Inference engineering explained: why prefill and decode need different optimizations
mikeflache · x · 2026-10-08
A systematic explainer argues inference engineering is an underrated skill: serving real traffic brings queueing, batching, KV-cache pressure, scheduling and tail-latency problems that training-focused engineers overlook. Prefill is compute-bound and parallelizable; decode becomes memory-bandwidth-bound. This split explains FlashAttention (less memory traffic), PagedAttention (blocked KV-cache management), MQA/GQA (fewer KV heads), continuous batching, chunked prefill, and prefix caching, plus how KV-cache size scales with active tokens, layers, KV heads, head dim and bytes per element.
Related event: Inference engineering deep dive: Prefill/Decode and KV cache optimization(2 posts)→
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