MLX vs llama.cpp on Mac: Which Should You Choose?

Choose llama.cpp for GGUF and cross-platform flexibility, or Rapid-MLX for an Apple Silicon MLX server with OpenAI- and Anthropic-compatible APIs. Test your own model and workload: decode and prompt processing can favor different engines.

Disclosure: We build Rapid-MLX. MLX is Apple's framework; Rapid-MLX is a server built on MLX. llama.cpp is a separate inference engine. Choose based on your model format, hardware and workload, not a universal speed claim.

What did the published Mac benchmark find?

On an idle M2 Pro Mac mini with 32 GB, the published benchmark measured three models using each framework's own tools. MLX led raw decode on Qwen and Gemma; llama.cpp led on gpt-oss and on prefill for all three. For Qwen3.6-35B-A3B, raw decode was 61.7 tokens/s for mlx-lm and 39.1 for llama.cpp; prefill was 277 and 460 tokens/s respectively. These are historical measurements, not a Rapid-MLX 1.0 measurement.

The test used llama-bench build 10330 with Metal, a 512-token prompt and 128 generated tokens, median of five; mlx-lm 0.31.3 used approximately 490 prompt tokens and 128 generated, median of three. Quantization classes matched but weights were not identical: Q4_K_M versus MLX 4-bit for Qwen and Gemma, native MXFP4 for gpt-oss. See raw results and the benchmark for server-path results and limitations.

Which should I pick?

Choose llama.cpp if GGUF support or running across different hardware matters most. Choose Rapid-MLX if you want an MLX-based Apple Silicon server with OpenAI- and Anthropic-compatible APIs and the built-in agent harness. For Apple's reference library and server, see mlx-lm. For a broad comparison see the compare hub.

Frequently asked questions

Is MLX always faster than llama.cpp on a Mac?

No. In our published M2 Pro test, the result depended on the model and whether the task was decode or prefill. The measurements above use different native quantizations and do not establish a universal winner.

Is Rapid-MLX the same as MLX?

No. MLX is Apple's machine learning framework. Rapid-MLX is an inference server built on MLX, with compatible APIs and an agent harness.

Can I use llama.cpp models with Rapid-MLX?

The benchmark used different native model formats. Check the model documentation for Rapid-MLX's supported models and formats before choosing weights.