Best AI Models for Mac Studio M2 Ultra (64 GB)

Yes. The Mac Studio M2 Ultra (64 GB) with 64 GB unified memory can run Qwen3.8 27B (Q4_K_M, 17.9 GB, 30% of memory). It is also the table's best pick, at ~32.1 tok/s. 225 of the 338 featured models get a Fit of S (runs great) here. The largest featured model that fits at Q4 or better is GPT OSS 120B (116.8B at Q4_0). 235B-class models need around 150 GB at Q4_K_M, meaning they need CPU offloading or extra memory beyond what's available in 60.5 GB. Check the compatibility table above for the full list with Fit, VRAM usage and estimated speed. Models are checked against the 60.5 GB available to AI (that is, the 64 GB of RAM minus roughly 3.5 GB for macOS), while Apple Silicon's unified memory allows the GPU to draw from that same pool without copying data.

The Mac Studio M2 Ultra (64 GB) has 64 GB unified memory. After macOS reserves ~3.5 GB for the operating system, approximately 60.5 GB is available for AI models. Model rankings here use that 60.5 GB limit instead of the total 64 GB. Unlike discrete GPUs where VRAM is separate from system RAM, a unified-memory device shares one memory pool between the CPU and GPU — this means no data copying overhead, but you share memory with macOS and open apps.

With 64 GB unified memory and 800.0 GB/s bandwidth, the Mac Studio M2 Ultra (64 GB) is excellent for running local AI models. Models that run well include Gemma 4 26B A4B IT (25.8B at Q4_K_M, ~91.7 tok/s); Muse Glimmer 30B (29.8B at Q4_K_M, ~30.5 tok/s); Gemma 4 31B IT (31.3B at Q4_K_M, ~27.5 tok/s). This is a premium configuration, with room for large models at good quality and most smaller ones at maximum quality.

For the Mac Studio M2 Ultra (64 GB), the best pick right now is Qwen3.8 27B at ~32.1 tok/s. Also running well: Gemma 4 26B A4B IT (25.8B at Q4_K_M, ~91.7 tok/s); Muse Glimmer 30B (29.8B at Q4_K_M, ~30.5 tok/s); Gemma 4 31B IT (31.3B at Q4_K_M, ~27.5 tok/s). The best choice depends on your use case: coding assistants benefit from code-tuned models, general chat from instruction-tuned ones. The list is ranked by quality, recency and Fit.

With 800.0 GB/s memory bandwidth, the Mac Studio M2 Ultra (64 GB) achieves approximately 124 tok/s on a 7B model at Q4_K_M — that's very fast, well above conversational speed. A 14B model runs at ~62 tok/s. In the ranking, the top pick Qwen3.8 27B runs at ~32.1 tok/s. Apple Silicon achieves high efficiency (~70%) thanks to unified memory — there's no PCIe bottleneck between CPU and GPU.

Yes — once you download a model, it runs entirely on the Mac Studio M2 Ultra (64 GB) without internet. Applications like Ollama and LM Studio make it straightforward to download, manage, and run models locally. All your conversations stay private on your device with zero data sent to external servers. This is one of the key advantages of local AI: complete privacy, no API costs, and no rate limits.