Don't do the same work twice: how one KV cache idea unfolds into full inference systems engineering
Abhishekcur · x · 2026-09-25
A walkthrough of why the simplest idea in inference — don't recompute what you already know — is also one of the most important. Since LLMs generate one token at a time, inference engines cache previously computed attention keys and values in the KV cache instead of recalculating them. Following that idea's consequences leads straight into GPU memory, bandwidth, cache layout, allocation, batching, sequence lengths, latency and throughput — in other words, systems engineering. The author's point: inference is about understanding the whole path from model → computation → memory → hardware → latency → users, and performance engineering is finding work that doesn't need to happen and not paying for it.
Related event: From KV Cache to Systems Engineering: A Guide to LLM Inference Optimization(2 posts)→
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