The best local LLM for a MacBook Pro M5 Pro (64 GB) is Qwen 4.1 32B-A3B at 56 tok/s. With 64 GB of unified memory it runs 57 of the models we benchmark — from compact options up to 119B-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 M5 Pro). 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 | 56 tok/s | 46/50 |
| 2 | Qwen 4 | 32B | Apache 2.0 | 55 tok/s | 45/50 |
| 3 | Qwen 4 Preview 32B-A3B | 32B | Apache 2.0 | 52 tok/s | 42/50 |
| 4 | Qwen 4 Coder | 32B | Apache 2.0 | 26 tok/s est. | 44/50 |
| 5 | GLM 5.2 Air | 106B | MIT | 14 tok/s est. | 40/50 |
| 6 | Gemma 4 31B | 31B | Apache 2.0 | 11 tok/s est. | 40/50 |
| 7 | Qwen 3.6-35B-A3B | 35B | Apache 2.0 | 24 tok/s est. | 38/50 |
| 8 | Gemma 4 26B-A4B | 26B | Apache 2.0 | 35 tok/s | 35/50 |
| 9 | Llama 5 70B | 70B | Llama 5 | 8 tok/s est. | 38/50 |
| 10 | Phi-5 Large 28B | 28B | MIT | 17 tok/s est. | 36/50 |
| 11 | Mistral Voyage Pro 70B | 70B | Apache 2.0 | 7 tok/s est. | 36/50 |
| 12 | Mistral Medium 4 | 41B | Apache 2.0 | 22 tok/s est. | 34/50 |
Showing the top 12 of 57 models that fit in 64 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 M5 Pro benchmark page for exact settings.
The MacBook Pro M5 Pro (64 GB) comfortably runs 57 of the models we benchmark, led by Qwen 4.1 32B-A3B. Grab one and start running LLMs offline today:
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Qwen 4.1 32B-A3B (32B, Apache 2.0) is the best all-round pick at 56 tok/s on the M5 Pro. If you want maximum speed, Phi-5 Mini hits 95 tok/s; for maximum capability, Qwen 4 still fits in 64 GB.
About 57 of the 79 models in the LLMCheck leaderboard fit in 64 GB of unified memory, from compact models up to Mistral Small 4 (119B).
Yes. A 70B model in Q4 quantization needs roughly 40–44 GB of memory, which fits in 64 GB with headroom for context.
64 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.