LLM inference engines like vLLM manage the complex pipeline from prompt to generated text. Nano-vLLM, a minimal 1,200-line implementation, demonstrates core production concepts: a producer-consumer architecture where prompts become sequences queued by a Scheduler, batching that trades latency for throughput, two-phase
Table of contents
Architecture, Scheduling, and the Path from Prompt to TokenThe Main Flow: From Prompt to OutputInside the SchedulerThe Block Manager: KV Cache Control PlaneThe Model Runner: Execution and ParallelismWhat’s NextSort: