Can I Run Gemma 2 Family on Mac mini (M4, 16 GB)?

Superseded model. Gemma 2 Family has been superseded by Gemma 4. This page is kept for reference; the newer family is a better starting point. View Gemma 4 →

Written by Jakub Rusinowski · Last updated June 27, 2024

Yes, but it is tight

Yes, but it is tight — Gemma 2 9B IT at Q6_K needs about 11 GB of the 12 GB usable on Mac mini (M4, 16 GB), leaving only ~1 GB before the runtime starts swapping. Expect ~7.3 tok/s (estimated), with room for about 8,192 tokens of context.

Confidence: medium · Recommended quantization: Q6_K · Estimated speed: ~7.3 tok/s

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)

Gemma 2 Family on Mac mini (M4, 16 GB): memory by quantization

Quant	Memory needed	Fits 12 GB?	Max context	Est. speed	Download
F16	21.6 GB	✗ No	—	—	18 GB
Q8_0	13.2 GB	✗ No	—	—	9.6 GB
Q6_K	11 GB	✓ Yes	8K	~7.3 tok/s	7.4 GB
Q5_K_M	10 GB	✓ Yes	8K	~8.2 tok/s	6.4 GB
Q4_K_M	9.1 GB	✓ Yes	8K	~9.3 tok/s	5.4 GB
Q3_K_M	7.5 GB	✓ Yes	8K	~12.1 tok/s	3.8 GB
Q2_K	6.6 GB	✓ Yes	8K	~14.4 tok/s	3 GB

What to watch out for

Only ~1 GB of headroom at Q6_K: a longer context or a second application can push this into swapping.
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 computed from this model's published attention configuration.

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