Best local AI models for Apple M3 Max

The largest models that fit fully

The 30 largest of the 183 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 Max chip with a 36 GB unified memory pool provides 28.8 GB of usable memory allocated for graphics and local AI model weights. This specific limit determines which large language models can run entirely on the high speed graphics hardware. Keeping the entire model within this 28.8 GB boundary ensures fast generation speeds and low latency during local execution.

The quantization column indicates the compression level applied to the model weights. For this system configuration the best quant represents the highest quality version of a model that still fits inside the available memory. A Q4_K_M quantization uses four bits per weight and offers a balanced trade off. Higher quantizations like Q5_K_M or Q6_K and Q8_0 provide better precision but require more memory space.

Several large models fit completely within the memory limit of the system. The Seed-OSS 36B model fits at Q4_K_M quantization using 26.4 GB of memory. The Qwen3.6-35B-A3B and Command R (35B) models both run at Q4_K_M using 25.6 GB of memory. You can also run the Yi 1.5 9B / 34B, Granite Code 3B to 34B, LLaVA 1.5 / 1.6 (7B to 34B), and Ovis 2 models at Q4_K_M using 24.9 GB of memory.

Models with slightly higher quantization levels are also compatible. The DeepSeek-Coder 1.3B / 6.7B / 33B and WizardCoder 33B models run at Q5_K_M using 28.1 GB of memory. The OTel 2.0 LLM 31B IT fits at Q4_K_M using 27.5 GB of memory. The Qwen3 8B / 14B / 32B, Qwen3.5 (dense variants), Aya Expanse 8B / 32B, Granite 4.0 Small/Tiny, and Qwen2.5-Coder 0.5B to 32B models all run at Q5_K_M using 27.3 GB of memory.

Other options include the Qwen3-30B-A3B and Qwen3-Coder 30B-A3B models at Q5_K_M using 25.6 GB of memory. The Gemma 3 27B, Gemma 3 4B/12B/27B (vision), and Wan 2.2 / 2.5 models fit at Q6_K using 26.6 GB of memory. The Gemma 4 26B-A4B and Gemma 4 (all sizes) models run at Q6_K using 25.6 GB of memory. The Aria model fits at Q6_K using 24.6 GB of memory. The Mistral Small 3.2, Magistral Small, and Devstral Small 1.1 models run at Q6_K using 23.6 GB of memory. The Solar Pro and Codestral 22B models run at Q8_0 using 28 GB of memory. The gpt-oss-20b and Reka Flash 3 models run at Q8_0 using 26.7 GB of memory.

Running models that exceed the 28.8 GB limit requires CPU offload to the system RAM. This process allows you to run larger models like Mixtral 8x7B which needs 34.4 GB at Q4_K_M and 36.4 GB of system RAM. The Llama 3.1 Nemotron 51B needs 37.3 GB at Q4_K_M and 39.3 GB of system RAM. The Jamba 1.5 Mini / Large needs 38.1 GB at Q4_K_M and 40.1 GB of system RAM. Offloading to system RAM significantly reduces processing speed because the system memory bus is slower than the unified memory on the chip.

Users must also consider the memory required for the context window. The memory figures listed here are calculated using a standard 4k context window. Expanding the context window to process longer documents or chat histories will increase memory consumption. If the context window grows too large the model may exceed the available memory and trigger slow CPU offloading.