It changes the economics, privacy, and architecture of building AI features into apps.

Knowing what layer you're on prevents choosing the wrong tool for a model.

Picking the wrong framework wastes optimization effort and hardware acceleration.

Treating it as a pipeline reveals every stage you can optimize and profile.

Moving a feature on-device can eliminate a scaling cost that grows with every request.

Choosing the right level decides how much control and responsibility you take on.

Ready recipes cut the effort of getting research models onto Apple silicon.

Portability lets a single artifact ship efficiently across future devices.

Automatic specialization saves you from hardcoding fragile per-device layer placement.

For multi-gigabyte models, caching prevents brutal first-launch delays.

It turns a painful first-launch compile into a quick startup for shipped models.

Separating weights from the model gives finer control over memory for huge networks.

For big generative and streaming models, memory behavior often decides real performance.

Smart compression is what makes large models fit and run fast on a phone.

A direct PyTorch path shortens the leap from research to shipped on-device model.

You can adopt a brand-new architecture without waiting for an OS update.

Finding where a conversion went numerically wrong is normally one of the hardest tasks.

Architecture alone doesn't determine app performance; measurement does.

You must watch real memory use and can't assume free continuous background inference.

A common abstraction lets you target many devices without rewriting the deployment path.

Understanding the shifted constraints decides whether an on-device feature is actually viable.