The best local LLM for a MacBook Pro M3 Max (128 GB) is Qwen 4.1 32B-A3B at 48 tok/s. With 128 GB of unified memory it runs 60 of the models we benchmark — from compact options up to 235B-class models. For everyday chat and coding, Qwen 4.1 32B-A3B is the sweet spot. Full ranking below.
Ranked by LLMCheck suitability (capability balanced against real speed on the M3 Max). Click a model for its full benchmark and setup. Speeds marked est. are scaled from measured runs by memory bandwidth.
| # | Model | Size | License | Speed | Capability |
|---|---|---|---|---|---|
| 1 | Qwen 4.1 32B-A3B | 32B | Apache 2.0 | 48 tok/s | 46/50 |
| 2 | Qwen 4 | 32B | Apache 2.0 | 47 tok/s | 45/50 |
| 3 | Qwen 4 Coder | 32B | Apache 2.0 | 45 tok/s | 44/50 |
| 4 | Qwen 4 Preview 32B-A3B | 32B | Apache 2.0 | 45 tok/s | 42/50 |
| 5 | Qwen3-235B-A22B | 235B | Apache 2.0 | 10 tok/s est. | 46/50 |
| 6 | GLM 5.2 Air | 106B | MIT | 20 tok/s est. | 40/50 |
| 7 | Qwen 3.6-35B-A3B | 35B | Apache 2.0 | 35 tok/s est. | 38/50 |
| 8 | Gemma 4 31B | 31B | Apache 2.0 | 16 tok/s est. | 40/50 |
| 9 | Llama 5 70B | 70B | Llama 5 | 12 tok/s est. | 38/50 |
| 10 | Phi-5 Large 28B | 28B | MIT | 26 tok/s | 36/50 |
| 11 | Gemma 4 26B-A4B | 26B | Apache 2.0 | 32 tok/s est. | 35/50 |
| 12 | Mistral Medium 4 | 41B | Apache 2.0 | 30 tok/s | 34/50 |
Showing the top 12 of 60 models that fit in 128 GB. See the full leaderboard or all benchmarks.
The fastest way to get started is Ollama. Install it, then pull the top pick for your Mac:
Prefer a GUI? LM Studio gives you a one-click download and chat window. For step-by-step help see our Ollama install guide, or open the Qwen 4.1 32B-A3B on M3 Max benchmark page for exact settings.
The MacBook Pro M3 Max (128 GB) tops out at Qwen3-235B-A22B. Newer Apple Silicon with more unified memory runs larger, smarter models much faster:
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Qwen 4.1 32B-A3B (32B, Apache 2.0) is the best all-round pick at 48 tok/s on the M3 Max. If you want maximum speed, Gemma 4 E2B hits 103 tok/s; for maximum capability, Qwen3-235B-A22B still fits in 128 GB.
About 60 of the 79 models in the LLMCheck leaderboard fit in 128 GB of unified memory, from compact models up to Qwen3-235B-A22B (235B).
Yes. A 70B model in Q4 quantization needs roughly 40–44 GB of memory, which fits in 128 GB with headroom for context.
128 GB is plenty for local AI — you can run capable 30B–70B-class models. Because Apple Silicon uses unified memory, that figure is both your system RAM and your VRAM.