Inside vLLM: Anatomy of a High-Throughput LLM Inference System

vLLM: Anatomy of a High-Throughput LLM Inference System

Aleksa Gordić breaks down the vLLM V1 engine, starting from a single-GPU offline run and building up to multi-node online serving. The post walks through paged attention, continuous batching, KV-cache block management, and advanced features like chunked prefill, prefix caching, and speculative decoding. It's based on an August 2025 commit. The article doesn't include specific benchmark numbers, but it clearly maps out how the scheduler, model executor, and distributed components fit together.

Why it matters: A solid technical deep-dive into vLLM V1's internals, from single-GPU scheduling to multi-node serving. No concrete benchmark numbers are given, which keeps the score from going higher.