a50b4c2ac2
Move block selection logic from compute_chunked_prefill/decode methods to attention.py caller. This improves separation of concerns: - attention.py now calls select_blocks() before compute_chunked_*() - Policy methods receive pre-selected blocks via selected_blocks parameter - Enables sparse policies to implement custom block selection without modifying the compute path Changes: - policy.py: Add selected_blocks parameter to abstract methods - full_policy.py: Remove internal select_blocks calls, use passed blocks - xattn_bsa.py: Sync signatures for prefill/decode methods - attention.py: Add select_blocks calls before policy delegation 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 |