Mac mini M4 Pro: Which Models Are Actually Faster?
Ollama, MLX, llama.cpp on Mac mini M4 Pro: RAM limits and local LLM tests.
The Mac mini M4 Pro is a useful compact machine for local LLMs: 24-64 GB unified memory, 273 GB/s bandwidth, quiet and efficient. But benchmarks on Apple Silicon need careful framing — tokens per second depend heavily on model, quantization, context, and running apps.
What I tested
I compared Ollama, MLX, and llama.cpp on my 32 GB M4 Pro. Here’s what I noticed:
Ollama is the simplest path. ollama run works immediately, and MLX support for selected models makes it faster. Best for most users.
MLX is faster than standard GGUF on Apple Silicon. On my M4 Max, MLX was 10-30% faster. On the M4 Pro, the difference is smaller but noticeable.
llama.cpp gives the most control. If you need reproducible benchmarks, llama-bench is the tool. But it’s more cumbersome for everyday use.
RAM and model size
24 GB: 13B-class in Q4. Gets tight for 30B+.
32 GB: 30B-class comfortably, 70B-Q4 possible but tight.
64 GB: Full range. 70B-Q4 runs, anything above swaps.
The 273 GB/s bandwidth is the same across all M4 Pro SKUs — RAM size is the deciding factor.
How to measure yourself
Pin model version, document quant, fixed context length, disable background apps. Warm up before measuring. llama-bench gives reproducible PP and TG measurements. Compare prompt-eval and generation separately.
My verdict
The M4 Pro with 32-64 GB is the sweet spot for local LLMs. Runtime choice depends on use case — Ollama for everyday, MLX for speed, llama.cpp for reproducibility.
Based on tests with Mac Mini M4 Pro 32 GB, June 2026.
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Sources and review basis
These primary and reference sources form the basis of the technical assessment. Vendor claims and external benchmarks are identified as such in the article.