Runtime comparison
MLX LM vs TabbyAPI
MLX LM is a MIT-licensed runtime for macOS running on Metal; TabbyAPI is AGPL-3.0-licensed for Linux · Windows on CUDA · ROCm. MLX LM rates easier to set up (4/5 vs 3/5 in our sourced ratings). Pick MLX LM for maximum control over how the model runs; pick TabbyAPI for serving many requests at once.
| Spec | MLX LM | TabbyAPI |
|---|---|---|
| License | MIT | AGPL-3.0 |
| Operating systems | macOS | Linux · Windows |
| GPU backends | Metal | CUDA · ROCm |
| Install | pip | source |
| Ease of use | 4/5 | 3/5 |
| Quant formats | mlx | exl2 |
| Engine | mlx | exllamav2 |
| GitHub stars | 6,236 | 1,273 |
| Latest version | v0.31.3 | — |
| 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 →TabbyAPI
Best for serving many requests at once. Command line and server, runs on CUDA · ROCm.
Full TabbyAPI 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); TabbyAPI github.com/theroyallab/tabbyAPI (as of 2026-07-08).