Mac Model Selection Guide — Local AI on macOS

Model selection on Apple Silicon is not just about capability—it is about fitting within your RAM and achieving acceptable throughput. The two variables are model size (parameters × quantization) and hardware (chip generation and RAM).

A practical selection table for Apple Silicon:

Model	Parameters	Quantization	RAM Needed	Target Chip
Phi-3.5-mini	3.8B	Q4_K_M	2.4 GB	M1+
Llama 3.2 1B	1B	Q4_K_M	0.7 GB	M1+
Llama 3.2 3B	3B	Q4_K_M	1.9 GB	M1+
Llama 3.2 7B	7B	Q4_K_M	4.9 GB	M1 Pro+
Mistral 7B	7B	Q4_K_M	4.9 GB	M1 Pro+
Llama 3.1 8B	8B	Q4_K_M	5.4 GB	M2 Pro+
Qwen 2.5 14B	14B	Q4_K_M	8.8 GB	M2 Pro 24GB+
Llama 3.1 70B	70B	Q4_K_M	43 GB	M2 Ultra 128GB

The 8 GB machines (M1/M2 base) are viable for 1B–3B models. They can load a 7B model but the memory headroom is too tight for comfortable use at longer context lengths.

The 16 GB machines (M1/M2 Pro base, M3 base) handle 7B models well at short contexts and 3B models at long contexts. This is the sweet spot for most developers.

The 24 GB+ machines (M3 Pro, M3 Max, M4 Pro) run 13B–14B models comfortably and can push into 30B territory with careful quantization selection.

Ollama model search:

# Search available models
ollama list
ollama search llama          # requires internet, lists from registry

# Pull the model
ollama pull llama3.2:3b

MLX model selection via the community hub:

# Find model size and recommended hardware
from mlx_lm import ls
models = ls()
# Returns list of model names with sizes
# mlx-community/Llama-3.2-3B-Instruct-4bit ~1.8GB