---
title: vLLM
description: "Configure vLLM as an LLM provider in Deja Vu for high-performance local inference with GPU-optimized serving."
---

[vLLM](https://docs.vllm.ai/) is a high-performance inference engine for large language models that provides significant performance improvements for local inference. It's designed to maximize throughput and memory efficiency for serving LLMs.

## Prerequisites

1. **Install vLLM**:

   ```bash
   pip install vllm
   ```

2. **Start vLLM server**:

   ```bash
   # For testing with a small model
   vllm serve microsoft/DialoGPT-medium --port 8000

   # For production with a larger model (requires GPU)
   vllm serve Qwen/Qwen2.5-32B-Instruct --port 8000
   ```

## Usage

```python
import os
from dejavu import Memory

os.environ["OPENAI_API_KEY"] = "your-api-key"  # used for embedding model

config = {
    "llm": {
        "provider": "vllm",
        "config": {
            "model": "Qwen/Qwen2.5-32B-Instruct",
            "vllm_base_url": "http://localhost:8000/v1",
            "temperature": 0.1,
            "max_tokens": 2000,
        }
    }
}

m = Memory.from_config(config)
messages = [
    {"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
    {"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
    {"role": "user", "content": "I'm not a big fan of thrillers, but I love sci-fi movies."},
    {"role": "assistant", "content": "Got it! I'll avoid thrillers and suggest sci-fi movies instead."}
]
m.add(messages, user_id="alice", metadata={"category": "movies"})
```

## Configuration Parameters

| Parameter       | Description                       | Default                       | Environment Variable |
| --------------- | --------------------------------- | ----------------------------- | -------------------- |
| `model`         | Model name running on vLLM server | `"Qwen/Qwen2.5-32B-Instruct"` | -                    |
| `vllm_base_url` | vLLM server URL                   | `"http://localhost:8000/v1"`  | `VLLM_BASE_URL`      |
| `api_key`       | API key (dummy for local)         | `"vllm-api-key"`              | `VLLM_API_KEY`       |
| `temperature`   | Sampling temperature              | `0.1`                         | -                    |
| `max_tokens`    | Maximum tokens to generate        | `2000`                        | -                    |

## Environment Variables

You can set these environment variables instead of specifying them in config:

```bash
export VLLM_BASE_URL="http://localhost:8000/v1"
export VLLM_API_KEY="your-vllm-api-key"
export OPENAI_API_KEY="your-openai-api-key"  # for embeddings
```

## Benefits

- **High Performance**: 2-24x faster inference than standard implementations
- **Memory Efficient**: Optimized memory usage with PagedAttention
- **Local Deployment**: Keep your data private and reduce API costs
- **Easy Integration**: Drop-in replacement for other LLM providers
- **Flexible**: Works with any model supported by vLLM

## Troubleshooting

1. **Server not responding**: Make sure vLLM server is running

   ```bash
   curl http://localhost:8000/health
   ```

2. **404 errors**: Ensure correct base URL format

   ```python
   "vllm_base_url": "http://localhost:8000/v1"  # Note the /v1
   ```

3. **Model not found**: Check model name matches server

4. **Out of memory**: Try smaller models or reduce `max_model_len`

   ```bash
   vllm serve Qwen/Qwen2.5-32B-Instruct --max-model-len 4096
   ```

## Config

All available parameters for the `vllm` config are present in [Master List of All Params in Config](../config).
