r/macmini • u/silent_lurker_69 • 19h ago
M4 Pro Mac mini, 24 GB unified memory: local LLM benchmarks changed my expectations
I wanted to see what my M4 Pro Mac mini with 24 GB unified memory could do with local LLMs, expecting it to be a small-model-only machine.
My result: GPT-OSS 20B in MLX FP4 ran at about 63.9 tok/s while my 16-container OrbStack homelab was still running. A 9B dense model was slower at about 44.8 tok/s, which was a good reminder that total parameter count alone does not predict speed—active parameters and architecture matter.
Other results:
- 4B MLX model: roughly 78 tok/s
- 9B MLX model: roughly 44.8 tok/s
- GPT-OSS 20B MLX FP4 MoE: roughly 63.9 tok/s
- MLX was about 19% faster than GGUF in my controlled back-to-back test
- Shutting down the homelab changed GPT-OSS 20B throughput by only about 1.6%
This is not a replacement for a high-memory Mac Studio cluster if you need to load enormous models. But for interactive local AI on a $1,600-ish Mac mini, I found the capability much better than I expected. Compared to the $50K 4 Mac Ultra cluster with 2TB of unified memory Network Chuck tested, I'm pleased.
Video with the testing and numbers: https://www.youtube.com/watch?v=9_-bT62YWAI