Run AI on Mac: Apple Silicon AI Guide (2026)

I switched from a PC with a dedicated NVIDIA GPU to a MacBook Pro for my daily work. Friends who run local AI told me I’d regret it—“Macs can’t do AI properly,” they said. No CUDA, no party.

They were wrong.

Apple Silicon handles local AI differently than traditional GPUs, and in some ways, it handles it better. I’ve been running Llama 3, Mistral, and other models on my M3 Max for months. Here’s everything you need to know about running AI on a Mac in 2026.

Why Apple Silicon Is Different for AI

Apple took a different approach with their M-series chips: unified memory architecture. Instead of separate RAM for the CPU and separate VRAM for the GPU (like traditional PC setups), Apple combines everything into a single pool of high-speed memory.

This sounds like a minor technical detail, but it fundamentally changes how AI works on Mac.

On a traditional PC:

Your GPU has 12-24GB VRAM (expensive)
AI models must fit in that VRAM
If they don’t fit, performance tanks

On Apple Silicon:

CPU and GPU share all system RAM
An M4 Max with 128GB unified memory gives AI access to 128GB
That’s more memory than any consumer GPU offers

The catch? Apple’s GPU cores are slower for raw AI inference than a comparable NVIDIA GPU. But when your model fits in VRAM on NVIDIA and fits in unified memory on Mac, the Mac often wins on memory-constrained tasks.

I’ve run 70B parameter models on my Mac that would require multiple expensive GPUs on a PC. The inference is slower per token, but it actually runs—which is more than I can say for most consumer PCs.

Unified Memory Explained: Why It Matters

Let me break down why unified memory is such a big deal for AI workloads.

When you run a Large Language Model, the entire model’s weights need to be in memory that the processor can access quickly. On a PC, that means GPU VRAM. An RTX 4080 has 16GB; an RTX 4090 has 24GB. That’s your ceiling.

On Apple Silicon, the “VRAM” is your entire unified memory pool. Buy a Mac with 64GB RAM, and your AI models have access to most of that 64GB. No artificial ceiling from GPU memory limits.

The practical impact:

Mac Configuration	Unified Memory	Runnable Models
M2/M3/M4 base	8-16GB	7B models (quantized)
M3/M4 Pro	18-36GB	13B models comfortably
M3/M4 Max	36-128GB	34B-70B models depending on config
M3/M4 Ultra	128-192GB	70B+ models at high quality

The memory bandwidth on Apple Silicon is also excellent. The M4 Max hits 546 GB/s—not as fast as an RTX 4090’s 1+ TB/s, but fast enough for fluid token generation with most models.

Here’s my honest take after using both: for models that fit in 24GB (the typical GPU ceiling), a high-end NVIDIA GPU is faster. For models that need 32GB+, Apple Silicon is often your only practical option without spending thousands on professional GPUs.

Which Mac for Local AI?

Let me give you straightforward recommendations based on what you want to run.

Entry Level: M3/M4 MacBook Air (16GB)

Surprisingly capable for 7B models. You can run Llama 3 8B, Mistral 7B, and similar models at 4-bit quantization with reasonable performance. It’s not fast, but it works.

Best for: Learning and experimentation. Light AI usage. Occasional coding assistance.

Limitation: Don’t expect to run anything larger than 7B-8B models. The memory is just too tight.

Mid Range: M3/M4 Pro MacBook Pro (36GB)

This is where Mac AI gets genuinely useful. 36GB unified memory lets you run 13B models comfortably and even some 34B models with quantization.

Best for: Regular local AI use. Quality code completion. Running multiple models for comparison.

The sweet spot for most developers. I’d recommend this tier unless you have specific needs for larger models.

High End: M3/M4 Max MacBook Pro (64-128GB)

Now we’re talking. 64GB handles 34B models beautifully. 128GB can run 70B models—the real frontier-adjacent stuff.

Best for: Heavy local AI development. Running large models for production work. Research and experimentation with the latest open-source models.

Serious investment, serious capability. If local AI is core to your workflow, this pays for itself in freedom from cloud API costs and rate limits.

The Beast: M2/M3 Ultra Mac Studio (128-192GB)

Maximum memory, maximum capability. You can run essentially any open-source model released today.

Best for: Teams, research labs, or individuals who need no-compromise local AI.

Probably overkill for individuals. The price premium over Max chips is steep for the incremental benefit.

Setting Up Ollama on Mac

Ollama is the easiest way to run LLMs locally on Mac. Here’s the complete setup.

Installation

# Install with Homebrew
brew install ollama

# Or download directly from ollama.com

The installation is simple—Ollama handles all the optimization for Apple Silicon automatically.

Running Your First Model

# Start Ollama (runs in background)
ollama serve

# Pull and run Llama 3.2 8B
ollama run llama3.2

# Or try Mistral
ollama run mistral

That’s it. Ollama detects your Mac’s unified memory and optimizes automatically. On my M3 Max with 48GB, it chooses appropriate quantization and batch settings without any manual configuration.

Model Recommendations for Mac

Based on memory, here’s what I’d recommend:

16GB Unified Memory:

llama3.2 - Latest 8B, excellent quality
mistral - Fast, capable 7B
codellama:7b - For code generation

36GB Unified Memory:

llama3.2:13b - Better reasoning
mixtral - Mixture of experts, strong performance
deepseek-coder:6.7b - Solid for code

64GB+ Unified Memory:

llama3.1:70b - Near-frontier quality
qwen2:72b - Excellent multilingual
codestral - Advanced code generation

Useful Ollama Commands

# List installed models
ollama list

# Pull a specific model
ollama pull llama3.2:13b

# Remove a model
ollama rm model-name

# Run with specific context length
ollama run llama3.2 --context-length 8192

# Check running models
ollama ps

For a complete Ollama guide including API usage and advanced configuration, check out our comprehensive Ollama tutorial.

LM Studio: A Visual Alternative

If you prefer a GUI, LM Studio is excellent on Mac. It’s a desktop app that makes downloading and running models point-and-click simple.

Why LM Studio?

Visual interface for browsing and downloading models
Easy model switching
Built-in chat interface
Local server mode for API access
No command line required

Setup

Download from lmstudio.ai
Launch the app
Search for a model in the Discover tab
Click download
Switch to Chat tab and start talking

LM Studio automatically detects your Mac’s memory and filters model recommendations accordingly. It’s a great option if you’re new to local AI or prefer graphical interfaces.

My honest take: I started with LM Studio, then moved to Ollama for development work. LM Studio is better for casual use and exploration; Ollama is better for scripting and integrating into workflows. Many people use both.

MLX: Apple’s AI Framework

Apple developed MLX specifically for Apple Silicon. It’s a machine learning framework optimized for the unified memory architecture.

Why MLX Matters

MLX is designed from the ground up for efficient unified memory use. Models running on MLX often outperform the same models running through other frameworks on Mac.

The MLX community has released optimized versions of popular models. If you’re doing serious AI work on Mac, exploring MLX is worth your time.

Getting Started with MLX

# Install MLX
pip install mlx mlx-lm

# Run a model
python -m mlx_lm.generate --model mlx-community/Llama-3.2-3B-Instruct-4bit --prompt "Hello"

The mlx-community hub on Hugging Face has pre-converted models ready for MLX. These often load faster and run more efficiently than standard GGUF files on Mac.

When to Use MLX vs Ollama

Use Ollama if:

You want simplicity
You need CLI access and scripting
You’re running common models

Use MLX if:

You’re developing ML applications
You want maximum performance from Apple Silicon
You need fine-grained control

For most people, Ollama is the right choice. MLX is for when you outgrow Ollama’s abstractions.

Model Recommendations by Memory

Here’s my updated guide for January 2026, organized by your Mac’s unified memory.

8GB Unified Memory (M2/M3/M4 Base)

You’re memory-constrained but not helpless.

Recommended models:

Llama 3.2 3B - Small but capable
Phi-3 mini - Microsoft’s efficient model
TinyLlama - For experiments

Reality check: You’ll run heavily quantized models with limited context windows. Useful for learning, but don’t expect production-quality output.

16GB Unified Memory

The minimum I’d recommend for regular local AI use.

Recommended models:

Llama 3.2 8B - Best general quality
Mistral 7B - Fast, good reasoning
CodeLlama 7B - Decent for code

Can run 8B models at reasonable quality. 13B is tight but possible with aggressive quantization.

32-36GB Unified Memory (Pro)

This is where Mac AI gets genuinely useful.

Recommended models:

Llama 3.2 13B - Much better than 8B
DeepSeek Coder 33B (quantized) - Excellent for code
Mixtral 8x7B - Strong reasoning

Comfortable zone for serious work. Most developers find this sufficient.

64GB Unified Memory (Max)

Running with room to spare.

Recommended models:

Llama 3.1 70B (4-bit) - Near-frontier quality
Qwen2 72B - Excellent overall
Codestral 22B - Premium code generation

You can run the big models. This is serious local AI territory.

128GB+ Unified Memory (Max/Ultra)

No practical limits on current open-source models.

Recommended models:

Llama 3.1 70B (8-bit) - Higher quality quantization
Command R+ (104B) - Advanced reasoning
Mixtral 8x22B - Massive mixture of experts

Run anything at high quality. Future-proofed for years.

Performance Expectations: Being Realistic

Let me set honest expectations for Mac AI performance.

Token generation speed (approximate, varies by model and quantization):

Mac	8B Model	13B Model	70B Model
M3 Air (16GB)	~25 tok/s	N/A	N/A
M3 Pro (36GB)	~35 tok/s	~25 tok/s	N/A
M3 Max (64GB)	~45 tok/s	~35 tok/s	~8 tok/s
M3 Ultra (192GB)	~50 tok/s	~40 tok/s	~15 tok/s

For reference, human reading speed is roughly 3-4 words per second. A 25 tok/s generation rate feels plenty fluid for interactive use.

Compared to NVIDIA GPUs: An RTX 4090 will outpace a Mac on raw tokens per second for models that fit in its 24GB VRAM. But the Mac can run larger models that the 4090 simply can’t handle.

The honest tradeoff: If performance is your only priority and you’re running 7B-13B models, a gaming PC with an RTX 4070 Ti or better will be faster. If running larger models matters, Mac with high memory is more capable.

Frequently Asked Questions

Can I run ChatGPT locally on Mac?

Not ChatGPT specifically (that’s OpenAI’s cloud service), but you can run similar open-source models. Llama 3.2 and Mistral offer quality approaching GPT-3.5/4 for many tasks.

Is my M1 Mac good enough for AI?

Yes! M1 is still fully supported. The M3/M4 are faster, but M1 Macs with sufficient memory run local AI fine. Check your unified memory—that’s what matters most.

Do I need to install CUDA on Mac?

No. CUDA is NVIDIA technology and doesn’t exist on Mac. Ollama, LM Studio, and MLX all use Apple’s Metal framework for GPU acceleration automatically.

Should I buy more memory or faster chip?

Memory, almost always. The difference between M3 Pro and M3 Max is less important than the difference between 36GB and 64GB for AI workloads. Prioritize memory.

Can I run Stable Diffusion on Mac?

Yes! Stable Diffusion runs well on Apple Silicon via apps like DiffusionBee and Automatic1111 (with MPS support). Apple’s Core ML optimizations make image generation quite fast.

Your Mac Is Ready for AI

If you already have an Apple Silicon Mac with 16GB+ memory, you’re ready to start running AI locally today. Install Ollama, download a model, and start experimenting. No expensive GPU purchase required.

For model recommendations and more detailed Ollama usage, check out our complete Ollama guide and our overview of the best open source LLMs to run locally.

The gap between Mac and PC for AI is much smaller than the discourse suggests. Each platform has strengths. Choose based on your overall needs, not just AI capability—but know that your Mac is more capable than you might have thought.