What is Groq?

CPUs with deterministic latency are technically feasible but have been abandoned because the market does not reward that design trade-off.

“You can actually design a CPU that has deterministic latency as well. In fact, the processors inside a lot of AI chips also have deterministic latency. Groq has advertised this. TPUs have that in the core as well. The challenge is getting deterministic latency and high speed at the same time. Non-deterministic latency comes from specific design choices in a CPU. It's actually possible to remove those design choices and make a CPU with deterministic latency, but those are not very attractive in the market, so people don't make those CPUs anymore.”

Running a frontier model of a few trillion parameters on Groq LPUs would require thousands of chips just to hold model weights, due to insufficient on-chip SRAM, constraining Groq to smaller models.

“If you think about it to run a frontier level model of like let's say a few trillion parameters you need thousands and thousands of Grock LPUs just to hold the weights.”

Groq, acquired by Nvidia, is positioned as specialized pre-fill compute hardware central to 2026 inference disaggregation trends.

“One thing 2026 could be is the year of disaggregation with, for example, Nvidia buying Groq and having more specialized pre-fill compute versus decode compute.”

Software-based diffusion LLM inference on commodity GPUs is more scalable than specialized AI inference chips from Cerebras and Groq, according to Stefano Ermon.

“Our solution is software based so it's much more scalable we're still running on GPUs. So you can you know get as much capacity as you as you know as you can get GPUs for which is relatively easier compared to specialized AI inference chips.”