Every open model in our directory, ranked by how well it runs on Apple Mac Studio (M1 Max, 2022) (64GB) at 4-bit. Grades weigh whether the model fits in memory and how fast it should run.
Apple Mac Studio (M1 Max, 2022) has 64 GB of VRAM, enough to run 135 of the 169 open models we track at 4-bit. The largest that still fits well is Kimi K2 Instruct (1000B), and the top-rated is Qwen3-30B-A3B at about 60 tok/s.
135 of 169 models run comfortably on Apple Mac Studio (M1 Max, 2022).
See full Apple Mac Studio (M1 Max, 2022) specs and pricingThe top models this device can run at 4-bit, ranked by fit and speed.
| Model | Grade | Speed | VRAM |
|---|---|---|---|
| Qwen3-30B-A3BAlibaba | SS | 59.8 tok/s | 5.4 GB |
| North Mini CodeCohere | SS | 38.4 tok/s | 8.4 GB |
| Nemotron 3 Nano OmniNVIDIA | SS | 37.7 tok/s | 8.5 GB |
| Qwen3.6 35B-A3BAlibaba | SS | 37.7 tok/s | 8.5 GB |
| Qwen3.5-35B-A3B |
Qwen3-30B-A3B is the highest-rated model that runs on Apple Mac Studio (M1 Max, 2022) at 4-bit, at about 60 tok/s. In total, Apple Mac Studio (M1 Max, 2022) can run 135 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.
| SS |
| 37.7 tok/s |
| 8.5 GB |
| Llama 2 13B ChatMeta | AA | 38.0 tok/s | 8.5 GB |
| Carnice-9b for Hermes agentkai-os | AA | 53.5 tok/s | 6.0 GB |
| Llama 3 8B InstructMeta | AA | 56.8 tok/s | 5.7 GB |
| LFM2.5-8B-A1BLiquid AI | AA | 110.8 tok/s | 2.9 GB |
| DiffusionGemma 26B-A4BGoogle | AA | 30.7 tok/s | 10.5 GB |
| Mixtral 8x7B InstructMistral AI | AA | 28.3 tok/s | 11.4 GB |
| Gemma 4 E4B ITGoogle | AA | 46.6 tok/s | 6.9 GB |
Qwen3-30B-A3BAlibaba | 30B(3B active) | SS | 59.8 tok/s | 5.4 GB | |
North Mini CodeCohere | 30B(3B active) | SS | 38.4 tok/s | 8.4 GB | |
Nemotron 3 Nano OmniNVIDIA | 30B(3B active) | SS | 37.7 tok/s | 8.5 GB | |
Qwen3.6 35B-A3BAlibaba | 35B(3B active) | SS | 37.7 tok/s | 8.5 GB | |
Qwen3.5-35B-A3BAlibaba | 35B(3B active) | SS | 37.7 tok/s | 8.5 GB | |
Llama 2 13B ChatMeta | 13B | AA | 38.0 tok/s | 8.5 GB | |
| 9B | AA | 53.5 tok/s | 6.0 GB | ||
| 8B | AA | 56.8 tok/s | 5.7 GB | ||
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LFM2.5-8B-A1BLiquid AI | 8.3B(1.5B active) | AA | 110.8 tok/s | 2.9 GB | |
DiffusionGemma 26B-A4BGoogle | 25.2B(3.8B active) | AA | 30.7 tok/s | 10.5 GB | |
Mixtral 8x7B InstructMistral AI | 46.7B(12.9B active) | AA | 28.3 tok/s | 11.4 GB | |
Gemma 4 E4B ITGoogle | 4B | AA | 46.6 tok/s | 6.9 GB | |
Gemma 3 4B ITGoogle | 4B | AA | 46.6 tok/s | 6.9 GB | |
Mistral 7B InstructMistral AI | 7B | AA | 50.4 tok/s | 6.4 GB | |
Gemma 4 26B-A4B ITGoogle | 26B(4B active) | AA | 29.2 tok/s | 11.0 GB | |
PersonaPlex 7BNVIDIA | 7B | AA | 67.2 tok/s | 4.8 GB | |
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Llama 2 7B ChatMeta | 7B | AA | 67.2 tok/s | 4.8 GB | |
Gemma 4 E2B ITGoogle | 2B | AA | 86.8 tok/s | 3.7 GB | |
VibeThinker-3BWeiboAI | 3B | AA | 84.5 tok/s | 3.8 GB | |
| 8B | AA | 24.2 tok/s | 13.3 GB | ||
minimax-m2.5MiniMax | 230B(10B active) | AA | 14.2 tok/s | 22.7 GB | |
Qwen3.5-122B-A10BAlibaba | 122B(10B active) | AA | 11.8 tok/s | 27.3 GB | |
Qwen3-235B-A22BAlibaba | 235B(22B active) | AA | 8.9 tok/s | 36.3 GB | |
Llama 2 70B ChatMeta | 70B | BB | 7.4 tok/s | 43.4 GB | |
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Mixtral 8x22B InstructMistral AI | 141B(39B active) | BB | 7.4 tok/s | 43.6 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.
