I recently tried to figure out what their offerings currently are. I'm hoping for `efficent but performant AI compute-chips` by Apple ever since they kicked out Nvidia in 2015 (for the ML Models / Exploration parts bellow). It will be interesting to see how good their products will feel in this fast-paced environment and how much legroom (RAM + Compute) will be left non-platform offerings.

To my understanding, they market their ML stack as four layers [1]:

- Platform Intelligence: ready-made OS features (e.g., Writing Tools, Genmoji, Image Playground) that apps can adopt with minimal customization.

- ML-powered APIs: higher-level frameworks for common tasks—on-device Foundation Models (LLM), plus Vision, Natural Language, Translation, Sound Analysis, and Speech; with optional customization via Create ML.

- ML Models (Core ML): ship your own models on-device in Core ML format; convert/optimize from PyTorch/TF via coremltools, and run efficiently across CPU/GPU/Neural Engine (optionally paired with Metal/Accelerate for more control).

- Exploration/Training: Metal-backed PyTorch/JAX for experimentation, plus Apple’s MLX for training/fine-tuning on Apple Silicon using unified memory, with multi-language bindings and models commonly sourced from Hugging Face.

[1] https://developer.apple.com/videos/play/wwdc2025/360/