Local AI suitability
The M4 Max with 36–128 GB of unified memory is the strongest local-AI Mac. High bandwidth for Apple Silicon (546 GB/s) plus large memory means 70B-class models at low quantization and 32B models at comfortable quantization run entirely in memory.
Excellent capacity, good speed. With 36 GB you can host 32B Q4/Q5 comfortably and 70B Q2/Q3 with tight context — a capability class otherwise reserved for dual-GPU desktops. Power efficiency and silence remain unmatched.
Recommended model sizes
- 14B (FP16)
- 32B (Q5/Q6)
- 70B (Q2/Q3)
Recommended quantizations
- Q4_K_M / Q5_K_M (GGUF)
- MLX 4-bit
Limitations
- Premium price versus building a dual-GPU desktop
- Some CUDA-exclusive tooling is unavailable
- 70B-class models still run slower than on multi-NVIDIA setups
How it compares
Versus dual RTX 4090s: the Mac is simpler, quieter and holds big models in one memory pool; the NVIDIA path is faster and cheaper per token. Versus the M4 Pro: more bandwidth and memory for 32B+ ambitions.
Compatible models
Model pages below include per-quantization memory estimates and FAQs.
FAQ
Can an M4 Max run a 70B model?
Yes, at Q2/Q3 quantization with a modest context, at roughly 8–15 tok/s depending on the exact memory configuration. It is one of the few single-machine options for this class.
How much memory is ideal for 32B models?
36 GB handles 32B Q4/Q5 comfortably; 48 GB adds long-context headroom and headroom for vision models.
M4 Max vs RTX 4090 desktop?
For 32B and below, both are excellent; the 4090 is faster per token, the Mac is silent, portable and holds bigger models in unified memory. Choose by workflow and noise tolerance.
Do MLX models differ from GGUF?
MLX is Apple's own format optimized for Apple Silicon; GGUF works everywhere including Macs. Many models are available in both — MLX builds are often a bit faster on Apple hardware.