Deploy Pixtral at Scale: vLLM + Docker Made Simple

Large language models are compute-heavy, and deploying them efficiently requires optimized inference engines like vLLM. In this guide, we’ll walk through containerizing Pixtral using Docker, running it with vLLM, and exposing the model endpoint for external access.

📦 Prerequisites

Docker installed (latest version recommended)
NVIDIA GPU with CUDA drivers (if running with GPU acceleration)
Model weights available (Pixtral Hugging Face repo or local)

🛠 Step 1: Create a Dockerfile

FROM nvidia/cuda:12.2.2-runtime-ubuntu22.04

# System dependencies
RUN apt-get update && apt-get install -y 
    git wget curl python3 python3-pip && 
    rm -rf /var/lib/apt/lists/*

# Install vLLM
RUN pip3 install --upgrade pip
RUN pip3 install vllm

# Set working directory
WORKDIR /app

# Expose port for API
EXPOSE 8000

# Default command (can be overridden)
CMD ["bash"]

⚙️ Step 2: Build the Docker Image

docker build -t pixtral-vllm .

📂 Step 3: Run the Container with Model Access

You can start the vLLM server inside the container to serve Pixtral.

docker run --gpus all -it -p 8000:8000 pixtral-vllm 
  python3 -m vllm.entrypoints.openai.api_server 
  --model <huggingface_repo_or_local_path_to_pixtral>

Replace <huggingface_repo_or_local_path_to_pixtral> with your model location.
The container will serve an OpenAI-compatible API on port 8000.

🌐 Step 4: Test the API Endpoint

After running the container, test with curl:

curl http://localhost:8000/v1/completions 
  -H "Content-Type: application/json" 
  -d '{"model": "pixtral", "prompt": "Hello Pixtral!"}'

Expected response:

{
  "id": "cmpl-1234",
  "object": "text_completion",
  "choices": [
    {"text": "Hello there!"}
  ]
}

📡 Step 5: Expose for External Access

If you want to make the API accessible from outside the host:

Map the container port 8000 to the host (-p 8000:8000).
Ensure firewall/security group allows inbound traffic on 8000.

For production, consider:

Reverse proxy with NGINX or Traefik
Adding authentication before exposing publicly

✅ Conclusion

You now have Pixtral deployed with vLLM inside Docker, exposed via an OpenAI-compatible API. This setup allows you to scale inference workloads efficiently, while keeping deployment portable.