How the hell is Groq raising more money?

Somehow, Palpatine returned.

Axios just dropped a bizarre scoop. Groq, the AI chip company company that was acquired by Nvidia in December of last year, is raising $650M. How, exactly, is a company that successfully exited raising more capital?

Well, technically, Nvidia did not acquire Groq. They licensed Groq’s technology and hired Groq’s key technical executives, but did not acquire the Groq corporate entity. That corporate entity continued to operate, focusing on maintaining Groq’s datacenters and their inference API. That API focuses on offering extremely fast inference on smaller models; their largest supported model is GPT OSS 120B, which is likely at least 10x smaller than frontier models like GPT-5.5 or Claude Mythos. This is a technical limitation of Groq’s architecture; without large amounts of high-bandwidth memory (HBM) in each chip package, the total cost to build and maintain a Groq cluster capable of serving a frontier model would be massive. However, their all-SRAM strategy lets them serve small models much faster than a conventional HBM-based chip, delivering more tokens-per-second at the cost of lower tokens-per-dollar. For certain applications, this makes sense.

More importantly, Groq has four large datacenter deployments that are already set up to serve inference workloads at scale. Building out new datacenters has been a major challenge for startups and hyperscalars alike, so Groq’s datacenters represent a major strategic asset. However, it remains to be seen if the Groq name gives them a legitimate leg up, and or if their brand association with the LPU chip and a specific high-speed, high-cost inference strategy limits them.

Datacenters matter more than ever

As inference demand surges, existing datacenters are quickly reaching full utilization and companies are trying to build new ones as fast as possible. But between regulation, power issues, and expertise, even large companies with experience building out datacenters are experiencing major delays in their datacenter construction projects. And for venture investors, who primarily invest in startups, getting direct exposure to datacenter demand is very difficult; because datacenters are so hard to build, very few startups are actually doing so.

Groq already has four functional datacenters, as well as the talent to build and operate more. When Nvidia licensed Groq’s technology, Groq’s chip design team, compiler team, and software team joined Nvidia, but Groq’s datacenter team stayed to maintain GroqCloud inference services. This makes the remaining Groq company a unique asset -- a private inference datacenter operator with clear operational expertise, plus what is likely an extremely low valuation due to most of their differentiating technology being acquired by Nvidia.

Let’s look at the numbers for some publicly traded AI datacenter companies to compare. CoreWeave is a 50 billion dollar company operating 43 AI datacenters. Nebius is worth about 50 billion dollars too, with only 11 datacenters, though they are larger than CoreWeave’s. One could argue that Groq’s datacenters alone could make them worth billions of dollars.

But what about the LPU?

Obviously, there are a couple issues with comparing Groq to CoreWeave and Nebius directly. Groq’s datacenters are all full of LPUv1 chips, which are seven years old at this point. And the new LPUv3 chips based on Groq’s architecture are being sold by Nvidia to any cloud provider who wants them. That means that Groq’s biggest technical advantage, of very fast tokens, is no longer unique to them.

Plus, the value of Groq’s brand adds additional complexity to the calculation. On one hand, Groq was a massively successful outcome. On the other, getting “acquired” and then still operating is confusing. And their brand is strongly associated with ultra-high-speed inference, which may not end up dominating inference workloads compared to lower-speed batched inference. This is particularly worrying as major organizations like Microsoft and Uber are raising the alarm about the high cost of AI coding tools.

Maybe Nvidia is giving Groq a sweetheart deal on buying hardware based on Groq’s technology, which would make Groq a significantly more appealing investment. Ultimately, it remains to be seen if Groq can successfully outfit their existing datacenters with new hardware to remain competitive with other cloud providers. I’m also curious to see whether they stay committed to high-speed, high-cost tokenomics, and if that strategy ends up being successful or not in the long term.