Understanding LLM Inference Engines: Inside Nano-vLLM

Nano-vLLM is a minimal 1,200-line Python implementation that demonstrates how large language model inference engines work, using a producer-consumer architecture where prompts are tokenized into sequences, batched by a scheduler for efficient GPU processing, and managed through innovative block-based memory allocation with prefix caching. The system balances throughput versus latency through batching decisions while handling two distinct phases: prefill (processing input prompts) and decode (generating output tokens one at a time).