vLLM: Anatomy of a High-Throughput LLM Inference System
Inside vLLM: Anatomy of a High-Throughput LLM Inference System (2025)
This post breaks down the core components of vLLM, a high-throughput LLM inference system. It covers the LLM engine, scheduling, paged attention, continuous batching, advanced features like chunked prefill and prefix caching, scaling to multi-GPU, and the serving layer. The author uses a running example and explains the V1 engine, aiming to give readers an accurate mental model of the system.
The KV-cache manager maintains a free_block_queue - a pool of available KV-cache blocks (often on the order of hundreds of thousands, depending on VRAM size and block size).
- miki123211
Another great way to understand how vllm works is to read the code of nano-vllm[1]. It's basically "vllm but cut down to size. It's ~5kloc, supports just one model, disposes of some of the abstraction layers that vllm needs due to its codebase size, but contains all the major pieces that make an inference engine fast.
- BinRoo
Love that this goes beyond paged attention. Curious how this compares with Radix Attention [1]?
[1] https://sgl-project-sglang-93.mintlify.app/concepts/radix-at...
- gdiamos
vLLM is originally marketed as paged attention, but in hindsight, separating the web server and GPU process, continuous batching, kv caching / chunking, and a huge model library including low precision mattered more.
I wonder how much it would cost to vibe code the whole thing from scatch?
I wonder how much better models need to get before such a thing wouldn't look like code vomit?