Elasticity

Time vLLM vs SGLang from process start to first token on the fleet, break it down by stage, then attack the biggest bar (weight loading, compile time, snapshotting techniques).

Why CPU/GPU-util and QPS are the wrong signals and in-flight concurrency is the right one; queueing intuition via Little's Law; when scale-to-zero pays.

The LLM Inference Handbook's getting-started part: cost crossovers, procurement economics (hyperscaler vs neocloud), and mapping model sizes to GPUs. Standard architecture-interview material.

Prefix-aware / KV-utilization-aware / PD-aware routing; app-level gateways (LiteLLM-class) with exact + semantic caching; and why offline batch inference is the cheapest tokens you'll ever serve.