The best local LLM for a MacBook Pro M5 Max (64 GB) is Qwen 3.6-27B at 40 tok/s. With 64 GB of unified memory it runs 52 of the models we benchmark — from compact options up to 119B-class models. For everyday chat and coding, Qwen 3.6-27B is the sweet spot. Full ranking below.
Ranked by LLMCheck suitability (capability balanced against speed on the M5 Max). Click a model for its full benchmark and setup. All speeds are index estimates (memory-bandwidth model, cross-referenced with sourced benchmarks where available) — submit a real run →
| # | Model | Size | License | Speed | Capability |
|---|---|---|---|---|---|
| 1 | Qwen 3.6-27B | 27B | Apache 2.0 | 40 tok/s | 44/50 |
| 2 | Muse Glimmer 30B | 30B | Apache 2.0 | 27 tok/s | 42/50 |
| 3 | KAT-Coder-V2.5 | 35B | Apache 2.0 | 52 tok/s | 39/50 |
| 4 | Qwen 3.6-35B-A3B | 35B | Apache 2.0 | 48 tok/s | 38/50 |
| 5 | Nemotron 3.5 Lightning | 30B | OpenMDW | 60 tok/s | 36/50 |
| 6 | Gemma 4 31B | 31B | Apache 2.0 | 22 tok/s | 40/50 |
| 7 | Gemma 4 26B-A4B | 26B | Apache 2.0 | 50 tok/s | 35/50 |
| 8 | Laguna XS 2.1 | 33B | OpenMDW | 55 tok/s | 33/50 |
| 9 | GLM-4.7-Flash | 31B | MIT | 35 tok/s | 35/50 |
| 10 | Qwen3-Coder-Next | 80B | Apache 2.0 | 35 tok/s | 35/50 |
| 11 | Mistral Small 4 | 119B | Apache 2.0 | 42 tok/s | 34/50 |
| 12 | Bonsai 27B | 27B | Apache 2.0 | 40 tok/s | 33/50 |
Showing the top 12 of 52 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 3.6-27B on M5 Max benchmark page for exact settings.
The MacBook Pro M5 Max (64 GB) comfortably runs 52 of the models we benchmark, led by Qwen 3.6-27B. Grab one and start running LLMs offline today:
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Qwen 3.6-27B (27B, Apache 2.0) is the best all-round pick at 40 tok/s on the M5 Max. If you want maximum speed, Maple Preview 20B-A1B hits 280 tok/s; for maximum capability, Muse Glimmer 30B still fits in 64 GB.
About 52 of the 80 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.