Best local AI models for Apple M1 Pro

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 M1 Pro chip with a 16 GB unified memory pool provides 11.2 GB of usable memory for running local AI models. This memory is shared between the processor and the graphics engine. Because the operating system and active applications also require memory, this 11.2 GB limit is the maximum space available for loading model weights. Keeping your model size within this boundary prevents system slowdowns.

To fit larger models into the available memory, you must use quantized versions. The quantization column shows the best format that fits your hardware. For example, the 15B models like Apriel-1.5-15B-Thinker and StarCoder2 15B fit using the Q4_K_M quantization which uses 11 GB. Models with 14B parameters such as Phi-3 Medium, Phi-4, Phi-4-reasoning / -plus, Wan 2.2 T2I, Wan 2.1, and SkyReels V2 also fit at Q4_K_M using 10.2 GB.

Smaller models can run with higher precision quantizations for better accuracy. The 13B models like Vicuna 13B, HunyuanVideo, HunyuanVideo-Avatar, LTX-Video / LTX-2, and FramePack use 11.1 GB at the Q5_K_M quantization. Models with 12B parameters including Gemma 3 12B, Gemma 4 12B, Mistral NeMo 12B, Pixtral 12B, FLUX.1 schnell, FLUX.1 Kontext dev, and FLUX.1 Krea dev run at Q5_K_M using 10.2 GB. Open-Sora 2.0 at 11B uses 10.8 GB at Q6_K.

Very compact models can run at even higher quality settings. Mochi 1 uses 9.8 GB at Q6_K. The 9B models like Gemma 2 9B, Nemotron Nano 9B, GLM-4 9B / GLM-4.5-Air, Yi-Coder 9B, GLM-4-9B-Chat / CodeGeeX4, and GLM-4V-9B / GLM-4.1V-Thinking use between 8.9 GB and 10.3 GB at Q6_K. Chroma uses 8.8 GB at Q6_K. Llama 3.1 8B runs at the high quality Q8_0 quantization using 10.7 GB.

If you want to run models that exceed the 11.2 GB limit, you must offload parts of the workload to your system RAM. This process requires a system with 32 GB of RAM. Offloading allows you to run FLUX.1 dev which needs 14.4 GB at FP8 and 16.4 GB of system RAM. It also lets you run Qwen2.5 14B, DeepSeek-Coder-V2 16B, Kimi-VL A3B, Ling-Coder-Lite, HunyuanImage 2.1 / 3.0, CogVLM2, Qwen-Image, Qwen-Image-Edit, and gpt-oss-20b. Offloading makes these larger models run much slower because system RAM has lower bandwidth than unified memory.

When planning your local AI setup, remember the 4k context caveat. The memory figures listed here are calculated using a standard 4k context window. If you increase the context length to process longer documents or chat histories, the memory usage will rise. This extra memory demand can push a model past the 11.2 GB limit and cause performance issues.