Groq: LPU Inference at Speed-of-Thought
Groq sells one thing: speed. Its LPU chips keep model weights in fast on-chip memory instead of slow GPU video memory, so open models (Llama, Qwen, DeepSeek distills, Gemma) generate at 300-1000+ tokens/sec through an OpenAI-compatible GroqCloud API. When a human is waiting on the answer, latency becomes the product.
01.The Problem: The User Is Watching a Spinner
Try a mental experiment about a voice assistant.
A human asks it something. If the reply starts after 1.5 seconds, the conversation already feels broken — people expect roughly half-second turn-taking, the way humans do.
Now build the answer token by token:
codetypical GPU serving: ~60 tokens/sec a 40-token answer: 40 / 60 = 0.67 s just to TYPE it out plus queue + prompt --> easily 1.5-2 s before the user hears anything
The model is done thinking almost instantly — it is typing slowly. Every word after the first costs another slice of time, and the user hears nothing until enough words exist to speak.
So the question becomes:
Why is the typing slow — and can we build a chip that types at the speed of thought?
Groq's entire business is that answer. Not a smarter model. Not a friendlier API. The models Groq serves are everyone else's weights. The product is time-to-first-token (TTFT) and tokens-per-second as a feature you can put in a product demo.
Why LPUs beat GPUs on tokens/sec for LLM decode
Why LPUs beat GPUs on tokens/sec for LLM decode
LLM decoding is memory-bandwidth-bound; the LPU removes DRAM/HBM round-trips by streaming weights from deterministic on-chip SRAM, yielding an order-of-magnitude faster token streams.
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