vLLM Metal Plugin

High-performance LLM inference on Apple Silicon using MLX and vLLM

vLLM Metal is a plugin that enables vLLM to run on Apple Silicon Macs using MLX as the primary compute backend. It unifies MLX and PyTorch under a single lowering path.

Documentation: https://docs.vllm.ai/projects/vllm-metal/en/latest/

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Latest News 🔥

• [2026/08] vLLM Metal now uses M5 NAX tensor units to accelerate MHA, GQA, and MQA prefill.
• [2026/08] Qwen3.8 now runs on Metal! mlx-community/Qwen3.8-27B-8bit serves a 27B hybrid SDPA + GDN linear model on a single Apple Silicon Mac.
• [2026/04] We released the new version v0.2.0! Unified paged varlen Metal kernel is now the default attention backend. 83x TTFT, 3.6x throughput compared to v0.1.0.

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Architecture

Upstream vLLM supplies the API server, scheduler, and paged block manager; mlx_lm supplies the token-wise model layers; vllm-metal owns the request-aware attention path — the paged varlen kernel, M5 NAX prefill, and speculative decoding.

Requirements

• macOS 15 (Sequoia) or later, on Apple Silicon
• Native arm64 Python 3.12. Rosetta/x86_64 Python is not supported.

Supported Models

vllm-metal supports a growing set of models on Apple Silicon. See the full matrix in docs/supported_models.md.

Installation

curl -fsSL https://raw.githubusercontent.com/vllm-project/vllm-metal/main/install.sh | bash

Using the install script above, the following will be installed under the ~/.venv-vllm-metal directory (the default).

• vllm-metal plugin
• vllm core
• Related libraries

If you run source ~/.venv-vllm-metal/bin/activate, the vllm CLI becomes available and you can access the vLLM right away.

For how to use the vllm CLI, please refer to the official vLLM guide. https://docs.vllm.ai/en/latest/cli/