The Groq deal secures key talent for Nvidia, including CEO Jonathan Ross, creator of the TPU, and keeps them from companies that may try to make their own chips
Twas the night before Christmas and all through the house, not a creature was stirring, not even a... wait. What's that?
SpyglassM.G. Siegler
Context & Ripple Effects
Nvidia’s arrangement with Groq was previously characterized as a non-exclusive technology license under which GroqCloud would continue operating while Ross and other executives moved to Nvidia. That licensing-and-executive transition makes this more than a conventional acquisition narrative: Nvidia gains people and inference know-how without fully absorbing the company.
The strategic value is concentrated in Ross’s chip-design experience and in denying that expertise to would-be custom-chip builders. It also leaves Groq as an independent operating presence, creating a split between control of key talent and continuity for its existing platform.
First-order effects
Nvidia adds Jonathan Ross and other Groq leaders, strengthening its internal capability around inference-oriented chip design and software architecture.
Groq loses senior operating talent while retaining its independent business and cloud service under the stated licensing structure.
Second-order effects
Companies considering in-house AI silicon lose a potential source of experienced leadership, raising the value of the remaining specialized hardware talent pool.
Nvidia can evaluate Groq’s inference technology inside its own product planning while Groq’s continued operation preserves a separate route to market for that technology.
Third-order effects
If similar arrangements proliferate, AI-chip competition may be shaped as much by targeted talent-and-IP deals as by outright acquisitions, allowing incumbents to neutralize emerging capabilities without consolidating every operating asset.
The pattern favors firms able to pair proprietary compute with software and elite design teams; independent chip startups may increasingly need to separate their commercial platforms from the mobility of their founders and technical leaders.
The trend:AI infrastructure leaders are using selective licensing and talent moves to deepen their integrated stacks while constraining the pool of teams capable of building alternative accelerators.
Anyone who thinks that the Nvidia-Groq deal was about solving CoWoS, energy, or HBM constraints is plainly wrong and doesn't understand the current paradigm of inference Groq deal creates another edge for Nvidia (it's not a magical game changer) Feynman and beyond may have [image…
Why @nvidia acquihiring @Groq for $20B is so interesting: It looks like a direct (and fast!) reaction by Nvidia to Google successfully using its own TPU chips for training AI models and inference (generating AI content) Because until recently, they used mostly Nvidia's GPUs...the…
Nvidia paid 3X Groq's September valuation to acquire it. This is strategically nuclear. Every AI lab was GPU dependent, creating massive concentration risk. Google broke free with TPUs for internal use, proving the “Nvidia or nothing” narrative was false. This didn't just [image]
So many bad takes on Groq as if its LPU is some magical new architecture or a TPU for hire. Groq's micro architecture does not matter. The *only* reason Groq has any traction is because it bet on SRAM. Without SRAM, there's no speed advantage, no PMF, no demand, and no
Maybe Groq's chips are legit, maybe they not, or maybe they're not *yet*, but even $20B is a relatively - for NVIDIA - small price to pay to effectively lock this team and tech up. Some last-minute Christmas shopping for Jensen Huang...
@GavinSBaker Yes..workload/segment specific infra. Groq could be to nvidia what instagram was to Facebook at that time. Diff workload segments in one case different demographic segments in the other.
@chamath Thanks Chamath - interesting thoughts. Time will tell as ever. Should have also said that Nvidia is getting an extremely talented team led by the brilliant @JonathanRoss321 who spent a long time in the wilderness and chewed a lot of metaphorical glass to get Groq to this
Groq CEO Jonathan Ross explains the importance of speed in delivering a service and relevance for AI. For Google, a 100 millisecond speed-up leads to 8% higher conversion rate. In consumer products, there's correlation with time to dopamine and higher margin (eg. cigs > soda). [v…
Very insightful post by Gavin below on Nvidia's $20B Groq licensing deal. AI inference has 2 steps, Prefill & Decode. Prefill means the model reads your whole prompt and context. Decode means it writes the reply one small chunk of text at a time. These 2 steps like different [ima…
This is directionally right. The HBM vs SRAM tradeoff in architecture design was clear many years ago. Those that picked HBM are in a queue behind Nvidia and Google. Good luck with that. More broadly, LLM decode patterns favor SRAM. But unlike Gavin, I think this creates a
Mostly aligned with Gavin on this. Whenever I was asked, “how does NVIDIA compete with ASICs” my response has always been for the past year: 1/ the AI pipeline will split into three distinct workloads 2/ CPX fills one, maybe two of the three workloads 3/ NVIDIA will have to fill
For the sake of clarity and as some have pointed in the replies, I should note that Nvidia is not actually acquiring Grok. It is a non-exclusive licensing agreement with some Grok engineers joining Nvidia. Grok will continue to operate their cloud business as an independent
“[U]ltra-low latency agentic reasoning workloads” — These guys claim to be analysts doing analyst things, but at core they're just arranging words together like those magnetic mad libs for refrigerators. [embedded post]