Runtime comparison
MLX LM vs vLLM
MLX LM is a MIT-licensed runtime for macOS running on Metal; vLLM is Apache-2.0-licensed for Linux on CUDA · ROCm. MLX LM rates easier to set up (4/5 vs 2/5 in our sourced ratings). Pick MLX LM for maximum control over how the model runs; pick vLLM for serving many requests at once.
| Spec | MLX LM | vLLM |
|---|---|---|
| License | MIT | Apache-2.0 |
| Operating systems | macOS | Linux |
| GPU backends | Metal | CUDA · ROCm |
| Install | pip | pip / source |
| Ease of use | 4/5 | 2/5 |
| Quant formats | mlx | awq, gptq |
| Engine | mlx | vllm |
| GitHub stars | 6,236 | 85,770 |
| Latest version | v0.31.3 | v0.24.0 |
| Graphical app (GUI) | No | No |
| Server mode | Yes | Yes |
| OpenAI-compatible API | Yes | Yes |
| CPU offload | No | No |
| Multi-GPU | No | Yes |
| Speculative decoding | No | Yes |
| LoRA support | Yes | Yes |
| KV-cache quantization | Yes | Yes |
Which should you use?
MLX LM
Best for maximum control over how the model runs. Command line and server, runs on Metal.
Full MLX LM guide →vLLM
Best for serving many requests at once. Command line and server, runs on CUDA · ROCm.
Full vLLM guide →Neither runtime changes whether a model fits — that is your memory and quantization. Check your hardware first, then pick the runtime.
Sources: MLX LM github.com/ml-explore/mlx-lm (as of 2026-07-08); vLLM github.com/vllm-project/vllm (as of 2026-07-08).