Local AI suitability
The M4 Pro with 24–48 GB of unified memory (27 GB usable on the common 32 GB configuration) runs large models without a discrete GPU: memory is shared between CPU and GPU, so model size depends on how much RAM you configured, not on VRAM.
Very good for model capacity, moderate for speed. 27 GB of usable unified memory holds 32B models at Q4 with room for context — something few discrete consumer GPUs can do. Speed is limited by 273 GB/s bandwidth, roughly 4090-class divided by four, but far better than CPU-only Windows machines.
Recommended model sizes
- 7B (FP16)
- 14B (Q8)
- 32B (Q4)
Recommended quantizations
- Q4_K_M / Q5_K_M (GGUF)
- MLX 4-bit
Limitations
- 273 GB/s bandwidth caps tokens per second on large models
- You cannot upgrade memory later — buy the RAM you need up front
- Some CUDA-only tooling is unavailable; MLX and llama.cpp cover most needs
How it compares
Versus an RTX 4070 (12 GB): the M4 Pro trades speed for capacity — slower on 7B–14B, but able to hold 32B Q4 entirely in memory. Versus the M4 Max (36+ GB): more memory and bandwidth for bigger models.
Compatible models
Model pages below include per-quantization memory estimates and FAQs.
FAQ
How much unified memory do I need for local AI?
Plan for roughly 1.5–2× your target model size in Q4. For 32B models, 32 GB is the practical minimum; 48 GB or more opens the 70B class at low quantization.
Is Ollama fast on Apple Silicon?
Yes. Ollama uses Metal acceleration; 7B–8B Q4 models typically run 20–35 tok/s on an M4 Pro, and MLX-based stacks can be faster still.
Can Macs run models as well as NVIDIA GPUs?
For capacity per dollar of memory, Macs are strong — 32B Q4 on a laptop is hard to match. For raw speed, discrete NVIDIA cards with higher bandwidth still win.
Does the M4 Pro support fine-tuning?
Light fine-tuning of small models with MLX is possible, but serious training still favors CUDA. Inference is where Apple Silicon shines.