Can I Run Llama 3.2 Family on Mac mini (M4 Pro, 64 GB)?

Written by Jakub Rusinowski · Last updated August 15, 2026

Yes — comfortably

Yes, comfortably — Llama 3.2 11B Vision Instruct at Q8_0 needs about 13.4 GB of the 48 GB usable on Mac mini (M4 Pro, 64 GB), leaving ~34.6 GB spare and running at ~12.1 tok/s (estimated), with room for about 65,536 tokens of context.

Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~12.1 tok/s

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Mac mini (M4 Pro, 64 GB) — what it gives a model

Usable memory for models	48 GB
Memory bandwidth	273 GB/s
Form factor	Mini PC
Operating system	macOS
Memory upgradeable	No — soldered

Llama 3.2 Family on Mac mini (M4 Pro, 64 GB): memory by quantization

Quant	Memory needed	Fits 48 GB?	Max context	Est. speed	Download
F16	23.3 GB	✓ Yes	64K	~6.7 tok/s	21.2 GB
Q8_0	13.4 GB	✓ Yes	64K	~12.1 tok/s	11.3 GB
Q6_K	10.8 GB	✓ Yes	64K	~15.2 tok/s	8.7 GB
Q5_K_M	9.7 GB	✓ Yes	64K	~17.3 tok/s	7.5 GB
Q4_K_M	8.5 GB	✓ Yes	64K	~19.9 tok/s	6.4 GB
Q3_K_M	6.7 GB	✓ Yes	64K	~26.5 tok/s	4.5 GB
Q2_K	5.6 GB	✓ Yes	64K	~32.5 tok/s	3.5 GB

Which Llama 3.2 Family sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Llama 3.2 90B Vision Instruct	57.8 GB	✗ Too large	—
Llama 3.2 11B Vision Instruct	8.5 GB	✓ Fits	~19.9 tok/s
Llama 3.2 3B Instruct	3.7 GB	✓ Fits	~52.4 tok/s
Llama 3.2 1B Instruct	1.8 GB	✓ Fits	~112.4 tok/s

What to watch out for

1 larger variant of Llama 3.2 Family does not fit and would need CPU offload or different hardware.
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 Pro 64 GB limitations

M4 Pro memory bandwidth is half the M4 Max's, so large models that fit will still generate roughly half as fast.

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.
64 GB unified memory at 273 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 computed from this model's published attention configuration.

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