Best local AI models for Apple M3 Pro

The largest models that fit fully

The 30 largest of the 149 models that fit; every smaller model in the catalog fits too. Best quant means the highest quality compression whose weights and 4k context both sit inside the memory.

Close, but only with CPU offload

These need more than the card holds at their smallest practical quant, so part of the model runs from system memory (figures assume 32 GB of it). They work, several times slower.

How to read this

The Apple M3 Pro chip with an 18 GB unified memory pool provides 12.6 GB of usable unified memory for local AI models. The remaining memory is reserved for the system and display. This memory limit determines the maximum size of the model you can run entirely on the graphics hardware. Running models within this 12.6 GB threshold ensures optimal execution speed.

The best quant column indicates the highest quality quantization level that fits within your usable memory. Quantization compresses model weights to save space. For example HunyuanImage 2.1 / 3.0 at 17B fits using the Q4_K_M quant which consumes 12.4 GB of memory. Ling-Coder-Lite at 16.8B also fits using Q4_K_M at 12.3 GB of memory. Smaller models like Gemma 3 12B or Mistral NeMo 12B can run at a higher quality Q6_K quant using 11.8 GB of memory.

When a model exceeds the 12.6 GB limit you must offload layers to the CPU and system RAM. This offloading allows you to run larger models but it significantly reduces processing speed. To use CPU offloading your Mac should have at least 32 GB of system RAM to handle the extra data transfer.

Several models require this CPU offload strategy. FLUX.1 dev at 12B needs 14.4 GB at FP8 or optimized settings which requires 16.4 GB of system RAM. Mistral Small 3.2 at 24B needs 17.6 GB at Q4_K_M and requires 19.6 GB of system RAM. Codestral 22B needs 16.1 GB at Q4_K_M and requires 18.1 GB of system RAM. Other offload options include CogVLM2 at 19B and Qwen-Image at 20B.

Memory consumption calculations assume a standard 4k context window. If you increase the context window to process longer documents the memory usage will grow. This extra memory demand may force you to use a lower quantization level or switch to a smaller model to prevent system slowdowns.