Apple Silicon is one of the best platforms for running AI models locally. The unified memory architecture means your GPU can use all system RAM — a 32GB Mac has 32GB of effective VRAM. No other consumer platform offers this.
Here are the best models for each Mac tier. For a broader look at all platforms, see our best GPU for AI locally guide.
Why Macs are great for local AI
- Unified memory = VRAM. A 32GB Mac Mini has more effective AI memory than an RTX 4080 (16GB VRAM).
- Silent. No GPU fans screaming. Run AI models in meetings without anyone noticing.
- Efficient. Apple Silicon uses a fraction of the power of discrete GPUs. Your electricity bill doesn’t change.
- MLX framework. Apple’s own ML framework is optimized specifically for Apple Silicon, often faster than llama.cpp for supported models.
Best models by Mac
Mac Mini M4 (16GB) — $599
| Model | Speed | Quality |
|---|---|---|
| Qwen3.5-4B | ~40 tok/s | Good for simple tasks |
| DeepSeek R1 7B | ~30 tok/s | Reasoning on a budget |
| Qwen3.5-0.8B | ~80 tok/s | Instant responses |
16GB is tight. Stick to models under 9B parameters. The Qwen3.5-4B is the best balance of quality and speed at this tier.
Mac Mini M4 (32GB) — $1,149
| Model | Speed | Quality |
|---|---|---|
| Qwen3.5-9B | ~28-35 tok/s | Beats GPT-OSS-120B |
| MiMo-V2-Flash (Q4) | ~25 tok/s | Strong coding |
| DeepSeek Coder V2 Lite | ~30 tok/s | Budget coding assistant |
| Qwen3.5-35B-A3B | ~35 tok/s | 35B knowledge, 3B speed |
This is the sweet spot. The Mac Mini M4 32GB is the best value for local AI in 2026. The Qwen3.5-9B running at 28-35 tok/s is genuinely useful for daily coding assistance.
Mac Mini M4 Pro (48GB) — $1,799
| Model | Speed | Quality |
|---|---|---|
| Qwen3.5-27B (Q4) | ~20 tok/s | Strong all-rounder |
| Qwen 2.5 Coder 32B (Q4) | ~18 tok/s | Best open-source coding |
| Codestral 25.01 | ~25 tok/s | Best autocomplete |
| Llama 4 Scout (Q4) | ~22 tok/s | 10M context capability |
48GB opens up the 27-32B model range. Qwen 2.5 Coder 32B at this tier gives you GPT-4o-level coding for free.
Mac Studio M4 Ultra (192GB) — ~$6,000
| Model | Speed | Quality |
|---|---|---|
| Qwen3.5-122B-A10B | ~25 tok/s | Near-frontier |
| DeepSeek V3 (Q4) | ~15 tok/s | Full 671B model |
| Qwen3.5-397B (Q4) | ~8-10 tok/s | Frontier-class |
| Llama 4 Maverick (full) | ~20 tok/s | 1M context, multimodal |
The Ultra is the only consumer device that can run full frontier-class models. DeepSeek V3 at 15 tok/s is usable for coding and analysis. Qwen3.5-397B at 8-10 tok/s is slower but delivers frontier quality.
Setup with Ollama
# Install
brew install ollama
# Run any model
ollama run qwen3.5:9bOllama automatically uses Apple Silicon’s GPU acceleration. No configuration needed.
Setup with MLX (Apple-optimized)
MLX is Apple’s machine learning framework, optimized specifically for Apple Silicon. It can be faster than Ollama for supported models.
pip install mlx-lm
# Run a model
mlx_lm.generate --model mlx-community/Qwen3.5-9B-4bit \
--prompt "Write a Python web scraper"MLX models are available on HuggingFace under the mlx-community organization. They’re pre-quantized for Apple Silicon.
Performance tips
- Close other apps. Every GB of RAM used by other apps is a GB less for your model.
- Use Q4 quantization. Best balance of quality and speed on Mac.
- Start with smaller context. 4K-8K context uses less memory than 32K. Increase only if needed.
- MLX vs Ollama: Try both. MLX is sometimes faster for specific models, Ollama is easier to use.
- Activity Monitor: Watch memory pressure. If it’s yellow or red, your model is too large.
The recommendation
| Budget | Buy | Run |
|---|---|---|
| $599 | Mac Mini M4 16GB | Qwen3.5-4B |
| $1,149 | Mac Mini M4 32GB | Qwen3.5-9B |
| $1,799 | Mac Mini M4 Pro 48GB | Qwen 2.5 Coder 32B |
| $6,000 | Mac Studio M4 Ultra 192GB | DeepSeek V3, Qwen 397B |
The Mac Mini M4 32GB at $1,149 is the best entry point. It runs models that genuinely replace paid API access for daily development work.