b5c0ef3b7a
Remove all chunked prefill related documentation (ring buffer, sgDMA, Triton merge kernels, known issues) and replace with layer-wise offload system documentation including: - Design philosophy and benefits - Memory layout and per-layer KV size table - Prefill and decode flow pseudocode - Critical implementation details (sync offload, causal=False for decode) - Helper methods in HybridKVCacheManager 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Nano-vLLM
A lightweight vLLM implementation built from scratch.
Key Features
- 🚀 Fast offline inference - Comparable inference speeds to vLLM
- 📖 Readable codebase - Clean implementation in ~ 1,200 lines of Python code
- ⚡ Optimization Suite - Prefix caching, Tensor Parallelism, Torch compilation, CUDA graph, etc.
Installation
pip install git+https://github.com/GeeeekExplorer/nano-vllm.git
Model Download
To download the model weights manually, use the following command:
huggingface-cli download --resume-download Qwen/Qwen3-0.6B \
--local-dir ~/huggingface/Qwen3-0.6B/ \
--local-dir-use-symlinks False
Quick Start
See example.py for usage. The API mirrors vLLM's interface with minor differences in the LLM.generate method:
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)
outputs[0]["text"]
Benchmark
See bench.py for benchmark.
Test Configuration:
- Hardware: RTX 4070 Laptop (8GB)
- Model: Qwen3-0.6B
- Total Requests: 256 sequences
- Input Length: Randomly sampled between 100–1024 tokens
- Output Length: Randomly sampled between 100–1024 tokens
Performance Results:
| Inference Engine | Output Tokens | Time (s) | Throughput (tokens/s) |
|---|---|---|---|
| vLLM | 133,966 | 98.37 | 1361.84 |
| Nano-vLLM | 133,966 | 93.41 | 1434.13 |