Can I Run Qwen3.8 on Mac mini (M4, 16 GB)?
Written by Jakub Rusinowski · Last updated September 11, 2026
Yes
Yes — Qwen3.8 27B at Q2_K needs about 10.6 GB of the 12 GB usable on Mac mini (M4, 16 GB) (~1.4 GB spare), at ~6.7 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: high · Recommended quantization: Q2_K · Estimated speed: ~6.7 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) |
Qwen3.8 on Mac mini (M4, 16 GB): memory by quantization
| Quant | Memory needed | Fits 12 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 57.1 GB | ✗ No | — | — | 55.6 GB |
| Q8_0 | 31 GB | ✗ No | — | — | 29.5 GB |
| Q6_K | 24.3 GB | ✗ No | — | — | 22.8 GB |
| Q5_K_M | 21.2 GB | ✗ No | — | — | 19.7 GB |
| Q4_K_M | 18.3 GB | ✗ No | — | — | 16.8 GB |
| Q3_K_M | 13.3 GB | ✗ No | — | — | 11.8 GB |
| Q2_K | 10.6 GB | ✓ Yes | 16K | ~6.7 tok/s | 9.1 GB |
Which Qwen3.8 sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Qwen3.8-Max | 1457.5 GB | ✗ Too large | — |
| Qwen3.8-Flash-Next | 109.7 GB | ✗ Too large | — |
| Qwen3.8 27B | 18.3 GB | ✗ Too large | — |
What to watch out for
- Only ~1.4 GB of headroom at Q2_K: a longer context or a second application can push this into swapping.
- Q2_K is the only quantization that fits, and it is a heavily degraded one — expect noticeably worse output than the same model at Q4_K_M. A smaller model at Q4 is usually the better trade.
- 3 larger variants of Qwen3.8 do 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 Qwen3.8 on Mac mini (M4, 16 GB)?
Yes — Qwen3.8 27B at Q2_K needs about 10.6 GB of the 12 GB usable on Mac mini (M4, 16 GB) (~1.4 GB spare), at ~6.7 tok/s (estimated), with room for about 16,384 tokens of context.
Which quantization of Qwen3.8 should I use on Mac mini (M4, 16 GB)?
Q2_K — it needs about 10.6 GB of the 12 GB available, downloads as roughly 9.1 GB, and runs at an estimated 6.7 tokens/sec with up to 16K of context.
What limits Qwen3.8 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)
- Qwen3-Coder on Mac mini (M4, 16 GB)
- SmolLM2 on Mac mini (M4, 16 GB)
- SmolLM3 on Mac mini (M4, 16 GB)
- StarCoder 2 on Mac mini (M4, 16 GB)
- Aya 3B (Tiny Aya) on Mac mini (M4, 16 GB)
Qwen3.8 on GPUs
- Qwen3.8 on NVIDIA GeForce RTX 5090
- Qwen3.8 on NVIDIA GeForce RTX 5080
- Qwen3.8 on NVIDIA GeForce RTX 5070 Ti
- Qwen3.8 on NVIDIA GeForce RTX 5070