AI Engineer · October 6, 2026

Inference latency: separate queueing, prefill and token generation

video AI Engineering Model Evaluation

Inference latency: separate queueing, prefill and token generation video thumbnail

Why it matters

Charles Frye explains an inference engine as a request server, a scheduler and a model runner. Queueing and prompt prefill contribute to time to first token; repeated decoding governs later token delivery. KV caching, prefix reuse, batching, CUDA graphs and speculative decoding address different parts of that workload and introduce their own resource tradeoffs. A museum application example illustrates how a demand spike can worsen both initial and subsequent token latency. The talk provides a diagnostic model, not a universal speedup claim.

My takeaway: Measure queue depth, prompt and output lengths, cache occupancy, time to first token and inter-token latency under representative concurrency. Identify the bottleneck before changing replicas, batching or speculative decoding, and compare the full latency distribution after each change.

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