Nano-vLLM — Overview
Nano-vLLM is a compact, readable implementation of a vLLM-like inference engine designed for fast offline LLM inference. The project emphasizes a small, understandable codebase (~1,200 lines of Python) while applying practical performance optimizations so users can run and experiment with modern foundation models locally.
Key features
- Fast offline inference: benchmarked to achieve throughput comparable to vLLM on consumer GPUs.
- Readable codebase: small and approachable implementation that is easy to inspect and modify.
- Optimization suite: includes prefix caching, tensor parallelism, Torch compilation, and CUDA graph support to accelerate generation.
- vLLM-like API: mirrors vLLM's interface with minimal differences, making it familiar to users of vLLM.
Installation & model download
- Install directly from GitHub: pip install git+https://github.com/GeeeekExplorer/nano-vllm.git
- Model weights: example in README shows using huggingface-cli to download model weights (e.g., Qwen/Qwen3-0.6B).
Quick start (example)
from nanovllm import LLM, SamplingParams
llm = LLM("/YOUR/MODEL/PATH", enforce_eager=True, tensor_parallel_size=1)
sampling_params = SamplingParams(temperature=0.6, max_tokens=256)
prompts = ["Hello, Nano-vLLM."]
outputs = llm.generate(prompts, sampling_params)
print(outputs[0]["text"])Benchmarks & recommended hardware
The README includes a benchmark run on an RTX 4070 Laptop (8GB) using Qwen3-0.6B. Under the test configuration (256 sequences, random input/output lengths 100–1024 tokens), Nano-vLLM showed slightly better throughput vs vLLM in the provided test (e.g., Nano-vLLM: 1434.13 tokens/s vs vLLM: 1361.84 tokens/s in that run). Benchmarks are illustrative — results will vary by model, GPU, driver, and configuration.
Use cases
- Local/offline inference of LLMs for development and experimentation.
- Research and education where compact, readable inference code is valuable.
- Performance tuning and prototyping of optimizations (cache strategies, parallelism).
Compatibility & limitations
- Designed to work with Hugging Face-style model weights (README examples use Hugging Face models).
- Small codebase trades off some production robustness for readability/experimentation convenience; for large-scale production serving, more feature-rich inference stacks may be preferable.
- Performance depends heavily on model size, GPU memory, and the exact optimization flags used.
Summary
Nano-vLLM is a pragmatic, developer-friendly inference implementation that makes it easier to understand and experiment with vLLM-style optimizations while achieving competitive local inference speed.
