Can I Run Mistral Small 3.2 on Mac mini (M4, 16 GB)?

Written by Jakub Rusinowski · Last updated September 6, 2026

Yes

Yes — Mistral Small 3.2 24B at Q2_K needs about 10.2 GB of the 12 GB usable on Mac mini (M4, 16 GB) (~1.8 GB spare), at ~7.5 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: medium · Recommended quantization: Q2_K · Estimated speed: ~7.5 tok/s

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Mac mini (M4, 16 GB) — what it gives a model

Usable memory for models	12 GB
Memory bandwidth	120 GB/s
Form factor	Mini PC
Operating system	macOS
Memory upgradeable	No — soldered
Price	$599 (lib/data/ai-stations.ts (street price), checked 2026-07-06)

Mistral Small 3.2 on Mac mini (M4, 16 GB): memory by quantization

Quant	Memory needed	Fits 12 GB?	Max context	Est. speed	Download
F16	49.6 GB	✗ No	—	—	47.2 GB
Q8_0	27.5 GB	✗ No	—	—	25.1 GB
Q6_K	21.8 GB	✗ No	—	—	19.4 GB
Q5_K_M	19.2 GB	✗ No	—	—	16.7 GB
Q4_K_M	16.7 GB	✗ No	—	—	14.2 GB
Q3_K_M	12.5 GB	✗ No	—	—	10.1 GB
Q2_K	10.2 GB	✓ Yes	16K	~7.5 tok/s	7.8 GB

What to watch out for

Q2_K is the only quantization that fits, and it is a heavily degraded one — expect noticeably worse output than the same model at Q4_K_M. A smaller model at Q4 is usually the better trade.
1 larger variant of Mistral Small 3.2 does not fit and would need CPU offload or different hardware.
This model does not publish its full attention configuration, so the KV-cache share of these figures is inferred from its parameter count rather than computed exactly.
Memory on this machine is not upgradeable, so the configuration you buy is the ceiling for every model you will ever run on it.

Mac mini M4 16 GB limitations

The cheapest credible always-on local-AI box, but 16 GB caps it at small and mid-size models.
Memory is soldered; upgrading means replacing the machine.

Recommended setup

Ollama or LM Studio (Metal) — MLX for the fastest Apple-native throughput

How these numbers are calculated

Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
16 GB unified memory at 120 GB/s, shared between CPU and GPU.
macOS reserves a share of unified memory for the system, so not all of it is available to a model.
Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
KV cache is inferred from the parameter count because this model does not publish its full attention configuration.

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