Inside vLLM: why serving shape matters
Paged KV-cache and continuous batching are the real product — not a list of model names.
A long technical walkthrough of vLLM is a reminder that inference systems are systems problems: scheduling, memory layout, and batching dominate “which checkpoint.”
If you run agents in production, the serving stack’s trade-offs (prefix caching, speculative decoding, multi-GPU) show up as latency tails and cost curves long before prompt wording does. Worth reading for the mental model, not the feature checklist.