NVIDIA has agreed to pay about $20 billion to acquire assets from artificial intelligence chip startup Groq. This is the company’s largest deal ever and continues its strategy of absorbing potential competitors before they challenge its market dominance.
The chipmaker’s latest licensing deal mirrors a similar deal just three months ago, reinforcing the theory that decentralized AI infrastructure may be the only alternative to Nvidia’s growing dominance.
3x Premium in 3 Months with Trump Jr. Connection
The deal comes just three months after Groq raised $750 million at a $6.9 billion valuation. The round included BlackRock, Samsung, Cisco, and 1789 Capital, where Donald Trump Jr. is a partner. Nvidia will acquire essentially all of the company’s assets except for its cloud computing business, but Groq is calling the deal a “non-exclusive licensing agreement.”
Groq CEO Jonathan Ross, a former Google engineer who helped create the search giant’s Tensor Processing Unit, will join Nvidia along with president Sunny Madra and other senior executives. The startup will continue to operate independently with CFO Simon Edwards as its new chief executive officer.
repeat playbook
Groq’s deal follows a pattern established by Nvidia just three months ago. In September, the company paid more than $900 million to hire Enfabrica’s CEO and employees, while also licensing the startup’s technology. Both deals use license structures rather than outright acquisitions, potentially avoiding antitrust scrutiny that blocked Nvidia’s $40 billion acquisition of Arm Holdings in 2022.
Koveisi’s letter frankly summarizes NVIDIA’s approach: “We will buy you before you can compete with us.”
Nvidia’s latest strategy:
“Before you compete with us, we will buy you.”
There’s never been a company like Nvidia. https://t.co/wsbuAgIqyM
— Kobeissi Letter (@KobeissiLetter) December 24, 2025
Technological advantage and competitive pressure
Groq’s language processing unit uses on-chip SRAM rather than external DRAM, which the company says is up to 10 times more energy efficient. While this architecture excels at real-time inference, the limited model size allows Nvidia to consider the trade-offs within the broader ecosystem.
The timing is notable. Google recently announced its 7th generation TPU, codenamed Ironwood, and released the fully TPU-trained Gemini 3 to the top of its benchmark rankings. Nvidia responded with the following in X: “We are pleased with Google’s success…NVIDIA is a generation ahead of the industry and is the only platform on which you can run any AI model.” When incumbents start making such reassuring statements, competitive pressures are clearly building.
Impact on decentralized AI
Although the transaction has no direct impact on the cryptocurrency market, it strengthens the narrative driving decentralized AI computing projects. Platforms like io.net are establishing themselves as an alternative to centralized AI infrastructure.
“People can put their own power on the network, whether it’s in a data center or themselves on a laptop, contribute their own available GPU power and be fairly compensated for it using tokenomics,” Jack Collier, chief growth officer at io.net, told BeInCrypto. The platform claims enterprise customers such as Leonardo.ai and UC Berkeley have achieved significant cost savings.
However, the gap between narrative and reality remains wide. Nvidia’s acquisition of Groq’s low-latency technology further extended its technological lead, making it difficult for substitutes to offer competitive performance.
The deal also raises questions about independent AI chip development. Cerebras Systems, another NVIDIA competitor preparing for an IPO, could eventually face similar pressure. It remains to be seen whether it will be able to maintain its independence or succumb to Nvidia’s financial gravity.
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