Running AI Models on Apple Silicon: M1 Through M4 Ultra
Apple Silicon's unified memory architecture gives Mac users a unique advantage for AI inference. Unlike discrete GPUs where VRAM is separate from system RAM, Apple Silicon shares all memory between CPU and GPU — meaning a 48GB M4 Pro can load models that would need a dedicated GPU with 48GB VRAM on Windows.
Chip Capabilities
| Chip | Max Memory | Usable for AI | Largest Model (Q4) |
|---|---|---|---|
| M1 | 16GB | ~10GB | 7B |
| M1 Pro | 32GB | ~21GB | 13B |
| M1 Max | 64GB | ~42GB | 34B |
| M2 | 24GB | ~16GB | 13B |
| M2 Pro | 32GB | ~21GB | 13B |
| M2 Max | 96GB | ~62GB | 70B |
| M3 | 24GB | ~16GB | 13B |
| M3 Max | 128GB | ~83GB | 70B |
| M4 Pro | 48GB | ~31GB | 32B |
| M4 Max | 128GB | ~83GB | 70B |
| M4 Ultra | 256GB | ~166GB | 405B |
Key Considerations
Speed vs Capacity: Apple Silicon can load very large models but generates tokens slower than equivalent NVIDIA GPUs. An M4 Max running a 70B model will be noticeably slower than an RTX 4090, but the 4090 can't even load that model with only 24GB VRAM.
MLX vs llama.cpp: MLX (Apple's framework) offers better optimization for Apple Silicon than llama.cpp. Consider using MLX-compatible models for the best performance on Mac.
Use our hardware checker to see exactly which models your Mac can run — we automatically detect your Apple Silicon chip and infer the correct memory configuration.