Apple Silicon’s unified memory architecture makes Macs the best consumer hardware for running AI models locally. The M4 generation (M4, M4 Pro, M4 Max, M4 Ultra) pushes this further with faster Neural Engine and higher memory bandwidth.
Here’s what runs well on each M4 variant.
Models by M4 variant
M4 MacBook Air (16GB unified memory)
| Model | Size | Speed | Best for |
|---|---|---|---|
| Qwen3 8B | 5 GB | ~35 tok/s | General coding (recommended default) |
| Phi-4 3.8B | 2.5 GB | ~50 tok/s | Fast autocomplete |
| Gemma 4 9B | 6 GB | ~30 tok/s | Multilingual tasks |
| DeepSeek R1 8B | 5 GB | ~35 tok/s | Reasoning |
With 16GB, you can comfortably run one 8B model with room for your IDE and browser. Don’t try 14B+ models — they’ll work but your system will swap and become unusable.
M4 Pro MacBook Pro (24-48GB)
| Model | Size | Speed | Best for |
|---|---|---|---|
| DeepSeek R1 14B | 9 GB | ~30 tok/s | Best reasoning under 16GB |
| Qwen 3.6-27B (Q4) | 16 GB | ~20 tok/s | Best quality coding |
| Codestral | 13 GB | ~25 tok/s | Code-specific tasks |
| Falcon H1R 7B | 5 GB | ~40 tok/s | Math/reasoning |
With 36-48GB, you can run a 27B model and still have room for everything else. This is the sweet spot for local AI development.
M4 Max (64-128GB)
| Model | Size | Speed | Best for |
|---|---|---|---|
| Llama 4 Scout (Q4) | ~25 GB | ~25 tok/s | Long context (10M tokens) |
| Qwen 3.5 72B (Q4) | ~40 GB | ~15 tok/s | Near-frontier quality |
| Mistral Large 2 (Q4) | ~40 GB | ~15 tok/s | Multilingual |
| Two models simultaneously | Varies | Varies | Model routing |
With 128GB, you can run 70B+ models at reasonable speed. This is where local AI starts competing with cloud APIs on quality.
M4 Ultra Mac Studio (192GB+)
| Model | Size | Speed | Best for |
|---|---|---|---|
| GLM-5.1 (quantized) | ~150 GB | ~5-10 tok/s | Frontier quality, fully local |
| Llama 4 Maverick (Q4) | ~60 GB | ~12 tok/s | Best open generalist |
| Multiple 27B models | ~50 GB total | ~20 tok/s each | Specialized routing |
At 192GB, you can run models that normally require cloud GPUs. A Mac Studio with M4 Ultra is a legitimate alternative to renting cloud compute.
Ollama vs MLX
Two options for running models on Mac:
| Ollama | MLX | |
|---|---|---|
| Setup | brew install ollama | pip install mlx-lm |
| Speed | Good | 10-20% faster on Apple Silicon |
| Model format | GGUF | MLX format |
| Model library | Huge (official + community) | Growing |
| API | REST API (OpenAI-compatible) | Python library |
| Best for | Most users, tool integration | Maximum performance |
Recommendation: Start with Ollama for ease of use and tool compatibility (Aider, Continue.dev, Open WebUI). Switch to MLX if you need maximum speed.
Recommended setups
Budget setup (M4 Air 16GB) — $0/month
ollama pull qwen3:8b
aider --model ollama/qwen3:8bGood for: learning, side projects, routine coding. Not enough for complex architecture work.
Developer setup (M4 Pro 36GB) — $0/month
ollama pull qwen3.5:27b-q4_k_m
ollama pull qwen3:8b # Fast model for autocomplete
# Aider with the big model
aider --model ollama/qwen3.5:27b
# Continue.dev with the small model for autocompleteGood for: daily professional coding. Quality approaches Claude Sonnet for most tasks.
Power setup (M4 Max 128GB) — $0/month
ollama pull qwen3.5:72b-q4_k_m # Near-frontier quality
ollama pull deepseek-r1:14b # Reasoning specialist
ollama pull qwen3:8b # Fast autocomplete
# Route between models
aider --model ollama/qwen3.5:72b # Complex tasks
aider --model ollama/deepseek-r1:14b # DebuggingGood for: replacing cloud APIs entirely. Quality matches GPT-5.4 Mini on most tasks.
Performance tips
- Close Chrome — it uses GPU memory that Ollama needs
- Use Q4_K_M quantization — best speed/quality balance on Apple Silicon
- Enable Flash Attention —
OLLAMA_FLASH_ATTENTION=1 ollama serve - Reduce context if not needed —
--num-ctx 2048for simple tasks - Use MLX for maximum speed — 10-20% faster than GGUF on M-series
FAQ
What AI models can I run on a Mac M4?
With the base M4 (16GB), you can run models up to 9B parameters. The M4 Pro (36GB) handles 27B models comfortably. The M4 Max (128GB) can run 72B models and even some frontier-class models at lower quantization.
Is the M4 Mac good for local AI?
Yes, Apple Silicon’s unified memory architecture makes Macs excellent for local AI. The M4 generation is 15-20% faster than M3 for inference, and the memory bandwidth handles large models efficiently without a discrete GPU.
Should I use Ollama or MLX on M4?
Start with Ollama for its simplicity and broad model support. Switch to MLX for specific models where you need 10-20% faster inference. MLX models are available pre-quantized on HuggingFace under the mlx-community organization.
Related: Best AI Models for Mac · Ollama Complete Guide · How Much VRAM for AI Models · Best AI Models Under 16GB VRAM · Best GPU for AI Locally · Best Cloud GPU Providers · Ollama Slow Inference Fix