Local AI suitability
The M3 Pro with 18 GB of unified memory sits in the middle of the Apple lineup: enough capacity for 7B–14B models at practical quantization, with bandwidth modest compared with discrete GPUs.
Good capacity, moderate speed. 14B Q4 models fit comfortably with context headroom; 32B models are out of reach at 18 GB. The 150 GB/s bandwidth keeps tokens-per-second respectable on 7B-class models.
Recommended model sizes
- 7B (Q6/Q8)
- 14B (Q4/Q5)
Recommended quantizations
- Q4_K_M (GGUF)
- MLX 4-bit
Limitations
- 150 GB/s bandwidth limits large-model throughput
- 18 GB cannot host 32B-class models
- Memory is not upgradeable after purchase
How it compares
Versus the M4 Pro (27 GB usable): the newer chip adds memory and bandwidth. If local AI is a primary use, stepping up to a 24–32 GB configuration is usually worth it.
Compatible models
Model pages below include per-quantization memory estimates and FAQs.
FAQ
Can an M3 Pro run a 14B model?
Yes, at Q4/Q5 within 18 GB of unified memory, at roughly 15–25 tok/s depending on runtime and context.
Can it run a 32B model?
Not practically — 32B Q4 needs roughly 20+ GB including context. With heavy CPU offload it may crawl, but it is not a recommended use.
Is MLX faster than llama.cpp on Mac?
For many models, MLX is currently faster on Apple Silicon. llama.cpp remains the most compatible choice across quantization formats.
Is 18 GB enough for coding models?
Yes — Qwen2.5 Coder 7B at Q6/Q8 fits with a good context window, making it a solid local coding assistant machine.