As inference splits into prefill and decode, Nvidia's Groq deal could enable a “Rubin SRAM” variant optimized for ultra-low latency agentic reasoning workloads
Nvidia is buying Groq for two reasons imo. 1) Inference is disaggregating into prefill and decode.
@gavinsbakerGavin Baker
Context & Ripple Effects
The story frames Nvidia and Groq around a changing inference workflow: prefill and token-by-token decode can be treated as distinct tasks rather than one uniform workload. The reported arrangement is complicated by Nvidia’s denial of an acquisition, while the corpus describes a non-exclusive technology license.
Nvidia gains rights to Groq’s inference technology through the reported non-exclusive licensing arrangement, potentially extending its inference offerings beyond general-purpose GPU configurations.
Groq can remain operational, including GroqCloud, while its technology becomes available to Nvidia; that separates the platform’s commercial future from an outright acquisition outcome.
Second-order effects
If Nvidia productizes a decode-focused, SRAM-heavy system, buyers running latency-sensitive agentic workloads gain a more specialized option and must weigh it against GPU-based inference stacks.
The move puts pressure on rival inference-chip and cloud providers to show where dedicated low-latency hardware outperforms broadly programmable accelerators, not merely where it is cheaper.
Third-order effects
Inference infrastructure may increasingly be designed and sold around workload stages—prefill versus decode—rather than as a single accelerator category, shifting value toward memory architecture, networking, and serving software.
Non-exclusive licensing could become a route for Nvidia to absorb specialized inference capabilities without fully consolidating their operators, though the commercial durability of that model remains uncertain.
The trend: AI inference is moving toward vertically integrated, workload-specific systems in which low-latency decode becomes a distinct strategic layer.
@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.
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…
@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
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
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
“[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]
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…
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...