The best local LLM for a Mac Studio M5 Max (128 GB) is DeepSeek V4 Flash at 39 tok/s. With 128 GB of unified memory it runs 59 of the models we benchmark — from compact options up to 284B-class models. For everyday chat and coding, DeepSeek V4 Flash 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 | DeepSeek V4 Flash | 284B | MIT | 39 tok/s | 47/50 |
| 2 | Qwen3.8-27B | 27.8B | Apache 2.0 | 30 tok/s est. | 46/50 |
| 3 | Inkling-Small | 276B | Apache 2.0 | 22 tok/s | 46/50 |
| 4 | Qwen3-235B-A22B | 235B | Apache 2.0 | 18 tok/s | 46/50 |
| 5 | Qwen 3.6-27B | 27B | Apache 2.0 | 30 tok/s | 44/50 |
| 6 | Laguna S 2.1 | 118B | OpenMDW | 26 tok/s | 43/50 |
| 7 | Muse Glimmer 30B | 30B | Apache 2.0 | 27 tok/s | 42/50 |
| 8 | KAT-Coder-V2.5 | 35B | Apache 2.0 | 52 tok/s | 39/50 |
| 9 | Qwen 3.6-35B-A3B | 35B | Apache 2.0 | 48 tok/s | 38/50 |
| 10 | Nemotron 3.5 Lightning | 30B | OpenMDW | 60 tok/s | 36/50 |
| 11 | Gemma 4 31B | 31B | Apache 2.0 | 26 tok/s | 40/50 |
| 12 | Gemma 4 26B-A4B | 26B | Apache 2.0 | 50 tok/s | 35/50 |
Showing the top 12 of 59 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 DeepSeek V4 Flash on M5 Max benchmark page for exact settings.
The Mac Studio M5 Max (128 GB) comfortably runs 59 of the models we benchmark, led by DeepSeek V4 Flash. Grab one and start running LLMs offline today:
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DeepSeek V4 Flash (284B, MIT) is the best all-round pick at 39 tok/s on the M5 Max. If you want maximum speed, Maple Preview 20B-A1B hits 288 tok/s; for maximum capability, Qwen3.8-27B still fits in 128 GB.
About 59 of the 81 models in the LLMCheck leaderboard fit in 128 GB of unified memory, from compact models up to DeepSeek V4 Flash (284B).
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.