LLM Latency in Production: What Actually Moves the Needle
· 10 min read
Most LLM latency advice falls into one of two failure modes: it focuses on the wrong metric, or it recommends optimizations that are too hardware-specific to apply unless you're running your own inference cluster. If you're building on top of a hosted API or a managed inference provider, a lot of that advice is noise.
This post focuses on what actually moves the needle — techniques that apply whether you control the stack or not, grounded in production data rather than benchmark lab conditions.
