Best local AI models for Apple M3

The largest models that fit fully

The 30 largest of the 144 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 chip with a 16 GB unified pool allocates up to 11.2 GB of usable unified memory for local AI models. This limit determines the size of the neural networks you can run directly on your hardware. When a model fits entirely within this 11.2 GB boundary, it runs at maximum speed because the processor accesses the weights directly from the fast unified memory pool.

To fit larger models into this memory space, developers use quantization. The quant column shows the specific compression level used to reduce the size of the model weights. For example, the 15B parameter Apriel-1.5-15B-Thinker and StarCoder2 15B models fit within 11 GB using the Q4_K_M quant. Models like Gemma 4 12B, Mistral NeMo 12B, Pixtral 12B, and FLUX.1 schnell use the Q5_K_M quant to fit within 10.2 GB of memory.

Smaller models can run with less compression because they require less memory overall. The Llama 3.1 8B model runs at a high quality Q8_0 quant while using 10.7 GB of memory. Similarly, the Open-Sora 2.0 model uses the Q6_K quant at 10.8 GB, while the Nemotron Nano 9B, GLM-4 9B, Yi-Coder 9B, and GLM-4V-9B models use the Q6_K quant at 8.9 GB.

When a model exceeds the 11.2 GB unified memory limit, you must offload the remaining weights to the system RAM. This process requires a system with 32 GB of RAM to handle the extra load. Offloading allows you to run larger models, but it reduces execution speed because data must transfer between different memory pools. For example, Qwen2.5 14B needs 11.6 GB at Q4_K_M and 13.6 GB of system RAM, while DeepSeek-Coder-V2 16B needs 11.7 GB at Q4_K_M and 13.7 GB of system RAM.

Other offload cases include HunyuanImage 3.0 which needs 12.4 GB at Q4_K_M and 14.4 GB of system RAM. The CogVLM2 model needs 13.9 GB at Q4_K_M and 15.9 GB of system RAM. The largest offload models like Qwen-Image require 14.6 GB at Q4_K_M and 16.6 GB of system RAM, while gpt-oss-20b requires 15.4 GB at Q4_K_M and 17.4 GB of system RAM.

All memory calculations assume a standard 4k context window. If you increase the context window to process longer documents, the memory usage will rise. This extra memory consumption might force you to use a more compressed quant or offload parts of the model to system RAM to prevent out of memory errors.