KV Cache in LLMs: The Optimization That Makes Modern AI Models Feel Fast

This is a written explainer on KV caching — the optimization that lets LLMs reuse attention computation across tokens rather than recomputing it on every generation step. It is not a tool, strictly speaking, but the audience for this digest includes builders who are making real decisions about which LLM APIs to use, how to structure prompts for latency, and why context window size affects response time in non-obvious ways. Understanding KV cache behavior informs those decisions concretely: it explains why prefill latency scales with prompt length, why cached system prompts are cheaper on some providers, and why streaming responses feel fast even when total generation time is long. The article is written accessibly without being shallow. Reservation: it is an introductory piece, so anyone already building at the infrastructure layer will find it too basic. The author is building a related tool, which provides context but also makes the piece a light promotional vehicle. -> Best for: technical PM or solo founder who is integrating LLM APIs and wants a mental model for the latency behavior they are debugging.