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.
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...
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.
[1] https://github.com/GeeeekExplorer/nano-vllm