14 posts tagged with "inference"

Most engineers building on LLMs treat latency as a single dial. They tune something — a batch size, a quantization level, an instance type — observe whether "it got faster," and call it done. This works until you hit production and discover that your p50 TTFT looks fine while your p99 is over 3 seconds, or that the optimization that doubled your throughput somehow made individual users feel the system got slower.

TTFT and throughput are not two ends of the same slider. They are caused by fundamentally different physics, degraded by different bottlenecks, and fixed by different techniques. Treating them as interchangeable is the root cause of most LLM inference incidents I've seen in production.

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.