Every open model in our directory, ranked by how well it runs on Apple Mac Mini (M2 Pro, 2023) (32GB) at 4-bit. Grades weigh whether the model fits in memory and how fast it should run.
Apple Mac Mini (M2 Pro, 2023) has 32 GB of VRAM, enough to run 119 of the 169 open models we track at 4-bit. The largest that still fits well is minimax-m2.5 (230B), and the top-rated is LFM2.5-8B-A1B at about 55 tok/s.
119 of 169 models run comfortably on Apple Mac Mini (M2 Pro, 2023).
See full Apple Mac Mini (M2 Pro, 2023) specs and pricingThe top models this device can run at 4-bit, ranked by fit and speed.
| Model | Grade | Speed | VRAM |
|---|---|---|---|
| LFM2.5-8B-A1BLiquid AI | AA | 55.4 tok/s | 2.9 GB |
| Qwen3-30B-A3BAlibaba | AA | 29.9 tok/s | 5.4 GB |
| VibeThinker-3BWeiboAI | AA | 42.2 tok/s | 3.8 GB |
| Gemma 4 E2B ITGoogle | AA | 43.4 tok/s | 3.7 GB |
| PersonaPlex 7BNVIDIA |
LFM2.5-8B-A1B is the highest-rated model that runs on Apple Mac Mini (M2 Pro, 2023) at 4-bit, at about 55 tok/s. In total, Apple Mac Mini (M2 Pro, 2023) can run 119 of the 169 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.
| AA |
| 33.6 tok/s |
| 4.8 GB |
| Llama 2 7B ChatMeta | AA | 33.6 tok/s | 4.8 GB |
| Llama 3 8B InstructMeta | AA | 28.4 tok/s | 5.7 GB |
| Carnice-9b for Hermes agentkai-os | AA | 26.8 tok/s | 6.0 GB |
| North Mini CodeCohere | AA | 19.2 tok/s | 8.4 GB |
| Nemotron 3 Nano OmniNVIDIA | AA | 18.9 tok/s | 8.5 GB |
| Qwen3.6 35B-A3BAlibaba | AA | 18.9 tok/s | 8.5 GB |
| Qwen3.5-35B-A3BAlibaba | AA | 18.9 tok/s | 8.5 GB |
LFM2.5-8B-A1BLiquid AI | 8.3B(1.5B active) | AA | 55.4 tok/s | 2.9 GB | |
Qwen3-30B-A3BAlibaba | 30B(3B active) | AA | 29.9 tok/s | 5.4 GB | |
VibeThinker-3BWeiboAI | 3B | AA | 42.2 tok/s | 3.8 GB | |
Gemma 4 E2B ITGoogle | 2B | AA | 43.4 tok/s | 3.7 GB | |
PersonaPlex 7BNVIDIA | 7B | AA | 33.6 tok/s | 4.8 GB | |
Llama 2 7B ChatMeta | 7B | AA | 33.6 tok/s | 4.8 GB | |
| 8B | AA | 28.4 tok/s | 5.7 GB | ||
| 9B | AA | 26.8 tok/s | 6.0 GB | ||
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North Mini CodeCohere | 30B(3B active) | AA | 19.2 tok/s | 8.4 GB | |
Nemotron 3 Nano OmniNVIDIA | 30B(3B active) | AA | 18.9 tok/s | 8.5 GB | |
Qwen3.6 35B-A3BAlibaba | 35B(3B active) | AA | 18.9 tok/s | 8.5 GB | |
Qwen3.5-35B-A3BAlibaba | 35B(3B active) | AA | 18.9 tok/s | 8.5 GB | |
Mixtral 8x7B InstructMistral AI | 46.7B(12.9B active) | AA | 14.2 tok/s | 11.4 GB | |
DiffusionGemma 26B-A4BGoogle | 25.2B(3.8B active) | AA | 15.4 tok/s | 10.5 GB | |
Llama 2 13B ChatMeta | 13B | AA | 19.0 tok/s | 8.5 GB | |
Gemma 4 26B-A4B ITGoogle | 26B(4B active) | AA | 14.6 tok/s | 11.0 GB | |
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minimax-m2.5MiniMax | 230B(10B active) | BB | 7.1 tok/s | 22.7 GB | |
Mistral 7B InstructMistral AI | 7B | BB | 25.2 tok/s | 6.4 GB | |
Gemma 4 E4B ITGoogle | 4B | BB | 23.3 tok/s | 6.9 GB | |
Gemma 3 4B ITGoogle | 4B | BB | 23.3 tok/s | 6.9 GB | |
| 8B | BB | 12.1 tok/s | 13.3 GB | ||
Falcon 40B InstructTechnology Innovation Institute | 40B | BB | 6.6 tok/s | 24.4 GB | |
Qwen3.5-9BAlibaba | 9B | BB | 6.5 tok/s | 24.6 GB | |
Qwen3.5-122B-A10BAlibaba | 122B(10B active) | BB | 5.9 tok/s | 27.3 GB | |
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Gemma 4 12BGoogle | 12B | DD | 5.0 tok/s | 32.0 GB | |
Model Compatibility
Use the calculator to weigh this device against any other, model by model, with speed and fit scores.
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.
