Best local AI models for Apple M4

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 M4 chip with 16 GB of unified memory provides a maximum of 11.2 GB of usable memory for local AI models. The remaining memory is reserved for the macOS operating system and system display. When running models locally, the entire active model must fit within this 11.2 GB limit to run at full speed on the integrated graphics hardware.

The quantization column shows the best format to balance size and accuracy. Quantization compresses model weights to use fewer bits per parameter. For example, Llama 3.1 8B runs at Q8_0 quantization using 10.7 GB of memory. Smaller models like Gemma 2 9B use Q6_K quantization at 10.3 GB of memory. Larger models like Apriel-1.5-15B-Thinker or StarCoder2 15B require Q4_K_M quantization to fit into 11 GB of memory.

Models like Phi-4, Phi-4-reasoning, Wan 2.2 T2I, and SkyReels V2 use Q4_K_M quantization and consume 10.2 GB of memory. Video models like HunyuanVideo, HunyuanVideo-Avatar, and LTX-Video use Q5_K_M quantization and consume 11.1 GB of memory. Image generation models like FLUX.1 schnell and FLUX.1 Krea dev also run within the limit using Q5_K_M quantization at 10.2 GB of memory.

If a model exceeds the 11.2 GB limit, you must use CPU offloading. This process moves parts of the model to the system RAM but slows down processing speed. For CPU offload cases, we assume a 32 GB system RAM configuration. Under this setup, FLUX.1 dev needs 14.4 GB at FP8 and requires 16.4 GB of system RAM. Qwen2.5 14B needs 11.6 GB at Q4_K_M and requires 13.6 GB of system RAM. DeepSeek-Coder-V2 16B needs 11.7 GB at Q4_K_M and requires 13.7 GB of system RAM.

Other offload examples include HunyuanImage 2.1 which needs 12.4 GB at Q4_K_M and requires 14.4 GB of system RAM. CogVLM2 needs 13.9 GB at Q4_K_M and requires 15.9 GB of system RAM. Qwen-Image needs 14.6 GB at Q4_K_M and requires 16.6 GB of system RAM. The largest offload model listed is gpt-oss-20b which needs 15.4 GB at Q4_K_M and requires 17.4 GB of system RAM.

Users must monitor the context window size during execution. The memory numbers listed here are calculated using a baseline 4k context window. If you increase the context window beyond 4k tokens, the memory usage will rise. This extra memory consumption can push a model past the 11.2 GB limit and trigger slow CPU offloading.