Detailed Introduction
Nano-vLLM is a lightweight implementation of vLLM developed from scratch. It aims to deliver high-performance offline inference comparable to vLLM while keeping the codebase readable and easy to customize. The project is implemented in concise Python, making it suitable for researchers and engineers to quickly iterate and integrate.
Main Features
- High-performance offline inference comparable to vLLM on single-GPU setups.
- A compact and readable codebase (~1,200 lines of Python) for easy customization and learning.
- An optimization suite including prefix caching, tensor parallelism, Torch compilation, and CUDA Graph.
Use Cases
- Local or edge inference for large models and throughput/latency benchmarking.
- Prototyping inference stacks that require readable and customizable implementations.
- Learning and verifying inference optimizations or comparing model runtime performance.
Technical Characteristics
- API surface mirrors vLLM for straightforward swap-in of inference backends.
- Uses Torch compilation and CUDA Graph to reduce latency and supports tensor parallelism for multi-GPU scaling.
- Includes examples and benchmarks (
example.py,bench.py) to help users reproduce results and evaluate performance.