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docs/getting_started/installation/gpu.apple.inc.md

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+--8<-- [start:installation]
+
+For GPU-accelerated inference on Apple Silicon, use [vLLM-Metal](https://github.com/vllm-project/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](https://huggingface.co/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](#set-up-using-vllm-metal) section below.
+
+--8<-- [end:installation]
+--8<-- [start:requirements]
+
+- OS: macOS Sonoma or later
+- Hardware: Apple Silicon
+- Metal support enabled
+
+!!! note
+    See the [Set up using vLLM-Metal](#set-up-using-vllm-metal) section below for installation instructions.
+
+--8<-- [end:requirements]
+--8<-- [start:set-up-using-python]
+
+## 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](https://github.com/vllm-project/vllm-metal#installation).
+
+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](https://huggingface.co/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:
+
+```bash
+# 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:
+
+```bash
+source ~/.venv-vllm-metal/bin/activate
+vllm chat
+```
+
+#### Option 2: API requests with curl
+
+```bash
+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
+
+```python
+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](../../serving/online_serving/openai_compatible_server.md).
+
+--8<-- [end:set-up-using-python]
+--8<-- [start:pre-built-wheels]
+
+vLLM-Metal is installed via the vLLM-Metal package. See the [Set up using vLLM-Metal](#set-up-using-vllm-metal) section above.
+
+--8<-- [end:pre-built-wheels]
+--8<-- [start:build-wheel-from-source]
+
+For build instructions from source, refer to the [vLLM-Metal documentation](https://github.com/vllm-project/vllm-metal#installation).
+
+--8<-- [end:build-wheel-from-source]
+--8<-- [start:pre-built-images]
+
+--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](https://github.com/vllm-project/vllm-metal).
+
+--8<-- [end:supported-features]