Can I Run Granite 3.0 on Mac mini (M4, 16 GB)?
Autor: Jakub Rusinowski · Ostatnia aktualizacja: 21 października 2024
Yes
Yes — Granite 3.0 8B Instruct at Q8_0 needs about 10.6 GB of the 12 GB usable on Mac mini (M4, 16 GB) (~1.4 GB spare), at ~7 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~7 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) |
Granite 3.0 on Mac mini (M4, 16 GB): memory by quantization
| Quant | Memory needed | Fits 12 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 18.1 GB | ✗ No | — | — | 16 GB |
| Q8_0 | 10.6 GB | ✓ Yes | 16K | ~7 tok/s | 8.5 GB |
| Q6_K | 8.7 GB | ✓ Yes | 16K | ~8.9 tok/s | 6.6 GB |
| Q5_K_M | 7.8 GB | ✓ Yes | 32K | ~10.1 tok/s | 5.7 GB |
| Q4_K_M | 7 GB | ✓ Yes | 32K | ~11.5 tok/s | 4.8 GB |
| Q3_K_M | 5.6 GB | ✓ Yes | 32K | ~15.4 tok/s | 3.4 GB |
| Q2_K | 4.8 GB | ✓ Yes | 32K | ~18.8 tok/s | 2.6 GB |
What to watch out for
- Only ~1.4 GB of headroom at Q8_0: 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.
FAQ
Can I run Granite 3.0 on Mac mini (M4, 16 GB)?
Yes — Granite 3.0 8B Instruct at Q8_0 needs about 10.6 GB of the 12 GB usable on Mac mini (M4, 16 GB) (~1.4 GB spare), at ~7 tok/s (estimated), with room for about 16,384 tokens of context.
Which quantization of Granite 3.0 should I use on Mac mini (M4, 16 GB)?
Q8_0 — it needs about 10.6 GB of the 12 GB available, downloads as roughly 8.5 GB, and runs at an estimated 7 tokens/sec with up to 16K of context.
What limits Granite 3.0 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)
- IBM Granite 4.1 on Mac mini (M4, 16 GB)
- IBM Granite 4.2 on Mac mini (M4, 16 GB)
- InternLM 3 on Mac mini (M4, 16 GB)
- LFM2.5 on Mac mini (M4, 16 GB)
- Llama 3.1 Family on Mac mini (M4, 16 GB)
Granite 3.0 on GPUs
- Granite 3.0 on NVIDIA GeForce RTX 5070
- Granite 3.0 on NVIDIA GeForce RTX 5060 Ti 8GB
- Granite 3.0 on NVIDIA GeForce RTX 5060
- Granite 3.0 on NVIDIA GeForce RTX 4070 Ti
What This Model Is Good At
Model & Tools
← Can I Run It? | Granite 3.0 model page | Check your hardware