Junhao Li’s write‑up masterfully dissects vLLM V1’s architecture—from HTTP request to GPU matrix ops—revealing how smart batching and paged KV cache transform LLM serving. This isn’t just academic elegance; it’s a hardcore systems‑level play, marrying OS-style memory paging with GPU scheduling for win‑win efficiency. Contrasting with monolithic inference loops, vLLM’s modular flow (API → Async LLM → EngineCore → GPU → back) sidesteps Python’s Global Interpreter Lock (GIL) hits and scales predictably. For engineering teams building inference platforms, the punchline is clear: combine continuous batching, cache paging, and IPC-driven parallelism to crush latency and cost simultaneously.

Life of an Inference Request (vLLM V1): How LLMs Are Served Efficiently at Scale