Overview
This page compares two formats using the project docs, fetched 2026-10-12. It does not pick a winner by speed. Community benchmarks disagree, and their numbers are not repeated.
What is the difference between MLX and GGUF?
MLX is Apple’s array framework, and MLX LM is the Python package that generates text with it on Apple silicon. GGUF is the single-file format used by llama.cpp. They are different files. A GGUF download is not an MLX download.
The mlx-lm README says MLX LM generates text and fine-tunes models on Apple silicon with MLX. The default model named there is mlx-community/Llama-3.2-3B-Instruct-4bit. MLX weights are also published under mlx-community. The GGUF format is documented by Hugging Face at GGUF on the Hub. llama.cpp’s own build doc is docs/build.md.
Which apps run MLX and which run GGUF on a Mac?
LM Studio’s app page says it runs llama.cpp (GGUF) or MLX models, and that on Apple silicon it supports running LLMs using Apple’s MLX. Ollama’s macOS page, fetched the same day, requires macOS Sonoma 14 or newer and “Apple M series (CPU and GPU support) or x86 (CPU only).” That page does not mention MLX. mlx-lm runs MLX models. llama.cpp runs GGUF, and on macOS its build doc says Metal is enabled by default.
The app comparison is lm-studio-vs-ollama-mac. Commands are on run-llm-on-mac.
Do MLX and GGUF quantize models the same way?
No. GGUF has many named types. MLX LM’s README shows a convert command and does not state a group size on the page fetched here, so group size is unknown.
The llama.cpp quantize README lists types including Q4_K_M and Q8_0. For Llama 3.1 8B it lists Q4_K_M at 4.8944 bits per weight and 4.58 GiB, and Q8_0 at 8.5008 bits per weight and 7.95 GiB. Its memory table lists 8B Q4_K_M at 4.9 GB and 70B Q4_K_M at 43.1 GB. Speed columns from that README are not copied, because they do not name Apple silicon.
mlx-lm documents:
pip install mlx-lm
mlx_lm.convert --model mistralai/Mistral-7B-Instruct-v0.3 -qA later example uploads to a repo named mlx-community/my-4bit-mistral. This page does not treat the -q flag as a published group size. The sister note for the shared quantization explanation is llm quantization explained. Weight arithmetic for a chosen bit width is on methodology.
Which should I choose?
Default to the format your app documents for the model you want. Use MLX when you are in the mlx-lm Python tools on Apple silicon. Use GGUF when you need the same file in llama.cpp, which also builds for other operating systems.
| Question | MLX via mlx-lm | GGUF via llama.cpp |
|---|---|---|
| What it is | Apple array framework and MLX LM tools | llama.cpp file format |
| Named on LM Studio’s app page | Yes, on Apple silicon | Yes, llama.cpp (GGUF) |
| Named on Ollama’s macOS page | Not on the page fetched 2026-10-12 | The macOS page does not name GGUF. The Ollama README shows ollama run and does not name the file extension in the lines quoted on lm-studio-vs-ollama-mac |
| Quant types | -q is shown; group size unknown | Named types such as Q4_K_M and Q8_0, with sizes in the quantize README |
| Speed | Unknown on this page | Unknown on this page |
Memory fit for a bit width you choose is independent of this table. See best-local-llm-for-mac.
Questions
Is MLX faster than GGUF?
Unknown. Neither the mlx-lm README nor the llama.cpp build doc fetched on 2026-10-12 publishes a head-to-head tokens-per-second result on a named Mac.
Can Ollama load an MLX folder?
Unknown from Ollama’s macOS page, which does not mention MLX. Check that page again before assuming support.