vllm-project--vllm/docs/getting_started/installation/gpu.apple.inc.md

--8<-- [start:installation]

For GPU-accelerated inference on Apple Silicon, use vLLM-Metal, a community-maintained hardware plugin that uses MLX as the compute backend and provides native GPU acceleration via Apple's Metal framework.

vLLM-Metal works with MLX-optimized models from the mlx-community organization on Hugging Face, which provides quantized versions of popular models optimized for Apple Silicon.

!!! tip For installation and usage instructions, see the Set up using vLLM-Metal section below.

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• OS: macOS Sonoma or later
• Hardware: Apple Silicon
• Metal support enabled

!!! note See the Set up using vLLM-Metal section below for installation instructions.

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Set up using vLLM-Metal

vLLM-Metal is distributed as a separate package that provides native GPU acceleration on Apple Silicon.

To install vLLM-Metal, follow the installation instructions in the vLLM-Metal documentation.

The installation will:

1. Set up the appropriate Python environment
2. Install MLX and required dependencies
3. Install the vLLM-Metal package

After installation, you can start using vLLM with Metal GPU acceleration.

!!! tip When using vLLM-Metal, use models from the mlx-community on Hugging Face for best performance. These models are optimized for MLX and often include quantized versions (4-bit, 8-bit) that run efficiently on Apple Silicon.

Example model: `mlx-community/Qwen2.5-0.5B-Instruct-4bit`

Using vLLM-Metal

After installation, vLLM-Metal provides an easy-to-use CLI for running an OpenAI-compatible API server:

# Activate the vLLM-Metal environment
source ~/.venv-vllm-metal/bin/activate

# Start the API server (specify your mlx-community model or it will use default)
vllm serve

Once the server is running, you have multiple options to interact with it:

Option 1: Interactive chat

Open a new terminal and start an interactive chat session:

source ~/.venv-vllm-metal/bin/activate
vllm chat

Option 2: API requests with curl

curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [{"role": "user", "content": "Hello!"}],
    "max_tokens": 50
  }'

Option 3: Python with OpenAI SDK

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="dummy"  # No auth required for local server
)

response = client.chat.completions.create(
    model="mlx-community/Qwen2.5-0.5B-Instruct-4bit",
    messages=[{"role": "user", "content": "Hello!"}]
)

print(response.choices[0].message.content)

For more details on the vllm CLI commands, see the OpenAI-compatible server documentation.

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vLLM-Metal is installed via the vLLM-Metal package. See the Set up using vLLM-Metal section above.

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For build instructions from source, refer to the vLLM-Metal documentation.

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--8<-- [end:pre-built-images] --8<-- [start:build-image-from-source]

--8<-- [end:build-image-from-source] --8<-- [start:supported-features]

vLLM-Metal provides:

• Native GPU acceleration using Metal
• MLX-based compute backend optimized for Apple Silicon
• OpenAI-compatible API server
• Support for popular model architectures

For specific feature support and limitations, refer to the vLLM-Metal documentation.

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