# 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

```bash
pip install git+https://github.com/GeeeekExplorer/nano-vllm.git
```

## Quick Start

See `example.py` for usage. The API mirrors vLLM's interface with minor differences in the `LLM.generate` method.
```python
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
- 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     | 98.95    | 1353.86               |
| Nano-vLLM      | 133,966     | 101.90   | 1314.65               |