# vllm

This rule guides Python work with vLLM, from environment setup to inference structure and observability. Use it while building or maintaining an LLM serving project.

- rule · `type=rule` · Cursor · ★ 3580
- Author: sanjeed5
- License: CC0-1.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/sanjeed5/awesome-cursor-rules-mdc/tree/HEAD/rules-mdc/vllm.mdc
- Updated: 2026-05-19
- HTML: https://affpapa.org/en/nevibecode/rules/vllm

## What it does

The `.cursor/rules/vllm.mdc` file targets Python files matching `**/*.py`. Its guidance separates inference from data preparation and business logic, makes `LLM` settings explicit, and keeps configuration out of request handlers. That structure makes the service easier to test in parts.

The rule advises using an isolated `conda` environment and matching the pinned vLLM version to CUDA. It names a minimum GPU compute capability of 7.0. Other topics include continuous batching, parallel sampling with `n`, quantization, and OpenLIT instrumentation. Initialize OpenLIT before creating `vllm.LLM`; GPUs below the stated compute capability do not meet the rule’s requirement.

Deployment guidance covers Docker and Kubernetes with GPU device plugins, as well as model versioning and endpoint tests. It recommends checking latency, throughput, and output correctness. `FastAPI`, `MLflow`, and `DVC` appear as examples, not required dependencies.

## Install

### Cursor (native platform)

**Manually: put the files in place**
- File: `.cursor/rules/vllm.mdc` — Frontmatter: description, globs, alwaysApply

### Claude Code

**Manually: put the files in place**
- File: `.claude/rules/vllm.md` — The paths frontmatter field limits the files: move globs from the source into paths

### Codex

**AGENTS.md**
- Codex reads AGENTS.md: paste the rule text there

### GitHub Copilot

**Manually: put the files in place**
- File: `.github/instructions/vllm.instructions.md` — The applyTo field sets which files it applies to: move globs from the source into applyTo

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/rules/vllm.md`

### Cline

**Manually: put the files in place**
- File: `.clinerules/vllm.md`

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/sanjeed5/awesome-cursor-rules-mdc/tree/HEAD/rules-mdc/vllm.mdc

## When to use it
- separate inference from data preparation
- pin vLLM to the CUDA version in use
- generate multiple samples with `n`
- instrument a vLLM service with OpenLIT
- test an inference endpoint before deployment

## Example prompts
- Review this Python service against the vLLM rule and separate its inference and preprocessing code.
- Check whether this project’s vLLM version and CUDA setup match the rule’s guidance.
- Add OpenLIT initialization before `vllm.LLM` is created, then review the endpoint tests.

## Source text · vllm.mdc · CC0-1.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 16f132e091917ad0.

BEGIN THIRD-PARTY FILE 16f132e091917ad0
````text
---
description: Definitive guidelines for writing high-performance, maintainable, and production-ready LLM inference code using vLLM.
globs: **/*.py
---
# vLLM Best Practices

vLLM is the gold standard for high-throughput LLM inference. Adhere to these guidelines to maximize performance, ensure reproducibility, and maintain robust LLM services.

## 1. Environment & Installation

**Always use isolated `conda` environments and pin `vLLM` to your CUDA version.** This prevents binary incompatibilities and ensures reproducible deployments.

❌ BAD:
```python
# Unreliable global install, prone to CUDA/PyTorch mismatches
pip install vllm
```

✅ GOOD:
```bash
# For NVIDIA GPUs (CUDA 12.1 is default, check vLLM docs for current default)
conda create -n vllm_env python=3.10 -y
conda activate vllm_env
pip install vllm==0.5.2 # Pin the exact version in requirements.txt

# For NVIDIA GPUs (CUDA 11.8 specific version, e.g., v0.4.0)
conda create -n vllm_cu118 python=3.10 -y
conda activate vllm_cu118
export VLLM_VERSION=0.4.0
export PYTHON_VERSION=310
pip install https://github.com/vllm-project/vllm/releases/download/v${VLLM_VERSION}/vllm-${VLLM_VERSION}+cu118-cp${PYTHON_VERSION}-cp${PYTHON_VERSION}-manylinux1_x86_64.whl --extra-index-url https://download.pytorch.org/whl/cu118
```
*   **Action**: Ensure `requirements.txt` explicitly lists `vllm==X.Y.Z`.
*   **Hardware**: Target GPUs with compute capability ≥ 7.0 (V100, A100, H100, etc.).

## 2. Code Organization & Structure

**Separate inference logic from data preprocessing and business logic.** Use a modular approach for clarity and testability.

❌ BAD:
```python
# main.py - monolithic script, hard to test or scale
from vllm import LLM, SamplingParams
# ... data loading, preprocessing, model init, inference, postprocessing ...
```

✅ GOOD:
```python
# llm_service/inference_engine.py
from vllm import LLM, SamplingParams
from typing import List

class InferenceEngine:
    def __init__(self, model_path: str, **kwargs):
        """Initializes the vLLM engine with specified model and configurations."""
        self.llm = LLM(model=model_path, **kwargs)
        self.sampling_params = SamplingParams(temperature=0.7, top_p=0.95, max_tokens=256)

    def generate(self, prompts: List[str]) -> List[str]:
        """Generates responses for a list of prompts using configured sampling parameters."""
        outputs = self.llm.generate(prompts, self.sampling_params)
        return [output.outputs[0].text for output in outputs]

# llm_service/main.py (or API endpoint, e.g., with FastAPI)
from .inference_engine import InferenceEngine
from typing import Dict

def serve_llm_request(request_data: Dict) -> Dict:
    """Handles an incoming LLM request, orchestrating preprocessing, inference, and postprocessing."""
    # 1. Preprocessing (e.g., validate input, format prompt from request_data)
    prompts = [request_data["text"]] # Simplified example
    
    # 2. Inference
    # Model path and parallelism should be loaded from config, not hardcoded here
    engine = InferenceEngine(model_path="mistralai/Mistral-7B-Instruct-v0.2",
                             tensor_parallel_size=2) # Explicitly configure parallelism
    results = engine.generate(prompts)
    
    # 3. Postprocessing (e.g., format output for API response, add metadata)
    return {"generated_text": results[0]}
```
*   **Action**: Define `LLM` engine parameters explicitly (e.g., `tensor_parallel_size` for distributed inference).

## 3. Performance & Scalability

**Leverage vLLM's core optimizations: continuous batching, parallel sampling, and quantization.**

### 3.1 Continuous Batching & Parallel Sampling
````
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