states both: OpenAI supplied the numbers, and the fair comparison is Nvidia's shipping Rubin generation rather than Blackwell.
Prosecutors in Keelung charged nine people, one of whom is still at large, over Supermicro servers carrying Nvidia B300 GPUs that reached China through Indonesia, Japan and Hong Kong. Seventy-four got through and another fifty-six were seized in Taiwan before they moved. The accused include a distribution manager at Nvidia's Taiwan office, whom prosecutors call the key figure for authorizing release of the chips, two Supermicro sales managers and a distributor's chief executive. It is the island's first known criminal case over the black market in AI accelerators, and it locates the export-control failure inside the vendors rather than at the border.
Contract manufacturers that build AI servers for Microsoft, Google and Oracle have told customers to expect increases of more than 15 percent on many systems, taking effect on machines shipped early next year, Bloomberg reported. The rises cover the flagship Vera Rubin and Grace Blackwell generations and vary by chip generation and memory configuration. The cause is memory rather than silicon: Samsung, SK Hynix and Micron cannot keep pace with AI infrastructure demand, and Nvidia is passing the cost through instead of absorbing it. Nvidia did not respond to requests for comment.
Starcloud added $250 million to the Series A it announced in March, led by Manhattan West, with Nvidia and Cisco among more than a dozen backers. The extension doubles its valuation to $2.3 billion. Starcloud already has an Nvidia H100 in orbit aboard Starcloud One, which it says completed the first AI training run in space, and is building production lines for a 200-kilowatt Starcloud-3 meant to fly on Starship. Its chief executive tied the raise to a launch squeeze: SpaceX plans to end Falcon 9 in 2028, and the alternatives are not yet flying regularly.
Reuters reported that Nvidia has bought a minority position in Cloverleaf Infrastructure, a company founded in 2024 that sits between utilities and developers to secure grid power, cooling and sites. Terms were not disclosed, though the Wall Street Journal put the figure at several hundred million dollars. Cloverleaf will run Nvidia's DSX platform to choose sites and size power. It lands days after Nvidia committed $1.5 billion to SoftBank's SB Energy, and it marks where the chip company now believes the real constraint sits.
Nvidia is investing $1.5 billion in SB Energy, the SoftBank-backed developer of the PORTS-Pike campus in Pike County, Ohio, and is providing credit support covering the land, power and buildings so the site can open at just over four gigawatts and scale to eight. SB Energy will own and operate the data center under a 20-year lease to OpenAI, with Nvidia named the exclusive compute supplier. SB Energy and SoftBank say they will build at least 10 gigawatts of new generation and put at least $4.2 billion into regional grid infrastructure through a partnership with the utility AEP Ohio that is designed to protect ratepayers.
Groq closed a $350 million round, led by Disruptive with Nvidia participating, at a $3.5 billion valuation — down from $6.9 billion in September 2025, before Nvidia hired founder Jonathan Ross and much of his senior team in a $20 billion licensing deal. Groq calls the figure a reset rather than a down round, and the business being funded is a different one: it runs its own inference chips across 13 data centers, and was certified this month as an Nvidia cloud partner so it can add Nvidia hardware alongside them. Capacity is set to climb from 54 megawatts to more than 200 megawatts in 2027.
In this week's Stratechery, Ben Thompson argues that Nvidia's new financing partnerships with Apollo, BlackRock and Blackstone — structured to mobilize $500 billion, with Nvidia backstopping up to 25 percent of residual value — move AI's capital risk out of corporate cash flow and into safety-seeking pools such as insurance floats and pension funds. He notes hyperscalers had already raised $194 billion in debt by July and Google raised $85 billion in equity. His historical comparison is Jay Cooke's 1873 railroad collapse, and his conclusion is blunt: AI had better deliver first.
The Bureau of Industry and Security, the US Commerce Department's export-enforcement arm, is reviewing how Chinese AI companies reach Nvidia hardware by renting computing power housed in other countries — an arrangement that is not currently illegal under the export rules. Southeast Asia is the focus, with operators in Thailand, Malaysia and Singapore leasing chips to Chinese clients; the Singaporean firm Megaspeed is already under investigation. Nvidia, which has said the controls already cost it the world's second-largest market, would fight any limit on overseas data-center access.
Officials walked executives from Meta, Nvidia, Microsoft, OpenAI and Anthropic through the completed frontier-model framework, and its shape is now clear. Only closed, proprietary American models that hit top scores on cybersecurity benchmarks are asked to submit for government testing — voluntarily, up to thirty days before release. Open-weight models are exempt, and Bloomberg reports that officials told American firms the open-weight models coming out of China will not be tested either. The framework itself will not be published; Chris McGuire of the Council on Foreign Relations called that baffling, arguing the world cannot run on secret, voluntary rules for its most important technology.
Volta, founded in January by former Brookfield Asset Management executives, leases artificial intelligence capacity and helps customers finance the chips behind it. It will supply Anthropic under a six-year agreement worth $10 billion, starting with a 133 megawatt site in Norway built with the crypto-mining firm Bitdeer and running Nvidia's Vera Rubin systems. Volta raised $300 million recently, valued at $2.4 billion, so the contract is worth more than four times the company selling it — the same lease-the-compute structure that now sits underneath much of Anthropic's capacity.
The inference company Wafer published benchmarks on July 31 putting Moonshot's Kimi K3, at 2.8 trillion parameters, at 952 tokens per second per node on AMD's MI355X against 1,568 on Nvidia's B300. The AMD part is slower in raw throughput but roughly 2.4-times cheaper per GPU, which works out to 48 tokens per second per dollar versus 33. Its 288 gigabytes of memory per GPU also lets the model fit in a single node where the Nvidia configuration needs two.
Anthropic published a position paper stating plainly that it "has never advocated for a ban on open-weights models," calling open models without dangerous capabilities a public good. The post follows an open letter organized by Nvidia, signed by Meta, Microsoft and Mistral, that Anthropic pointedly declined to join. What Anthropic does want is narrower: chip export controls on China, a crackdown on state-backed distillation, and mandatory pre-release safety testing for every sufficiently capable model, open or closed.
Nvidia is putting billions into Safe Superintelligence, the lab Ilya Sutskever founded after leaving OpenAI, in a deal Bloomberg sizes at $5 billion. The investment buys access to Nvidia's Vera Rubin platform and lifts the lab's compute by about an order of magnitude. Sutskever framed it as a threshold moment: "We have research that is worthy of scaling up, and having access to a big NVIDIA computer will let us do so." Safe Superintelligence has now raised $7 billion at a $32 billion valuation.
The Wall Street Journal reports that Nvidia is in talks to guarantee $250 billion in financing so OpenAI can lease a 10-gigawatt campus that SoftBank's energy arm is developing in Piketon, Ohio, on the site of a former uranium enrichment plant. The full project could cost more than $500 billion, and Nvidia is separately discussing another $350 billion tied to chip purchases. Investor Michael Burry, who is short Nvidia, called the structure circular — a chip vendor guaranteeing its own customer's spending on its own chips.
Nvidia and South Korea's SK Group announced an expanded partnership valued at over $500 billion, covering multi-gigawatt AI factories and a long-term memory supply agreement. SK Telecom will build a 2-gigawatt facility in Korea running Nvidia's next-generation Vera Rubin systems on SK hynix HBM4 memory, with the first phase due online in 2027. The memory half matters most: high-bandwidth memory has become the tightest constraint on shipping AI hardware.
Nvidia chief executive Jensen Huang published a letter — his first-ever post on the social platform X — urging Washington not to restrict downloadable AI models, and within a day the signatory list doubled from 25 companies to 50, including OpenAI, Google, Microsoft, AMD, Cisco, and GitHub. The conspicuous absences were Anthropic, the most vocal proponent of tighter controls, and Amazon, Anthropic's largest investor. With Google — itself an Anthropic backer — signing anyway, the split leaves the safety-focused lab increasingly isolated on the year's central AI-policy fight.
AMD used its Advancing AI event to launch Helios, its first full rack-scale system aimed squarely at Nvidia's grip on AI data centers. Each rack packs 72 of AMD's new Instinct MI455X GPUs with sixth-generation EPYC processors and Pensando networking, delivering up to 2.9 exaflops of inference performance. Microsoft will run Helios on Azure and Anthropic plans to install up to two gigawatts of the chips, with OpenAI and Meta also signed on. Systems ship in the second half of the year.
The White House's technology chief, Michael Kratsios, publicly accused Chinese lab Moonshot AI of running large-scale distillation against Anthropic's Fable model to help build its Kimi K3 system — and of training on export-controlled Nvidia GB300 servers accessed through Thailand. Treasury Secretary Scott Bessent said sanctions and Entity List designations are "on the table." Some researchers are skeptical distillation alone could explain Kimi K3, noting Anthropic only released Fable publicly on July 1.
Capping a Tokyo visit by chief executive Jensen Huang, Nvidia and the Japanese government unveiled a national Physical AI Initiative to build open foundation models for robots, autonomous machines, and factory digital twins — a bet on Japan's manufacturing base and shrinking workforce. Partners span Toyota, which is building assisted-driving systems and factory simulations on Nvidia platforms, robot makers Fanuc, Yaskawa, and Kawasaki, plus SoftBank, Fujitsu, and Mitsubishi Electric. Japan is pairing the effort with up to $6.2 billion in planned spending over five years toward homegrown physical AI. It is the clearest sign yet that the robotics race is consolidating around a handful of national, chip-anchored ecosystems.
Reflection, the American open-weight AI lab founded by two former DeepMind researchers, agreed to spend $1 billion for access to Nvidia's latest GB300 chips through 2029, hosted by European infrastructure provider Nebius. The deal follows a similar compute pact with SpaceX weeks earlier, as Reflection, valued at $8 billion, races to train open alternatives to OpenAI and Anthropic. Backers of the startup include Nvidia, Sequoia, and Lightspeed.
An internal memo reviewed by Reuters shows Meta plans to begin manufacturing Iris, the fourth generation of its custom MTIA accelerator, in September — designed with Broadcom and built by TSMC. Iris will not replace the Nvidia and AMD GPUs Meta already buys in bulk, but it is meant to shave the cost of scaling to seven gigawatts of compute by year-end and 14 gigawatts in 2027. Meta expects to spend as much as $145 billion on AI infrastructure in 2026 alone. The chip cleared bug testing in roughly six weeks with no major problems.
Reuters reports that DeepSeek has spent about a year secretly recruiting chip engineers and courting manufacturing partners to build an in-house processor for AI inference — the stage where a trained model answers user queries. The goal is to cut its dependence on Nvidia and Huawei as US export controls keep tightening. It follows OpenAI's Broadcom-built Jalapeno inference chip and Anthropic's own reported chip ambitions — a sign that frontier labs increasingly want to own their silicon.
Micron posted fiscal third-quarter revenue of $41.5 billion, up 346 percent from a year ago, with profit jumping to $28.2 billion — and its shares spiked enough on June 25 to briefly vault its market value past Meta and Tesla, near $1.4 trillion. The surge reflects a broader shift: high-bandwidth memory has become the scarcest part of an AI server, and Micron is one of only three suppliers that can make it at scale. Wall Street analysts are now openly asking whether the memory maker is the next Nvidia.
SpaceX agreed to lease AI computing capacity to Reflection AI, an open-source lab founded by former Google DeepMind researchers, for $150 million a month from July through 2029 — up to $6.3 billion in total. Reflection gets Nvidia GB300 chips at SpaceX's Colossus 2 data center near Memphis, Tennessee, the facility xAI built before folding into SpaceX. It joins much larger SpaceX compute deals with Anthropic and Google, turning the company's spare chip inventory into a fast-growing leasing business.
AI chipmaker Groq confirmed a $650 million funding round, led by the investment firm Disruptive, as it rebuilds after Nvidia's roughly $20 billion deal late last year to license Groq's technology and hire away its founder and much of its top engineering team. Groq is now doubling down on its "neocloud" business — renting out fast inference capacity — and restocking its executive bench. The startup last carried a valuation near $6.9 billion and did not disclose a new one.
Qualcomm is reportedly negotiating to acquire Tenstorrent, the AI-chip startup led by legendary designer Jim Keller, in a deal valued between $8 billion and $10 billion. Tenstorrent builds RISC-V-based processors it claims beat general-purpose GPUs on specific AI workloads, so the purchase would hand Qualcomm a credible data-center play against Nvidia and a path beyond smartphone chips. The talks are unconfirmed and could still fall apart, but they show how aggressively mobile-chip makers are now chasing AI silicon.
Amazon is in talks to sell its in-house Trainium chips directly to outside data centers for the first time, breaking from years of reserving them for AWS cloud customers. AI chief Peter DeSantis confirmed the discussions in Paris but named no buyers, pointing to rising demand — especially in Europe — to keep AI compute under local control. CEO Andy Jassy has called the homegrown chip business an opportunity worth 50 billion dollars a year; pulling it off would put Amazon in direct hardware competition with Nvidia for the first time.
Nvidia sold $25 billion of investment-grade notes across seven tranches maturing as far out as 2056 — its first corporate bond deal since a $5 billion raise in 2021. Demand reached more than $85 billion, over three times the offering, and the company upsized the sale from an initial $20 billion. For a firm sitting on a roughly $5 trillion market value and ample cash, tapping the debt market signals just how much it plans to spend building out AI data-center capacity.
Loft Orbital says its YAM-9 satellite became the first spacecraft to run a vision-language model in orbit, using Google DeepMind's Gemma 3 to answer plain-English queries — classifying where wilderness meets development, or spotting infrastructure near rail hubs — without beaming raw imagery to ground analysts first. The model ran on an Nvidia Jetson edge chip paired with NAVI-Orbital software from NASA's Jet Propulsion Laboratory. Onboard triage like this could sharply cut how much data satellites need to downlink, and Loft says 50 to 100 such craft would give near-real-time eyes on Earth.
Germany's Neura Robotics raised $1.4 billion in a Series C round backed by Amazon, NVIDIA, Qualcomm, Bosch, and the European Investment Bank, valuing the cognitive-robotics company at about $7 billion. It is the biggest single round ever raised by a full-stack robotics maker, and a sign that strategic players are betting hard on humanoids for warehouse and factory work. Neura already holds more than $1 billion in pre-orders for its two-armed, two-legged 4NE-1 robot, whose first units are due to ship late this year.
Nvidia told Chinese cloud customers that its new Vera data-center CPU is now open for orders and could ship as early as August, according to Reuters. The pitch is a workaround: after US export curbs choked Nvidia's GPU sales in China, the company is leaning on standalone CPUs — which face looser restrictions — to defend the market. Nvidia's finance chief has said Vera CPU revenue could approach $20 billion in the coming fiscal year.
Jensen Huang and Hyundai chair Chung Euisun used a meeting in Seoul to widen their partnership from research prototypes toward factory-ready robotics, spanning mobility, manufacturing, and autonomous driving. Hyundai will stand up an AI supercomputer built on 50,000 Nvidia Blackwell GPUs to train models for self-driving and smart factories, and the two companies will work with the Korean government on new AI technology centers. Huang called Hyundai one of the best robotics companies in the world. The deal is one of the largest national commitments yet to physical AI — the effort to move machine intelligence off the screen and into machines that move.
Bloomberg reports that Beijing is preparing to spend roughly 2 trillion yuan, about $295 billion, over five years on a network of interconnected AI computing hubs run mostly by state firms like China Mobile and China Telecom. The blueprint, drafted by agencies including the National Development and Reform Commission, would source at least 80 percent of the hardware, including AI chips, from domestic suppliers such as Huawei — effectively designing Nvidia and AMD out of the country's largest buildout. It is the clearest signal yet that China intends to win the AI compute race on homegrown silicon.
Google will pay SpaceX about $920 million a month from October 2026 through June 2029 — roughly $30 billion in total — for access to around 110,000 Nvidia chips, capacity SpaceX first built for its own xAI division. Google Cloud called it short-term "bridge capacity" for its Gemini Enterprise platform while its own data centers scale up. The deal lands days before SpaceX's IPO, where Google already holds about a 5 percent stake, and echoes an earlier SpaceX compute arrangement with Anthropic worth roughly $1.25 billion a month.
In this week's Stratechery analysis, Ben Thompson argues that AI agents shift the center of gravity from the device to the cloud — making Nvidia's GPU-heavy RTX Spark AI PC a poor fit, since agents want strong local CPUs that call out to cloud inference, while praising Microsoft's Project Solara, which treats the cloud as the hub and phones and PCs as interchangeable spokes. He frames Microsoft's in-house MAI models as a way for cautious enterprises to own custom agents without handing their workflows to frontier labs.
At Computex, Intel detailed Crescent Island, a data-center GPU built on its Arc Xe3P architecture and aimed at AI inference. Partner cards can carry up to 480 gigabytes of LPDDR5X memory — more than Nvidia's and AMD's flagships — while skipping the scarce, expensive high-bandwidth memory those chips rely on, which Intel says makes the card cheaper to produce and run on a 350-watt air cooler. Intel is targeting a second-half 2026 launch as it tries to chip away at Nvidia's data-center lead.
NVIDIA used its Computex keynote to enter the personal-computer market, unveiling the N1X — an Arm-based chip co-designed with MediaTek that pairs a 20-core processor with a Blackwell graphics processor and 6,144 CUDA cores. It will debut in Windows laptops from Microsoft, Dell, HP, ASUS, Lenovo, and MSI before the 2026 holidays, with performance models priced above $2,000 to challenge Apple's MacBook Pro and longtime chip leaders Intel and AMD. A lower-power N1 variant will start under $1,500.
Separately at the same Computex keynote, NVIDIA launched Nemotron 3 Ultra, the largest model in the open-weights family it first previewed in December. The hybrid Mamba-Transformer mixture-of-experts (MoE) model carries roughly 550 billion total parameters with about 50 billion active per token, and NVIDIA says it tops US open-weights rankings while running about 30 percent cheaper than leading alternatives. The weights and training recipes are free to download.
Groq is raising 650 million dollars to refocus the company on its inference neocloud — the on-demand cloud platform powered by Groq's own AI chips — after a December 2025 arrangement saw Nvidia pay 20 billion dollars for senior Groq engineering talent and a hardware license. Existing investors are leading the round, with Disruptive and Infinitium committed to fill any unsubscribed shares. Adam Winter is now interim CEO and Matt Eng interim CFO. The company argues inference is a much bigger market than training right now, and the wedge for the smaller team that remains.
At the groundbreaking for a new Taipei headquarters, Jensen Huang announced that Nvidia will spend $150 billion a year in Taiwan, up from $10 to $15 billion annually four to five years ago. The headquarters will employ 4,000 people and target a 2030 opening, anchoring Nvidia closer to TSMC, which fabricates its chips, and Foxconn, which assembles them into server racks. The pledge lands as Washington has spent two years pushing chipmakers to build US capacity, and it cements Taiwan rather than the United States as the structural center of advanced AI manufacturing for at least the rest of the decade.
In this week's Stratechery analysis, Ben Thompson argues that SpaceX's rumored IPO at a $2 trillion valuation only makes sense if Starship enables data centers in orbit. His core thesis: terrestrial data center expansion is now constrained more by community zoning opposition than by power generation, and the existing Starlink V2 Mini satellite form factor — about 7.4 meters by 2.7 meters — is comparable to NVIDIA's NVL72 rack. Combined with Starlink's laser interconnects, the constellation already has the network topology required for distributed orbital compute; power dissipation and radiation hardening become engineering problems rather than fundamental obstacles to agentic-inference workloads.
In this week's Stratechery analysis, Ben Thompson argues that Nvidia's revised reporting taxonomy — splitting hyperscaler revenue from everyone else — reveals the contours of an emerging commoditization fight at the top of the stack. Nvidia is "fighting commoditization" with its largest customers, where hyperscalers increasingly design their own silicon, while it "runs the whole stack" for AI clouds, sovereigns, and enterprises. The reframe lands one week after Nvidia's record $81.6 billion quarter on May 20, when hyperscaler revenue held at roughly half of the $75.2 billion data-center total.
At a press event in Taipei this week, NVIDIA CEO Jensen Huang told reporters the company has *"largely conceded"* China's AI accelerator market to Huawei, with NVIDIA's share now near zero after the US H200 export-clearance stalemate dragged into a second month. Huawei expects roughly $12 billion in 2026 revenue from its Ascend line — up from $7.5 billion in 2025 — on orders already placed by Alibaba, ByteDance, and Tencent, all of which deployed DeepSeek V4 services within hours of the model's Ascend-optimized release in April. Huang said he still expects Beijing to eventually allow H200 imports, but for now the homegrown stack is shipping while NVIDIA's clearance letters sit in customs.
NVIDIA Labs has quietly posted Nemotron Diffusion to Hugging Face — a 14-billion-parameter language model that switches between autoregressive decoding, parallel diffusion decoding, and a *"self-speculation"* mode that drafts with diffusion and verifies with autoregression, all without changing model weights. The accompanying technical report claims a 2.2-times throughput lift over the comparable Qwen 3 8-billion-parameter baseline at matched accuracy, scaling to 850 tokens per second on a GB200 (a 3.3-times lift). Base, instruct, and vision-language variants are all open-weight; the architecture is positioned as a path from memory-bound to compute-bound inference as GPUs keep outrunning memory bandwidth.
Nvidia reported $81.6 billion in revenue for the quarter ending April 26, a 20% sequential jump, with data-center revenue at a record $75.2 billion. The earnings disclosure also surfaced $43 billion in non-marketable startup equity — nearly double the prior quarter — including a previously-undisclosed []0 billion commitment to OpenAI. On the call, Jensen Huang positioned the new Vera CPU as a "brand new toolIds: 00 billion total addressable market" built for autonomous-acting agents.
NVIDIA Labs released SANA-WM, an open-source 2.6 billion parameter world model under Apache 2.0 that generates one-minute videos at 720p resolution with 6 degrees of camera-pose control. The team reports training on roughly 213,000 video clips in 15 days on 64 H100 GPUs, and says a distilled variant runs on a single consumer GPU. The paper positions SANA-WM as a baseline for embodied-AI and robotics research at a fraction of closed-model compute budgets.
In a TechCrunch interview, Menlo Ventures partner Deedy Das argues that roughly 10,000 founders and employees at OpenAI, Anthropic, NVIDIA, Meta, and xAI have crossed 20 million dollars in personal wealth over the past five years — while engineers earning under 500,000 dollars a year increasingly fear they cannot get there from here. Das calls the split in San Francisco "the worst I've ever seen" and ties the gap to a broader "deep malaise about work" pervading even well-paid technical roles in the AI era.
In this week's Stratechery analysis, Ben Thompson argues that AI compute is bifurcating into three distinct workload categories that need fundamentally different hardware. Training keeps high-bandwidth GPUs (NVIDIA's lock-in); answer inference rewards token speed (Cerebras's WSE-3 packs 44 gigabytes of on-chip SRAM at 21 petabytes per second of bandwidth versus NVIDIA H100's 80 gigabytes of HBM at 3.35 terabytes per second); agentic inference, where humans aren't in the loop, mostly cares about memory capacity and cost-per-token at scale. Thompson treats Cerebras's revised IPO pricing of $150 to $160 per share (up from $115 to $125) and Anthropic's lease of 220,000 NVIDIA GPUs at SpaceX's Colossus 1 (the 300-megawatt Memphis data center) as concrete evidence that buyers are now sorting their compute spend by workload category rather than picking a single vendor.
Nvidia has now committed more than $40 billion to equity investments in AI companies in 2026, including a single $30 billion stake in OpenAI, up to $3.2 billion in glassmaker Corning, and up to $2.1 billion in data-center operator IREN. Wedbush analyst Matthew Bryson described the pattern as "squarely circular" — money cycling between chip vendor, model customer, and infrastructure provider. The chipmaker has also closed roughly two dozen private startup rounds plus 67 venture deals across 2025, intensifying scrutiny of how concentrated the AI capital stack has become around a single supplier.
NVIDIA Research published Nemotron Elastic, a post-training method that embeds 30, 23, and
12 billion-parameter nested reasoning models inside a single 30 billion-parameter parent —
extractable via zero-shot slicing without further fine-tuning. The recipe achieves a 360-times
token reduction over pretraining from scratch, and the 30 billion checkpoint compresses to 18.7
gigabytes under NVFP4 quantization. The 23-to-30 billion configuration advances the
accuracy-and-latency Pareto frontier with up to 16 percent higher accuracy and 1.9-times lower
latency than the default Nemotron Nano v3 budget control. All three precision variants (BF16,
FP8, NVFP4) are available on Hugging Face under nvidia/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B.
Cerebras Systems filed to sell 28 million shares priced between $115 and $125 per share, targeting $3.5 billion in proceeds at a $26.6 billion valuation — the largest US tech IPO of 2026 so far. OpenAI is one of the chipmaker's largest customers under a multi-year contract worth more than $10 billion signed in January, and holds a $1 billion secured loan plus warrants for over 33 million shares, potentially making OpenAI a major shareholder post-listing. The offering puts Cerebras' Wafer-Scale Engine 3 against Nvidia in a public-market test of GPU pricing power and frontier-lab compute lock-in.
The Department of Defense announced contracts with Nvidia, Microsoft, AWS, and Reflection AI to deploy AI on Impact Level 6 and Impact Level 7 classified networks — the most sensitive systems short of compartmented intelligence. The deals follow earlier agreements with Google, SpaceX, and OpenAI, and are framed as a vendor-diversification push following a public dispute with Anthropic over usage restrictions. Contract values were not disclosed.
Mistral released Mistral Medium 3.5, a 128-billion-parameter dense model with a 256,000-token context window, available open-weights under a modified MIT license at $1.50 / $7.50 per million input/output tokens. The release pairs with Vibe remote agents — async cloud coding agents launched from CLI or Le Chat that handle refactors and test generation in parallel. Reported benchmarks: 77.6% on SWE-Bench Verified, 91.4 on τ³-Telecom, and self-hosting on as few as four GPUs.
Swedish legal-AI startup Legora closed a $50 million Series D extension led by NVentures (NVIDIA) and Atlassian at a $5.6 billion post-money valuation, claiming over $100 million ARR and 1,000+ law firms across 50 markets. Harvey still leads at an $11 billion valuation with 100,000 lawyers and 1,300 organizations as customers. Both companies are launching celebrity ad campaigns — Harvey with Gabriel Macht, Legora with Jude Law — and expanding into each other's geographies.