[Official FAQ] Share your experience or follow-up questions below.

Yes, Macs can be a very practical way to run local models, mainly because of unified memory: the CPU and GPU share one memory pool, so a Mac can hold larger models than a typical consumer GPU with the same price tag.

• Memory size sets the largest model you can run. By default macOS lets the GPU use only a portion of total memory, so as a rough guide keep the model file plus context cache under about 60–70% of RAM. Advanced users can raise the limit, but do your research first.
• Speed depends on memory bandwidth. Higher-tier chips (Pro, Max, Ultra within a generation) generally generate tokens faster than base chips.
• Long prompts are slower to process than on a high-end NVIDIA GPU, so time-to-first-token can be noticeable with big contexts.
• MoE models fit Macs well, because only part of the weights is active per token, as long as everything fits in memory.

Software: Ollama, LM Studio and llama.cpp all use Metal. Apple's MLX framework (for example the mlx-lm package) is optimized for Apple Silicon and can be faster in some cases, so it is worth comparing.

Approximate memory guidance:

• 16 GB: small to mid models (around 7B–8B at 4-bit) with modest context.
• 32 GB: more comfortable for 14B–30B class quantized models.
• 64 GB and up: larger models and longer context.

Since memory cannot be upgraded later, buy more than you think you need. If you already own a Mac, share your chip, RAM, model and speed to help others calibrate expectations.