MLX Swift is an open-source array computing framework for Apple platforms that lets you write mathematical code using n-dimensional arrays, with automatic GPU execution and automatic differentiation via function transformations like `grad`. It brings NumPy-style numerical computing to Swift with lazy evaluation and a clean, math-like API.
• Write vectorized math that operates on entire arrays at once instead of scalar-by-scalar loops — code reads like the math, runs on the GPU by default
• Automatic differentiation via `grad` lets you compute gradients of arbitrary functions without hand-writing derivatives — the foundation of custom ML training loops
• Seamlessly interoperates with the broader MLX ecosystem (Python, C++, C) so you can prototype in Python and ship in Swift using the same concepts and operations
Foundation Models is a new Apple framework introduced in iOS 27 that gives developers on-device access to the same Apple Intelligence language model powering system features, enabling text generation, structured output, and tool-calling entirely on-device without a network connection.
Visual Intelligence brings iOS 17's Visual Look Up capabilities to a new developer-facing API surface in iOS 27, letting apps pipe live camera frames or static images through on-device scene understanding to extract subjects, text, barcodes, and rich semantic labels without any cloud round-trip.
iOS 27 opens the Foundation Models framework to third-party LLM providers via a new public LanguageModel protocol, enabling anyone to integrate custom, server-based, or open-source models using the same Swift API as Apple's on-device system model.
In-depth guide
iOS 27 On-Device AI & Apple Intelligence →App Schemas let developers describe their app's content and actions using pre-defined domain schemas (like the Calendar domain) so Siri can understand, search, and act on app data without custom NLP. Entities conforming to IndexedEntity are donated to Spotlight's semantic index, enabling natural-language queries over app content.