Best local AI models for Apple M1

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 system with 16 GB of unified memory provides a maximum of 11.2 GB of usable memory for running local AI models. This memory pool is shared between the processor and the graphics engine. Running models within this 11.2 GB limit ensures that the entire model fits inside the fast unified memory. If a model exceeds this limit it cannot run entirely on the graphics hardware.

The quantization column shows the compression format used to fit these models into memory. Quantization reduces the size of the model weights. For example the Q4_K_M format uses four bit quantization to compress large models like the 15B StarCoder2 or the 14B Phi-4 down to 11 GB and 10.2 GB respectively. Smaller models like Llama 3.1 8B can run at a higher quality Q8_0 format using 10.7 GB of memory.

Models that are too large for the 11.2 GB unified memory limit require CPU offload. This process splits the model between the graphics memory and the system RAM. Running models like FLUX.1 dev or Qwen2.5 14B requires offloading parts of the model to the system RAM. This configuration assumes your system has 32 GB of system RAM to handle the extra memory load.

Offloading comes with a performance cost. Moving data between the system RAM and the processor is much slower than keeping the model inside the unified memory pool. For example the 16B DeepSeek-Coder-V2 needs 11.7 GB of memory at Q4_K_M quantization and requires 13.7 GB of system RAM. This split slows down the generation speed significantly compared to models that fit entirely within the 11.2 GB limit.

Users must also consider the memory cost of the context window. The memory figures listed for these models are calculated using a standard 4k context window. If you increase the context window to process longer documents the memory usage will grow. This extra memory requirement can push a model like Gemma 3 12B or Mistral NeMo 12B past the 11.2 GB limit and trigger slow CPU offloading.