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.
Update (April 24, 2026): DeepSeek V4 Flash may run locally on Mac when GGUF quantizations become available. See how to run V4 locally.
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 |
|---|---|---|
| Qwen 3.6-27B (Q4) | ~22 tok/s | 77.2% SWE-bench — best coding model at this tier |
| 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. Qwen 3.6-27B is the new top pick — a 27B dense model that scores 77.2% on SWE-bench Verified (beating the 397B flagship) and runs on just 22GB VRAM. Apache 2.0 licensed. The Qwen3.5-9B running at 28-35 tok/s remains a great lighter alternative.
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.
Related
- Best Self-Hosted AI Models in 2026
- Best GPU for Running AI Models Locally in 2026
- Best Cloud GPU Providers in 2026
- How to Run Qwen 3.6 Locally
- How Much VRAM Do You Need for AI?
FAQ
What’s the best AI model to run on a Mac in 2026?
Qwen 3.5 27B is the best all-around model for Macs with 32GB+ unified memory. It fits comfortably in Q4 quantization and delivers quality approaching cloud APIs for most coding and writing tasks.
How much RAM do I need to run AI on a Mac?
16GB is the minimum for useful local AI (limited to 4-9B models). 32GB is the sweet spot for running 27B models. 48GB+ unlocks the best open-source coding models like Qwen 2.5 Coder 32B at full quality.
Is Ollama or MLX better for Mac?
Ollama is easier to set up and has broader model support. MLX can be 10-20% faster for supported models since it’s optimized specifically for Apple Silicon. Try both — Ollama for convenience, MLX when you need maximum speed.
Related: How to Choose an AI Coding Agent · AI Coding Tools Pricing · Best AI Models for Mac