If you’re using a Mac with Apple Silicon (M1, M2, M3 chips), you’re sitting on a surprisingly powerful machine for AI development — one that’s finally getting the software support it deserves. Whether you’re into training your own models, running LLMs locally, or just experimenting with machine learning, there’s a growing list of frameworks tailor-made for macOS.
In this post, we’ll break down the top machine learning frameworks for Apple, what they’re for, and when to use them.
1. MLX (Machine Learning eXperimentation)
What it is:
MLX is Apple’s own machine learning framework, open-sourced in late 2023. It’s designed to run efficiently on Apple Silicon, using the GPU and unified memory architecture to its advantage.
Why it’s cool:
- Blazingly fast on M1/M2/M3 chips.
- Easy-to-use Python API (with Swift bindings too).
- Actively maintained by Apple engineers.
- Supports training, fine-tuning, and inference.
- Native support for LLMs like Mistral, LLaMA, and TinyLLaMA.
Use MLX if:
You want to fine-tune or run large language models locally and you’re on an Apple Silicon machine.
👉 GitHub: https://github.com/ml-explore/mlx
2. Ollama
What it is:
Ollama is a powerful, user-friendly tool that lets you run and manage LLMs locally on your machine, including Apple Silicon.
Why it’s cool:
- One-liner setup for models like LLaMA, Mistral, and Code LLaMA.
- Simple CLI for model creation and usage.
- Apple Silicon support out of the box.
- Fine-tuning support via LoRA adapters (from MLX, Axolotl, etc.).
- Exposes an OpenAI-compatible API for your apps.
Use Ollama if:
You want to run and use LLMs with zero friction, and even deploy them in your own apps.
👉 Website: https://ollama.com
3. Core ML
What it is:
Core ML is Apple’s official machine learning framework built into iOS, macOS, watchOS, and tvOS.
Why it’s cool:
- Integrates tightly with Apple hardware.
- Optimized for on-device inference.
- Great for app developers building ML-powered features.
- Supports models converted from PyTorch, TensorFlow, and ONNX using CoreMLTools.
Use Core ML if:
You’re building iOS/macOS apps that need to deploy ML models on-device (think vision, NLP, classification, etc.).
👉 Docs: https://developer.apple.com/documentation/coreml
4. Create ML
What it is:
Apple’s GUI-based tool for training common ML models without writing any code.
Why it’s cool:
- Built into macOS (in Xcode or as a standalone app).
- Drag-and-drop UI for training classifiers, style transfer, text models, etc.
- Super beginner-friendly.
- Exports Core ML models directly.
Use Create ML if:
You’re a beginner, or you need to quickly train a model for an app without diving into code.
👉 More Info: https://developer.apple.com/machine-learning/create-ml/
5. TensorFlow-metal / PyTorch-mps
What they are:
GPU-accelerated backends that let TensorFlow and PyTorch use Apple Silicon’s GPU for training.
Why they’re cool:
- No need for CUDA or Nvidia GPUs.
- Uses the Metal Performance Shaders (MPS) API under the hood.
- Speeds up model training significantly vs. CPU-only.
Use them if:
You’re using TensorFlow or PyTorch and want to train models on your Mac without external GPUs.
👉 PyTorch MPS: https://pytorch.org/docs/stable/notes/mps.html
👉 TensorFlow-metal: https://developer.apple.com/metal/tensorflow-plugin/
Bonus: Axolotl, Unsloth, and Others
While not Apple-only, these open-source fine-tuning frameworks support Apple Silicon to varying degrees:
- Unsloth: Memory-efficient LLM fine-tuning, works well with Ollama & Google Colab.
- Axolotl: Advanced fine-tuning and instruction-tuning framework.
- Transformers + Datasets (🤗): The Hugging Face ecosystem can be run on Apple Silicon, though it’s a bit more memory-intensive.
🏁 Final Thoughts
Machine learning on Apple devices has come a long way. With Apple investing more in ML tools and with the community embracing local-first development, it’s now easier than ever to build, fine-tune, and run models on your Mac.
Whether you’re an iOS developer, a tinkerer training LLMs, or just curious about AI — the Apple ecosystem finally has the tools to keep up.
