CapitalUnited States

SiMa.ai raises $150m at $1.45bn valuation to fund 1,000-TOPS physical AI chip

Credited to Rackline · Thomas Brennan · sima.ai7m ago

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SiMa.ai, a San Jose chip designer that builds artificial-intelligence processors for robots, factory equipment and vehicles, has raised $150m in a Series C round that values the company at $1.45bn. Its business is what the industry calls physical AI: computing that runs inside machines rather than in data centers.

The round, announced on Sept. 28, was co-led by Fidelity Management & Research Company and Amplify Partners and takes SiMa.ai's total funding to $500m. Alter Venture Partners, Dell Technologies Capital, Maverick Capital, +ND Capital, Point72 and StepStone Group also took part. AllianceBernstein, Baron Capital, J.P. Morgan and the State of Michigan invested in the company for the first time.

It is the first time SiMa.ai has disclosed a valuation. The company said the money would pay for a next generation of hardware, due in the first half of 2028, that is designed to deliver 1,000 dense TOPS, or trillions of operations per second. That is five times the most powerful version of its current chip. SiMa.ai plans to sell the new design as licensable machine-learning IP, as chiplets and as complete systems-on-chip, aimed at drones, humanoid robots, driver-assistance systems and AI-equipped car cockpits.

"Physical AI in humanoids, automotive, and drones is the gateway to a $50 trillion market that has remained largely untouched by modern innovation," said Krishna Rangasayee, SiMa.ai's founder and chief executive.

Rangasayee started the company in November 2018 to make machine-learning chips for devices that run on tight power budgets, beginning with computer vision. Fidelity first backed it in May 2021, leading an $80m Series B to bring SiMa.ai's first-generation chip into production. Maverick then led a $70m raise in April 2024 and an $85m round in August 2025, in which StepStone joined. SiMa.ai said at the time that the 2025 money would go partly toward its automotive plans.

The company's current product is Modalix, a second-generation chip made on TSMC's N6 process. SiMa.ai unveiled it in September 2024 in versions ranging from 25 to 200 TOPS and said in August 2025 that it was in production and shipping. It runs image-recognition models as well as large language and other generative AI models. SiMa.ai also sells a 50-TOPS module, built with the Swiss firm Enclustra, that is pin-compatible with modules from the leading GPU supplier, so customers can fit it into existing hardware designs. The chips come with SiMa.ai's Palette software, which the company says cuts the time needed to get an AI model running on a device from months to days or hours.

SiMa.ai does not report revenue figures. It said only that it "quadrupled year-over-year revenue growth" between 2024 and 2025 and is "tracking strong upward revenue momentum for 2026."

The Sept. 28 announcement named a dozen customers and partners, including Bosch, Emerson, Micron, Synopsys and TRUMPF, without saying which of them buy its chips in volume. Two of the relationships have been described in more detail. In July 2024 TRUMPF, the German laser maker, agreed to develop AI chips and software with SiMa.ai for its welding, cutting, marking and metal 3D-printing machines. The companies said the technology could check weld quality in real time at more than 3,000 images a second, replacing separate inspection steps in electric-vehicle and battery production. In April this year Micron made a strategic investment of undisclosed size, and SiMa.ai's modules now ship with Micron's LPDDR5X memory.

The investor list for the new round leans heavily on large public-equity managers. Fidelity, AllianceBernstein, Baron and J.P. Morgan all run big stock portfolios. Scott Darling, president of Dell Technologies Capital, said AI moving into the physical world had been "challenged by the tradeoff between performance and power." John Avirett, a partner at StepStone, said that scaling physical AI "demands incredible power efficiency."

The new valuation rests largely on a chip that will not arrive for more than a year. SiMa.ai has industrial partners such as TRUMPF and Bosch, but although the company directed part of its 2025 funding at cars, it has not yet named a carmaker or humanoid-robot maker as a production customer. Those design wins are what investors will be looking for next.

Source · sima.ai

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Chips & Supply · United States

Nvidia GPUs are everywhere. Here are the ways companies are accessing them

Credited to CNBC · cnbc.com · 3h ago

Marc Benioff, chief executive officer of Salesforce Inc., left, and Jensen Huang, chief executive officer of Nvidia Corp., during the 2026 Dreamforce conference in San Francisco, California, US, on Tuesday, Sept. 15, 2026.

David Paul Morris | Bloomberg | Getty Images

Nvidia GPUs are the most sought-after processors in AI, and they're in such demand that the chipmaker's stock climbed to yet another record this week, lifting its market cap close to $6 trillion.

Customers can now shop around for access to the chips at the giant clouds from Amazon, Microsoft and Google, as well as at so-called neoclouds like CoreWeave. They can also go to various online marketplaces or even buy the costly hardware directly.

For Nvidia, it all adds up to unrelenting growth, as management anticipates $108 billion in revenue for the October quarter, which would mark an 89% year-over-year jump.

But the paradox of choice can be a headache for companies needing computing power yesterday.

While cloud infrastructure providers have ranked at the top of Nvidia's customer list for several years, the business is diversifying. Five clients accounted for at least 10% of Nvidia's accounts receivable in July quarter, up from three in January, according to a filing.

Industry research firm SemiAnalysis counted 323 Nvidia GPU providers as of September, up from 209 less than 11 months earlier.

"You're going to see a whole new crop of really, really exciting neoclouds with hundreds of billions of dollars backlog together," Nvidia CEO Jensen Huang said at a Goldman Sachs tech conference in San Francisco last month.

Here's a rundown of the various options for accessing GPUs, and why each might make sense:

Hyperscalers

Many big companies spend tens of millions of dollars per year on a smorgasbord of cloud services from Amazon, Google and Microsoft. Since the 2022 launch of ChatGPT, companies have increasingly turned to the hyperscalers for GPUs so they can run generative AI workloads.

The top cloud providers come with a reputation advantage. If a software company relies on Amazon and Microsoft for GPUs and other capabilities, it won't need to panic about prospective customers questioning its suppliers.

Microsoft Azure signage on the exhibition floor during the Nvidia GTC conference in San Jose, California, on March 18, 2026.

"When you're talking to enterprises, your subprocessor had better be Azure," said Bindu Reddy, CEO of AI assistant startup Abacus, referring to Microsoft's cloud infrastructure.

In the past year, leading AI labs Anthropic and OpenAI have committed to spending over $500 billion between Amazon and Microsoft, which controlled 59% of the cloud infrastructure market in 2025, according to industry researcher Gartner.

"Hyperscalers are in a good position to show trust to the enterprises because of their 10-plus years of full-stack capabilities," said Gartner analyst Hardeep Singh. But hyperscalers don't always have as many GPUs as enterprises require, he said.

Andy Jassy, Amazon's CEO, told analysts in July that the retailer and cloud pioneer won't be able to serve all the demand it foresees this year.

"I believe this dynamic will also be true in 2027," he said.

Flagship neoclouds

If the hyperscalers were adequate, neoclouds wouldn't be proliferating.

Modal, a startup operating virtual sandboxes where AI agents work independent of main IT environments, went from running on the hyperscalers to signing up with the major neoclouds, and now it uses 25 of them, said CEO Erik Bernhardsson.

"You can get a few hundred GPUs or maybe a thousand, but at our scale, we needed way more GPUs," he said.

The hyperscalers themselves are also chasing the neoclouds. Google and Microsoft have started tapping CoreWeave, even as they all compete with one another.

"Some of the hyperscalers have approached us about taking care of customers they're worried about because they don't have the ability to service those customers when they need it," said Marc Boroditsky, chief revenue officer of Nebius, a Netherlands-based neocloud with operations in the U.S.

Video generation startup Reactor uses GPUs through Nebius and hyperscalers, said CEO Alberto Taiuti. The location of data centers matters, he said, because Reactor wants user-created videos to show up right away. Nebius delivers the specific GPUs Reactor needs, solid customer service and sufficient hardware and software, at a good price, Taiuti said.

Michael Intrator, co-founder and CEO of CoreWeave, speaks during an interview with CNBC on the floor at the New York Stock Exchange (NYSE) in New York City, U.S., Feb. 27, 2026.

The most visible neoclouds can require some upfront payment, and chips might not come online for months, because providers raise funding based on contracts and set up the data center equipment, Bernhardsson said.

It would be difficult for CoreWeave to turn over 10,000 GPUs to a new customer with one day's notice, said Chen Goldberg, an executive vice president. CoreWeave's near-term capacity remains essentially sold out, CEO Mike Intrator said on the company's August earnings call.

Baby neoclouds

If companies want GPUs immediately, they might have to go beyond the name brands. Some neoclouds aren't household names because they target specific countries, which can be viable in certain cases.

"Capacity right now is tight, and your relationships with your suppliers is actually one of the most closely guarded secrets for companies like ours," said Zhen Lu, CEO of Runpod.

Specialist neoclouds can offer more flexibility than the larger GPU clouds, which often require upfront payments and long-term commitments. Some sell so-called bare-metal GPUs, which give customers more control but also require them to manage more of the technical work themselves.

Companies using these smaller neoclouds share the same concerns: When can they get the GPUs, and at what price? Sunny Smith, co-founder and technology chief at Massed Compute, said customers are often willing to commit to capacity when they expect prices to rise.

Bring your own

Oracle, one of the world's largest cloud providers, is letting clients bring in their own GPUs. The software maker has more debt than Amazon or Microsoft, and its credit rating is lower so it has less flexibility to go on a GPU spending spree. But it's happy to operate the technology.

"As we're generally able to preserve and improve margins in the case of things like bring-your-own-hardware, the ROIC for those types of structures will be even higher," Oracle CFO Hilary Maxson told analysts on a June earnings call, using the acronym for return on invested capital.

Oracle hasn't disclosed the names of companies that choose this path. John DiFucci, a Guggenheim Securities analyst who recommends buying Oracle shares, said it would make sense for Advanced Micro Devices and Nvidia, the top two producers of GPUs, to bring their own to Oracle.

Super Micro Computer CEO Charles Liang speaks at the Computex conference in Taipei, Taiwan, on June 5, 2024.

Annabelle Chih | Bloomberg | Getty Images

Instead of buying thousands of GPUs, early-stage startups with limited capital can borrow them for hours at a time for less through clouds. And for companies with heavy-duty computing needs, Oracle's new route might be more attractive than building whole data centers. OpenAI committed to spending over $300 billion with Oracle over five years, but it hasn't mentioned anything about bringing in GPUs.

The method might make sense for companies that have the capital to purchase AI chips but don't have enough power, data center space or skilled labor. Like its hyperscaler peers, Oracle works hard to secure all three in healthy quantities.

Tactical deals

Another emerging option is to cut large deals with firms that have truckloads of GPUs for rent.

SpaceX arranged to turn over excess capacity in separate deals with hyperscaler Google and open-source startup Reflection.

SpaceX builds a data center in Southaven, Mississippi, on Aug. 2, 2026.

In April, SpaceX agreed to provide Cursor with GPUs and then bought the AI coding startup outright for $60 billion. And in May, SpaceX landed a deal to rent GPUs to Anthropic for $1.25 billion each month through mid-2029. That's more than what most startups can afford.

For SpaceX, though, the numbers work out nicely.

"The current economics have translated into a less than one-year payback on our new capital deployments for compute," Bret Johnsen, the company's finance chief, told analysts in August.

It's not only SpaceX. In July CNBC reported that Meta was working to form a cloud unit that could sell AI computing power.

Going old school

Meanwhile, companies continue to install GPU-filled servers in on-premises data centers the old-school way as CEOs work to balance capability with cost control.

Revenue nearly doubled in the enterprise and small and medium business parts of hardware maker Lenovo's Infrastructure Solutions Group during the June quarter. "We're seeing more and more enterprises now starting to say, 'How do I bring AI into my four walls?'" said Vlad Rozanovich, a senior vice president.

People walk through the hallways at Equinix Data Center in Ashburn, Virginia, on May 9, 2024.

Amanda Andrade-Rhoades | The Washington Post | Getty Images

The hourly spot price for an Nvidia B200 GPU has more than doubled since March, according to data from Ornn, a startup that maintains indexes.

Collaboration software maker Dropbox relies on GPUs in its data centers, CEO Ashraf Alkarmi said.

"If we want to do a lot more, I think our supply chain connections will still be beneficial and a structural advantage," he said.

Everpure, which sells data center storage hardware and software, has acquired its own GPUs, to run open-weight AI models for the company's software engineers, said CEO Charlie Giancarlo.

"In a very dynamic pricing environment, it's always good to have multiple sources that you can go to," he said.

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