nano-vllm

Nano-vLLM is a small, readable reimplementation of a popular AI inference tool called vLLM. Inference here means running an AI language model to generate text, which is computationally intensive. The goal is to provide something similar to the full vLLM system but in about 1,200 lines of Python code that someone can actually read and understand.

vLLM is a widely used open-source tool for hosting language models efficiently, but its codebase is large and complex. Nano-vLLM was written from scratch to demonstrate how the core ideas work in a much smaller package, while still achieving comparable performance. According to the benchmark in the README, on one particular GPU it was slightly faster than the original vLLM for the same workload: 1,434 tokens per second vs 1,361 on an RTX 4070 laptop GPU.

The features it includes are: prefix caching (reusing computation from shared prompt beginnings), tensor parallelism (splitting model work across multiple GPUs), Torch compilation (a way to speed up PyTorch computations), and CUDA graph capture (reducing GPU overhead by pre-recording GPU operations). These are standard acceleration techniques used in production inference systems.

Using it follows the same pattern as vLLM: you load a model from a local path, define sampling parameters like temperature and maximum output length, pass in a list of prompts, and get text outputs back. The README shows an example using a small Qwen language model. The API intentionally mirrors vLLM with only minor differences in the generate method.

Installation is a single pip command pulling directly from GitHub. Model weights are downloaded separately via the Hugging Face command-line tool before running.