The same LLM can behave like two very different workloads within a single request.
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The same LLM can behave like two very different workloads within a single request. During prefill, the model processes the prompt tokens together. There is substantial parallel work, large matrix operations, good opportunities for weight reuse, and the KV cache is built for the entire prompt. As batch size and prompt length grow, prefill can expose enough arithmetic intensity to move closer to the compute-bound region of the hardware. This phase shows up most visibly in TTFT, time to first token. Then the workload changes. During decode, every active sequence advances one token at a time. Ea
Posted by Sandhya (1.3k followers) 1 days ago · 7 likes · 305 views · view the original post on X. Kept by the Dev Radar as Hosting & infra.
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