Can I Run Nemotron 3 Nano Omni on Mac mini (M4 Pro, 64 GB)?

Written by Jakub Rusinowski · Last updated September 6, 2026

Yes — comfortably

Yes, comfortably — Nemotron 3 Nano Omni 30B-A3B at Q8_0 needs about 34.5 GB of the 48 GB usable on Mac mini (M4 Pro, 64 GB), leaving ~13.5 GB spare and running at ~33.1 tok/s (estimated), with room for about 65,536 tokens of context.

Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~33.1 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

Nemotron 3 Nano Omni on Mac mini (M4 Pro, 64 GB): memory by quantization

Quant	Memory needed	Fits 48 GB?	Max context	Est. speed	Download
F16	62.6 GB	✗ No	—	—	60 GB
Q8_0	34.5 GB	✓ Yes	64K	~33.1 tok/s	31.9 GB
Q6_K	27.2 GB	✓ Yes	64K	~39.4 tok/s	24.6 GB
Q5_K_M	23.8 GB	✓ Yes	64K	~43.2 tok/s	21.3 GB
Q4_K_M	20.7 GB	✓ Yes	128K	~47.5 tok/s	18.1 GB
Q3_K_M	15.4 GB	✓ Yes	128K	~57.2 tok/s	12.8 GB
Q2_K	12.4 GB	✓ Yes	128K	~64.4 tok/s	9.9 GB

What to watch out for

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 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 inferred from the parameter count because this model does not publish its full attention configuration.

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