Can I Run Gemma 3 on Mac mini (M4, 16 GB)?
Autor: Jakub Rusinowski · Ostatnia aktualizacja: 12 marca 2025
Yes, but it is tight
Yes, but it is tight — Gemma 3 12B Instruct at Q6_K needs about 11.7 GB of the 12 GB usable on Mac mini (M4, 16 GB), leaving only ~0.3 GB before the runtime starts swapping. Expect ~6.1 tok/s (estimated), with room for about 8,192 tokens of context.
Confidence: medium · Recommended quantization: Q6_K · Estimated speed: ~6.1 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) |
Gemma 3 on Mac mini (M4, 16 GB): memory by quantization
| Quant | Memory needed | Fits 12 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 25.8 GB | ✗ No | — | — | 24 GB |
| Q8_0 | 14.6 GB | ✗ No | — | — | 12.8 GB |
| Q6_K | 11.7 GB | ✓ Yes | 8K | ~6.1 tok/s | 9.8 GB |
| Q5_K_M | 10.3 GB | ✓ Yes | 32K | ~6.9 tok/s | 8.5 GB |
| Q4_K_M | 9.1 GB | ✓ Yes | 32K | ~8 tok/s | 7.2 GB |
| Q3_K_M | 7 GB | ✓ Yes | 64K | ~10.8 tok/s | 5.1 GB |
| Q2_K | 5.8 GB | ✓ Yes | 64K | ~13.4 tok/s | 3.9 GB |
Which Gemma 3 sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Gemma 3 27B Instruct | 18.4 GB | ✗ Too large | — |
| Gemma 3 12B Instruct | 9.1 GB | ✓ Fits | ~8 tok/s |
| Gemma 3 4B Instruct | 3.6 GB | ✓ Fits | ~22.8 tok/s |
| Gemma 3 1B Instruct | 1.5 GB | ✓ Fits | ~78.2 tok/s |
What to watch out for
- Only ~0.3 GB of headroom at Q6_K: a longer context or a second application can push this into swapping.
- 1 larger variant of Gemma 3 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 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.
FAQ
Can I run Gemma 3 on Mac mini (M4, 16 GB)?
Yes, but it is tight — Gemma 3 12B Instruct at Q6_K needs about 11.7 GB of the 12 GB usable on Mac mini (M4, 16 GB), leaving only ~0.3 GB before the runtime starts swapping. Expect ~6.1 tok/s (estimated), with room for about 8,192 tokens of context.
Which quantization of Gemma 3 should I use on Mac mini (M4, 16 GB)?
Q6_K — it needs about 11.7 GB of the 12 GB available, downloads as roughly 9.8 GB, and runs at an estimated 6.1 tokens/sec with up to 8K of context.
What limits Gemma 3 on Mac mini (M4, 16 GB)?
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Which runtime should I use?
Ollama or LM Studio (Metal) — MLX for the fastest Apple-native throughput
Other Models on Mac mini (M4, 16 GB)
- Gemma 3n on Mac mini (M4, 16 GB)
- Gemma 4 on Mac mini (M4, 16 GB)
- GLM-4.7 / GLM-Z1 on Mac mini (M4, 16 GB)
- GLM-5 / GLM-5.1 on Mac mini (M4, 16 GB)
- GPT-OSS on Mac mini (M4, 16 GB)
Gemma 3 on GPUs
- Gemma 3 on NVIDIA GeForce RTX 5090
- Gemma 3 on NVIDIA GeForce RTX 5080
- Gemma 3 on NVIDIA GeForce RTX 5070 Ti
- Gemma 3 on NVIDIA GeForce RTX 5070