Every open model in our directory, ranked by how well it runs on Mac mini (M6) (16GB) at 4-bit. Grades weigh whether the model fits in memory and how fast it should run.
Mac mini (M6) has 16 GB of VRAM, enough to run 190 of the 261 open models we track at 4-bit. The largest that still fits well is AliceAI-Foundation-80B-A3B-Base (81.3B), and the top-rated is Holo4-35B-A3B at about 53 tok/s.
190 of 261 models run comfortably on Mac mini (M6).
See full Mac mini (M6) specs and pricingThe top models this device can run at 4-bit, ranked by fit and speed.
| Model | Grade | Speed | VRAM |
|---|---|---|---|
| Holo4-35B-A3BHcompany | SS | 52.7 tok/s | 2.3 GB |
| LFM2.5-8B-A1BLiquid AI | AA | 42.4 tok/s | 2.9 GB |
| Qwen3-30B-A3BAlibaba | AA | 22.9 tok/s | 5.4 GB |
| AliceAI-Foundation-80B-A3B-Baseyandex | AA | 14.4 tok/s | 8.5 GB |
| North Mini CodeCohere | AA | 14.7 tok/s | 8.4 GB |
| Nemotron 3 Nano OmniNVIDIA | AA | 14.4 tok/s | 8.5 GB |
| Qwen3.6 35B-A3BAlibaba | AA | 14.4 tok/s | 8.5 GB |
| Qwen3.5-35B-A3BAlibaba | AA | 14.4 tok/s | 8.5 GB |
| Llama 3 8B InstructMeta | AA | 21.7 tok/s | 5.7 GB |
| Gemma 4 E2B ITGoogle | AA | 33.2 tok/s | 3.7 GB |
| LensVLM-9BApple | AA | 20.5 tok/s | 6.0 GB |
| Carnice-9b for Hermes agentkai-os | AA | 20.5 tok/s | 6.0 GB |
Holo4-35B-A3BHcompany | 35B(3B active) | SS | 52.7 tok/s | 2.3 GB | |
LFM2.5-8B-A1BLiquid AI | 8.3B(1.5B active) | AA | 42.4 tok/s | 2.9 GB | |
Qwen3-30B-A3BAlibaba | 30B(3B active) | AA | 22.9 tok/s | 5.4 GB | |
| 81.29B(3B active) | AA | 14.4 tok/s | 8.5 GB | ||
North Mini CodeCohere | 30B(3B active) | AA | 14.7 tok/s | 8.4 GB | |
Nemotron 3 Nano OmniNVIDIA | 30B(3B active) | AA | 14.4 tok/s | 8.5 GB | |
Qwen3.6 35B-A3BAlibaba | 35B(3B active) | AA | 14.4 tok/s | 8.5 GB | |
Qwen3.5-35B-A3BAlibaba | 35B(3B active) | AA | 14.4 tok/s | 8.5 GB | |
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| 8B | AA | 21.7 tok/s | 5.7 GB | ||
Gemma 4 E2B ITGoogle | 2B | AA | 33.2 tok/s | 3.7 GB | |
LensVLM-9BApple | 9B | AA | 20.5 tok/s | 6.0 GB | |
| 9B | AA | 20.5 tok/s | 6.0 GB | ||
VibeThinker-3BWeiboAI | 3B | AA | 32.3 tok/s | 3.8 GB | |
Llama 2 13B ChatMeta | 13B | AA | 14.5 tok/s | 8.5 GB | |
Mixtral 8x7B InstructMistral AI | 46.7B(12.9B active) | AA | 10.8 tok/s | 11.4 GB | |
PersonaPlex 7BNVIDIA | 7B | AA | 25.7 tok/s | 4.8 GB | |
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Llama 2 7B ChatMeta | 7B | AA | 25.7 tok/s | 4.8 GB | |
DiffusionGemma 26B-A4BGoogle | 25.2B(3.8B active) | AA | 11.7 tok/s | 10.5 GB | |
Gemma 4 26B-A4B ITGoogle | 26B(4B active) | AA | 11.2 tok/s | 11.0 GB | |
Xing4.0-29B-A4BChina Telecom AI | 29B(4B active) | AA | 11.0 tok/s | 11.2 GB | |
MiniCPM5-2Bopenbmb | 2.52B | BB | 26.8 tok/s | 4.6 GB | |
Mistral 7B InstructMistral AI | 7B | BB | 19.3 tok/s | 6.4 GB | |
Gemma 4 E4B ITGoogle | 4B | BB | 17.8 tok/s | 6.9 GB | |
Gemma 3 4B ITGoogle | 4B | BB | 17.8 tok/s | 6.9 GB | |
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LFM2.5-2.6BLiquid AI | 2.69B | BB | 25.2 tok/s | 4.9 GB | |

Model Compatibility
Use the calculator to weigh this device against any other, model by model, with speed and fit scores.
Straight answers to the questions we hear most often.
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Holo4-35B-A3B is the highest-rated model that runs on Mac mini (M6) at 4-bit, at about 53 tok/s. In total, Mac mini (M6) can run 190 of the 261 open models we track.
We size each model at 4-bit (Q4_K_M), add the KV cache for its context length and a fixed runtime overhead, then compare that to this device VRAM. Each model is graded on how comfortably it fits and how fast it should run on this card.
It is an estimate of decode speed in tokens per second at 4-bit, based on this device memory bandwidth and each model size. Real speed depends on your runtime, batch size, and context length, so use it to compare models, not as a hard promise.
Lower-bit quantization shrinks a model so more of them fit. These grades already assume 4-bit, which is the common local default. Going below 4-bit can squeeze a larger model in at some quality cost; going above needs more VRAM and may not fit.
Mixture-of-experts models only activate a fraction of their parameters at a time, so their runtime memory is closer to the active size than the headline size. We score fit on the active parameters, which is why some very large models still fit.