AI model recommendations for Mac Studio M2 with up to 192GB RAM. Ideal for the largest models. This configuration provides optimal performance for local AI models.
With up to 192GB unified memory, the Mac Studio M2 Ultra can load multiple large models simultaneously or run the largest available models. Improved memory bandwidth delivers faster inference across all model sizes.
Based on our analysis, 8 out of 8 recommended models run excellently on this configuration. The sweet spot for Mac Studio with Apple M2 is 70B+ parameter models with Q4_K_M quantization, which provides the best trade-off between quality and inference speed.
Qwen / 35B / Q4_K_M / ~20 GB
Best for: Reasoning, Coding, Agent scenarios·Pop: 90/100
Perf: ~14.0 tok/s · first token ~2.0s
Best for reasoning, coding, agent scenarios. Strong fit for 64 GB RAM with balanced speed and quality.
ollama run qwen3.5:35b-a3b-instruct-q4_K_M
Qwen / 27B / Q4_K_M / ~16 GB
Best for: Chat, Coding, Complex reasoning·Pop: 82/100
Perf: ~17.7 tok/s · first token ~1.0s
Best for chat, coding, complex reasoning. Strong fit for 64 GB RAM with balanced speed and quality.
ollama run qwen3.5:27b-instruct-q4_K_M
Qwen / 35B / Q4_K_M / ~22 GB
Best for: Production, Long context, Agent scenarios·Pop: 85/100
Perf: ~14.0 tok/s · first token ~2.0s
Best for production, long context, agent scenarios. Strong fit for 64 GB RAM with balanced speed and quality.
ollama run qwen3.5:flash-q4_K_M
LFM2 / 24B / Q4_K_M / ~14 GB
Best for: Local AI agents, privacy-first tool calling, MCP workflows·Pop: 80/100
Perf: ~19.7 tok/s · first token ~1.0s
Best for local ai agents, privacy-first tool calling, mcp workflows. Strong fit for 64 GB RAM with balanced speed and quality.
ollama run liquidai/lfm2:24b-a2b-instruct-q4_K_M
Qwen / 30B / Q4_K_M / ~22 GB
Best for: Quality, Coding·Pop: 78/100
Perf: ~16.1 tok/s · first token ~1.9s
Best for quality, coding. Strong fit for 64 GB RAM with balanced speed and quality.
ollama run qwen3:30b-q4_K_M
Gemma / 27B / Q4_K_M / ~21 GB
Best for: Quality, Coding·Pop: 71/100
Perf: ~17.7 tok/s · first token ~1.0s
Best for quality, coding. Strong fit for 64 GB RAM with balanced speed and quality.
ollama run gemma3:27b-instruct-q4_K_M
Gemma / 27B / Q4_K_M / ~21 GB
Best for: Quality, Coding·Pop: 67/100
Perf: ~17.7 tok/s · first token ~1.0s
Best for quality, coding. Strong fit for 64 GB RAM with balanced speed and quality.
ollama run gemma2:27b-instruct-q4_K_M
Mistral / 46.7B / Q4_K_M / ~30 GB
Best for: Coding, Quality·Pop: 72/100
Perf: ~10.8 tok/s · first token ~2.2s
Best for coding, quality. Strong fit for 64 GB RAM with balanced speed and quality.
ollama run mixtral:8x7b-instruct-q4_K_M
With 64GB RAM and the Apple M2 chip, we recommend Qwen3.5 35B-A3B Instruct for the best balance of speed and quality. The Apple M2 handles 70B+ parameter models well.
Mac Studio with Apple M2 supports GB configurations. For most AI workloads, 64GB provides good headroom. A 7B model typically needs 4-5GB of free RAM, while 14B models need 8-10GB.
Apple M2 on Mac Studio achieves approximately 14 tokens per second with optimized models. With up to 192GB unified memory, the Mac Studio M2 Ultra can load multiple large models simultaneously or run the largest available models. Improved memory bandwidth delivers faster inference across all model sizes.
Yes, Ollama runs natively on Apple Silicon including Apple M2. You can install it in minutes and run models like Qwen3.5 35B-A3B Instruct locally. Our wizard recommends the best models based on your exact Apple M2 configuration and available RAM.
Use our interactive wizard to test different RAM configurations and priorities for your specific Apple M2 setup.
Open ModelFit Wizard →