The best local LLM for a Mac mini M5 Pro (48 GB) is Qwen3.8-27B at 15 tok/s. With 48 GB of unified memory it runs 49 of the models we benchmark — from compact options up to 80B-class models. For everyday chat and coding, Qwen3.8-27B is the sweet spot. Full ranking below.
Ranked by LLMCheck suitability (capability balanced against speed on the M5 Pro). 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 | Qwen3.8-27B | 27.8B | Apache 2.0 | 15 tok/s est. | 46/50 |
| 2 | Qwen 3.6-27B | 27B | Apache 2.0 | 14 tok/s | 44/50 |
| 3 | Muse Glimmer 30B | 30B | Apache 2.0 | 14 tok/s est. | 42/50 |
| 4 | KAT-Coder-V2.5 | 35B | Apache 2.0 | 27 tok/s est. | 39/50 |
| 5 | Gemma 4 31B | 31B | Apache 2.0 | 13 tok/s est. | 40/50 |
| 6 | Qwen 3.6-35B-A3B | 35B | Apache 2.0 | 27 tok/s est. | 38/50 |
| 7 | Nemotron 3.5 Lightning | 30B | OpenMDW | 31 tok/s est. | 36/50 |
| 8 | Gemma 4 26B-A4B | 26B | Apache 2.0 | 35 tok/s | 35/50 |
| 9 | Qwen3-Coder-Next | 80B | Apache 2.0 | 18 tok/s est. | 35/50 |
| 10 | GLM-4.7-Flash | 31B | MIT | 13 tok/s est. | 35/50 |
| 11 | Laguna XS 2.1 | 33B | OpenMDW | 28 tok/s est. | 33/50 |
| 12 | Maple Preview 20B-A1B | 20B | MIT | 281 tok/s | 20/50 |
Showing the top 12 of 49 models that fit in 48 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 Qwen3.8-27B on M5 Pro benchmark page for exact settings.
The Mac mini M5 Pro (48 GB) comfortably runs 49 of the models we benchmark, led by Qwen3.8-27B. Grab one and start running LLMs offline today:
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Qwen3.8-27B (27.8B, Apache 2.0) is the best all-round pick at 15 tok/s on the M5 Pro. If you want maximum speed, Maple Preview 20B-A1B hits 281 tok/s; for maximum capability, Qwen 3.6-27B still fits in 48 GB.
About 49 of the 81 models in the LLMCheck leaderboard fit in 48 GB of unified memory, from compact models up to Qwen3-Coder-Next (80B).
Yes. A 70B model in Q4 quantization needs roughly 40–44 GB of memory, which fits in 48 GB with headroom for context.
48 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.