vLLM-MLX – Run LLMs on Mac at 464 tok/s

3 comments

• $waybarrios 3 days ago

  Hey HN! I built vLLM-MLX alike framework on macOS, which is painfully slow on Apple Silicon machines.

  vLLM-MLX brings native GPU acceleration using Apple's MLX framework, with:

    • OpenAI-compatible API (drop-in replacement)
    • Multimodal: Text, Images, Video, Audio in one server
    • Continuous batching for concurrent users (3.4x speedup)
    • TTS in 10+ languages (Kokoro, Chatterbox)
    • MCP tool calling support
  
    Performance on M4 Max:
    - Llama-3.2-1B-4bit: 464 tok/s
    - Qwen3-0.6B: 402 tok/s
    - Whisper STT: 197x real-time
  

  Quick start: pip install -e . vllm-mlx serve mlx-community/Llama-3.2-3B-Instruct-4bit

  Works with standard OpenAI SDK. Happy to answer questions!

  GitHub: https://github.com/waybarrios/vllm-mlx

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  • $thebruce87m 2 days ago

    What’s the recommended RAM for running some of these? There is a “Memory” section but the numbers look low compared to what I was expecting - maybe this is right but they are heavily quantised.

    Basically trying to work out what I get to play with on my 16Gb M1.

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    • $mannycalavera42 2 days ago

      same here