66 lines
2.1 KiB
Markdown
66 lines
2.1 KiB
Markdown
<p align="center">
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<img width="300" src="assets/logo.png">
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</p>
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<p align="center">
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<a href="https://trendshift.io/repositories/15323" target="_blank"><img src="https://trendshift.io/api/badge/repositories/15323" alt="GeeeekExplorer%2Fnano-vllm | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
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</p>
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# Nano-vLLM
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A lightweight vLLM implementation built from scratch.
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## Key Features
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* 🚀 **Fast offline inference** - Comparable inference speeds to vLLM
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* 📖 **Readable codebase** - Clean implementation in ~ 1,200 lines of Python code
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* ⚡ **Optimization Suite** - Prefix caching, Tensor Parallelism, Torch compilation, CUDA graph, etc.
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## Installation
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```bash
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pip install git+https://github.com/GeeeekExplorer/nano-vllm.git
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```
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## Model Download
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To download the model weights manually, use the following command:
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```bash
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huggingface-cli download --resume-download Qwen/Qwen3-0.6B \
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--local-dir ~/huggingface/Qwen3-0.6B/ \
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--local-dir-use-symlinks False
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```
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## Quick Start
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See `example.py` for usage. The API mirrors vLLM's interface with minor differences in the `LLM.generate` method:
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```python
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from nanovllm import LLM, SamplingParams
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llm = LLM("/YOUR/MODEL/PATH", enforce_eager=True, tensor_parallel_size=1)
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sampling_params = SamplingParams(temperature=0.6, max_tokens=256)
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prompts = ["Hello, Nano-vLLM."]
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outputs = llm.generate(prompts, sampling_params)
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outputs[0]["text"]
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```
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## Benchmark
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See `bench.py` for benchmark.
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**Test Configuration:**
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- Hardware: RTX 4070 Laptop (8GB)
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- Model: Qwen3-0.6B
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- Total Requests: 256 sequences
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- Input Length: Randomly sampled between 100–1024 tokens
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- Output Length: Randomly sampled between 100–1024 tokens
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**Performance Results:**
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| Inference Engine | Output Tokens | Time (s) | Throughput (tokens/s) |
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|----------------|-------------|----------|-----------------------|
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| vLLM | 133,966 | 98.37 | 1361.84 |
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| Nano-vLLM | 133,966 | 93.41 | 1434.13 |
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## Star History
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[](https://www.star-history.com/#GeeeekExplorer/nano-vllm&Date) |