The short version
- MLX is Apple's machine learning framework for Apple Silicon. mlx-lm runs language models on it.
- llama.cpp is a fast C/C++ inference engine that runs GGUF models on almost any hardware, including Macs through Metal.
- Ollama is the easy front end: one command to download and run a model, with a local API. On Apple Silicon it now has an MLX-powered preview.
- For the fastest Mac-native results use MLX; for the widest choice of models and platforms, llama.cpp; for the easiest start, Ollama or LM Studio.
1What each one is
| MLX (mlx-lm) | llama.cpp | Ollama | |
|---|---|---|---|
| Made by | Apple, open source (MIT) | Open-source community | Ollama, open source |
| Runs on | Apple Silicon | Mac, Windows, Linux, many GPUs | Mac, Windows, Linux |
| Model format | MLX (safetensors) | GGUF | Its own library; GGUF and, in preview, MLX on Mac |
| Best for | Speed and research on Macs | Portability, quantization choice | Ease of use, apps that need an API |
| Training and fine-tuning | Yes (LoRA and full) | Limited | No |
2Speed on Apple Silicon
All three are limited by the same thing when generating text: memory bandwidth. Differences come from how well each uses the GPU and how its models are quantized.
MLX is written for Apple Silicon's unified memory and, on M5 chips, uses the GPU's neural accelerators: Apple measured time to first token 3.3 to 4.1 times faster than on M4 (Apple). In March 2026 Ollama began moving to MLX on Apple Silicon in a preview with large speedups for supported models (Ollama), which says a lot about where Mac performance is.
llama.cpp remains very fast and well measured on Macs; its public benchmark thread is the best reference for comparing chips (llama.cpp). For numbers on MLX specifically, see MLX Bench.
3Which one to use
- You want to chat with a model today: LM Studio (an app that runs both MLX and GGUF models) or Ollama.
- You're building an app that needs a local API: Ollama, or
mlx_lm.serverfor an OpenAI-compatible endpoint on MLX. - You want the most speed from a Mac, or to fine-tune: MLX with mlx-lm.
- You need the same setup on Mac, Linux and Windows, or a specific GGUF quantization: llama.cpp.
Our step-by-step guide to running an LLM on your Mac covers all of them.
4MLX beyond one Mac
MLX also runs across several Macs, over Thunderbolt 5 with RDMA (Mac AI clusters). And it's the engine of MLX Network: MLX Node runs AI jobs on the MLX framework for the network and earns MLX Tokens for verified work. MLX Network is an independent project, not made by Apple; here's the difference.
5Questions
- Is MLX faster than llama.cpp?
- On Apple Silicon, MLX is often as fast or faster for generation and especially for prompt processing on M5 chips. Results vary by model and quantization, so compare on your own Mac.
- Can Ollama use MLX?
- Yes, in preview since Ollama 0.19 on Apple Silicon, for supported models, on Macs with more than 32 GB of memory.
- Do MLX and GGUF models differ?
- They're different file formats for the same kinds of models. Many popular models are published in both; the mlx-community organization on Hugging Face has thousands of MLX conversions.