MLX vs GGUF on a Mac: which is faster?

It depends on the job, and public tests conflict. MLX usually shows higher generation tok/s, but GGUF in llama.cpp can finish long-prompt, short-answer tasks faster because prompt processing dominates. We haven't measured it ourselves, so test both on your Mac with your real prompts.

Checked 2026-10-22 · memory figures are estimates

What public benchmarks show PUBLIC SOURCES

Source	Setup	Result
famstack.dev	Qwen3.5-35B-A3B, M1 Max 64GB, LM Studio	MLX 4-bit showed 57 tok/s generation vs 29 for GGUF Q4_K_M, but GGUF finished all five short-output document tasks faster
baem1n/llm-bench	Qwen3.5 models, M5 Max	MLX ahead of llama.cpp on generation tok/s, e.g. 138.3 vs 91.0 on 35B-A3B
apple-silicon-arena	Base M4	Decode winner flips with machine load; memory use comparable once context matches

Quoted from the linked pages, not tested by us. Our own speed data is llama.cpp (GGUF) only.

How to test it on your Mac

Install both engines. Use LM Studio, which runs both MLX and GGUF models, or mlx-lm and llama.cpp directly.
Load the same model at a similar quant. For example a 4-bit MLX build and a Q4_K_M GGUF of the same model. Note the bits per weight differ slightly.
Time a real task end to end. Measure total time from sending the prompt to the last token, not only the generation tok/s counter, with your usual prompt length.

How we got this answer

We compared three public benchmarks that ran both engines on the same Mac. They disagree because they measure different things: generation speed alone, or total time including prompt processing. Memory needs are about the same for a 4-bit build either way; see the Mac tiers for what fits.

16GB Mac · 24GB Mac · 64GB Mac · 128GB Mac

FAQ

Does MLX use less memory than GGUF?

Not meaningfully at the same bit width. One public test found an apparent MLX memory win came from llama.cpp pre-allocating a larger context; with matched context the gap was about 0.3 GB.

Why is the tok/s counter misleading?

It usually counts only generation. Long prompts must be processed first, and that prefill time can dominate, so a higher counter can still mean a slower answer.

Which should I use for coding agents?

Agents send long prompts with short replies, which is where GGUF did relatively well in one public test. We haven't measured it, so time both with your own agent.

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