Mac Studio M2 Ultra 128GB

Yes! Mac Studio M2 Ultra 128GB (128 GB unified memory) can run 137 models at full speed and 338 total. Top picks: Qwen3-Coder-Next (score: 93/100), Qwen 3.5 122B A10B (score: 93/100), Qwen3-Coder 30B A3B Instruct (score: 92/100). See the full tiered compatibility list above.

Mac Studio M2 Ultra 128GB has 128 GB of unified memory shared between CPU and GPU, all available for AI model inference. Unlike discrete GPUs with separate VRAM, unified memory means models can use the full 128 GB without data transfer overhead.

Not exactly. Unified memory is excellent for making larger models fit on Apple Silicon, because the CPU and GPU share one memory pool. But it is still not identical to dedicated VRAM on a high-bandwidth discrete GPU. For local AI, unified memory often wins on flexibility and capacity, while discrete GPUs can still win on raw tokens per second once a model fits comfortably.

Yes, Mac Studio M2 Ultra 128GB is excellent for running LLMs locally. With 128 GB unified memory and Metal acceleration, it handles 338 models with top scores above 80/100.

Because fit and speed are not the same thing. Mac Studio M2 Ultra 128GB can often fit larger models thanks to unified memory, but a smaller NVIDIA GPU with fast dedicated VRAM and mature CUDA kernels can still deliver higher decode throughput once the model fits. In practice, Apple Silicon is excellent for flexible local AI on one machine, while CUDA often stays ahead for the easiest setup and highest raw inference speed.

We recommend using llama.cpp on Mac Studio M2 Ultra 128GB. Install it with a single command, then pull your preferred model. llama.cpp supports Metal acceleration out of the box on Apple Silicon.

For coding on Mac Studio M2 Ultra 128GB, we recommend Qwen3-Coder-Next. It achieves 31.3 tokens per second with 256K context window using 65.0 GB of unified memory. Qwen3-Coder-Next is a specialized fit for Coding. It is a recent-generation family, which helps on current local SOTA workloads. It fits natively with comfortable headroom. Context coverage stays within the requested workload envelope. Known distribution channels: huggingface, ollama, lm-studio.

Yes, Mac Studio M2 Ultra 128GB with 128 GB unified memory can run Flux.1 Dev at FP16. Use ComfyUI or Draw Things for the best experience on macOS.

Mac Studio M2 Ultra 128GB (128 GB unified memory) supports various AI generation tasks. For image generation, SDXL and Stable Diffusion 3.5 run well with Metal acceleration. Flux.1 Dev also runs natively. For video, LTX Video 2.3 can generate short clips.

Mac Studio M2 Ultra 128GB is excellent for AI image generation. With 128 GB unified memory and Metal GPU acceleration, it runs all major diffusion models including Flux.1, SDXL, and SD 3.5.

There are 4 upgrade path(s) from Mac Studio M2 Ultra 128GB: NVIDIA H200 141GB (141 GB), Mac Studio M3 Ultra 256GB (256 GB). Upgrading would unlock larger models like Qwen 3.5 397B A17B and Kimi K2.5 and faster inference.

Yes, Mac Studio M2 Ultra 128GB with 128 GB unified memory can run Qwen 3.5 122B-A10B (MoE) at Q4 and Qwen 3.5 27B at full FP16 precision. The MoE architecture activates only 10B parameters per token, giving excellent inference speed despite the large model size. Use MLX or Ollama for best results.

The best local LLMs for Mac Studio M2 Ultra 128GB (128 GB) are: Qwen3-Coder-Next (93/100, 31 tok/s), Qwen3-Coder 30B A3B Instruct (92/100, 70 tok/s), Qwen 3.6 35B A3B (92/100, 59 tok/s). These models fit natively in unified memory with room for context. For coding, try the top coding pick above. For general chat, the highest-scored model gives the best Apple Silicon local AI experience.

Mac Studio M2 Ultra 128GB achieves 22-31 tok/s for well-fitted models with 800 GB/s memory bandwidth. Token generation speed on Apple Silicon is primarily limited by memory bandwidth and fit. Comfortable reading speed is about 6-8 tokens per second, so most natively-fitting models will feel responsive for interactive chat. MLX generally delivers 10-20% better performance than llama.cpp on newer Apple Silicon chips.