Groq Raises $350 Million at $3.5 Billion Valuation as Nvidia Joins Funding Round

Groq raises $350M to fuel its pivot from AI chips to neocloud

Startup Groq has raised $350 million as it continues to pivot from an AI chipmaker to a neocloud company that provides powerful GPUs and AI infrastructure services. The new capital, led by investment firm Disruptive with planned participation from Nvidia, values the company at $3.5 billion. That's down from the $6.9 billion Groq was valued at last September, just a few months before Nvidia hired the startup's founder and CEO, Jonathan Ross, and other top talent as part of a licensing deal. A spokesperson for the company told TechCrunch that despite the difference in valuation, the company doesn't see it as a down round, but rather as establishing a new valuation for the "post-Nvidia-lincensing-deal version of Groq." Groq was focused on building its own chips, dubbed LPUs (language processing units), to compete with Nvidia on inference -- the type of compute needed to run AI workloads in real time. After it lost its star team, Groq shifted from being a pure AI chipmaker into a cloud and data center provider that operates Nvidia systems, making the remaining Groq company an Nvidia customer. In June, Groq raised a $650 million round to kick off its pivot. The company intends to scale from 54 megawatts to more than 200 megawatts by 2027. Today, Groq operates 13 data centers across North America, Europe, the Middle East, and Asia Pacific, serving more than 6 million developers, enterprises, and AI-native companies. Groq says the fresh funds will support "those seeking usage of medium and larger sized clusters of Nvidia accelerated computing for training and inference." "We are building Groq into the world's leading AI inference cloud," Alex Davis, Groq's chairman and CEO of Disruptive, said in a statement. "Inference will without a doubt become the largest and most critical layer of AI infrastructure." While inference is in high demand as enterprises scale AI workloads, it's an open question whether neoclouds will be a profitable enough business to provide returns on their considerable investment in the long term. CoreWeave reported strong second-quarter revenue growth and recently landed major contracts, including with Meta and Anthropic. However, investors remained concerned about the company's high capital expenditures, heavy reliance on debt, and exposure to rapidly depreciating hardware, and its ability to turn growth into free cash flow. Groq's financials are still private for now, but its pivot puts the company directly inside Nvidia's AI infrastructure ecosystem. That's not exactly a unique relationship among neoclouds today. Nvidia supplies the GPUs powering clouds from CoreWeave, Lambda, and Nebius, while also investing billions into some of those companies as they race to build more capacity. TechCrunch has reached out to Groq for more information.

Groq closes $350M Series A at $3.5bn evaluation and Nvidia joins the round

Groq, the AI-inference chip company that spent the better part of a decade pitching itself as the scrappy alternative to Nvidia, has raised fresh money at a valuation that quietly concedes how much has changed. The company announced on Monday that they took in $350m at a $3.5bn valuation, roughly half the $6.9bn it commanded last September. The round was led by Disruptive, the Dallas firm whose founder Alex Davis is now Groq's executive chairman, and, in a twist that would be difficult to invent, Nvidia itself joined in. Only months earlier Nvidia had licensed Groq's technology and hired away much of its talent, an episode we covered when the company first set about picking up the pieces. Late last year Nvidia struck a non-exclusive licensing agreement for Groq's language-processing-unit technology, a deal widely reported at around $20bn and just as widely described as a "not-acqui-hire". There was no outright purchase of the company. Instead Nvidia secured the intellectual-property rights it wanted and walked off with founder and chief executive Jonathan Ross, a former Google engineer who had helped build that company's tensor chips, together with a good part of the senior team. What remained needed a new plan and new people. Co-founder Doug Wightman stepped up as chief executive, a fresh bench of executives was recruited, and Groq repositioned itself less as a chip designer squaring up to Nvidia and more as a data-centre operator selling AI inference by the token. The stated ambition now is a "neocloud" that pushes capacity beyond 200 megawatts within a year. The inference business it leans on, a cloud that already serves millions of developers and processes trillions of tokens a week, came with the pivot rather than the departed chip team. A $650m raise in June was the opening act of that reconstruction, and this $350m round is the second. The optics of the company that emptied the building now helping to refurbish it are peculiar even by the standards of the AI-chip boom, where allegiances are fluid and almost everyone is somehow both a customer and a competitor at once. It is the same tangle visible across the sector, where Nvidia's grip has grown so complete that would-be challengers increasingly find it easier to partner than to fight. Backing Groq costs Nvidia very little, and it buys a friendly, dependent supplier of inference capacity, plus a stake in whatever the rebuilt firm turns out to be. Still, the numbers deserve a raised eyebrow. Halving a company's paper worth inside a year is a brisk correction in a market where valuations have mostly travelled in one direction, and where newer entrants keep minting billion-dollar price tags on the strength of inference demand alone. Rival inference-chip startups such as Fractile have raised at buoyant valuations, and one London challenger recently tripled its worth to $3.3bn while betting openly against Nvidia. Groq's discount is a useful reminder that the gold rush prices in founders and engineers as much as silicon, and that losing both carries a bill. Whether $350m is enough to matter is the open question. Groq still runs a genuine inference business, with data centres across several continents and developers who use it precisely because it is fast and cheap, and demand for inference is not in doubt. What is less certain is whether a rebuilt Groq, shorn of the founder who defined it and now part-funded by the giant that hollowed it out, can be much more than a comfortable supplier orbiting Nvidia. The valuation suggests investors have already made their peace with the smaller ambition. At $3.5bn, they are paying for a going concern, not a giant-killer.

Groq Raises $350 Million to Fund AI Inference Goals | PYMNTS.com

The Series A round will help Groq support customers "seeking usage of medium and larger sized clusters of Nvidia accelerated computing for training and inference," the company said in a news release Monday (Aug. 17). The round was led by tech investment firm Disruptive, with planned participation from Nvidia, which signed a $20 billion licensing deal with Groq last year. "We look forward to continuing our partnership with Nvidia at such an important juncture for the ecosystem," Alex Davis, Groq executive chairman and CEO of Disruptive, said in the release. "Inference will without a doubt become the largest and most critical layer of AI infrastructure," Davis added. "Our team has unmatched experience operating LPUs at scale and delivering the performance, efficiency and reliability that the next generation of AI demands." According to the release, Groq operates 13 data centers around the world, serving more than six million developers, Fortune 500 enterprises and thousands of "AI-native companies." The company raised $650 million in June. Nvidia last year acquired tech from Groq and hired several members of the company's team, though Groq said it would continue to operate as an independent business. As covered here last year, inference refers to a stage in which a trained AI model processes new data and generates results. "When a customer service chatbot answers a query or an AI system analyzes a financial document, that is inference at work," that report said. "While training creates the model by processing vast datasets to learn patterns, inference applies that learned knowledge to perform specific tasks at scale." As companies deploy AI systems that handle thousands or millions of requests daily, inference becomes the key operational challenge and cost driver. Another PYMNTS report last fall examined inference and why, for most enterprises, it now matters more than training. Training a large language model happens just once or only occasionally. Inference takes place each time a user interacts with an AI system. A single model might manage millions of inference requests every month, each needing computational resources, adding both latency and costs. "For companies running artificial intelligence in customer-facing applications, inference performance directly affects user experience, system reliability and operational expenses," PYMNTS wrote.