Nvidia $NVDA ( ▲ 1.85% ) struck a licensing agreement with AI chip startup Groq, aiming to boost the efficiency of its inference stack as power and compute constraints tighten across the AI ecosystem.
While some early chatter framed the move as a $20 billion acquisition, Groq’s own announcement makes clear this is a non-exclusive licensing deal, not a buyout. Groq will continue operating independently, with no financial terms disclosed. That distinction likely helps Nvidia avoid immediate antitrust scrutiny.
An acqui-hire in everything but name
Even without an official acquisition, this deal has all the hallmarks of an acqui-hire. Groq founder Jonathan Ross, president Sunny Madra, and other key team members are joining Nvidia to help scale the licensed technology.
Ross is no stranger to this space. He previously worked at Google $GOOGL ( ▼ 0.18% ) , where he helped design the company’s first TPU. That experience now feeds directly into Nvidia’s effort to defend its dominance as alternative AI architectures gain traction.
Why inference efficiency suddenly matters more
Inference is the “thinking” phase of AI models, and it is becoming the real bottleneck as deployment scales. Groq’s LPUs, or language processing units, are built specifically for fast, low-latency inference and are designed to be significantly more energy efficient than traditional GPUs.
Unlike most accelerators, Groq’s chips do not rely on external high-bandwidth memory, which remains in tight supply. Instead, they use on-chip SRAM, helping sidestep one of the biggest hardware constraints facing AI data centers.
For Nvidia, the move looks strategic rather than defensive. Integrating Groq’s low-latency inference tech into its AI factory architecture could make Nvidia’s platforms cheaper to run, easier to scale, and more competitive against TPU-powered systems like Google’s Gemini.
And for Groq’s early backers, including Social Capital’s Chamath Palihapitiya, this deal quietly marks one of the rare AI hardware bets that is already paying off.