apple-on-device-ai

On-device AI inference on Apple Silicon is enabled by wiring Foundation Models, Core ML, MLX Swift, and llama.cpp into a single local workflow. This stack supports private, offline text generation, classification, and streaming directly across iOS and macOS devices.

Foundation Models support structured output on iOS 26+ using the @Generable schema for deterministic responses. Tool calling is orchestrated through these schemas, providing controlled session management and localized generation guards directly on-device.

The best way to deploy Core ML models for offline inference involves model conversion, optimization, and deployment across iOS and macOS devices. This approach ensures high-throughput local execution while managing resource constraints and enforcing availability checks.

MLX Swift and llama.cpp work together to provide high-throughput open-source LLM options and cross-platform inference on Apple Silicon. They are integrated into the local stack to handle text generation and memory management alongside Foundation Models.

On-device AI requires availability checks and memory management guards to prevent resource exhaustion and enforce safety guardrails. Serialized access and backend routing ensure the local inference workflow respects hardware constraints and maintains system stability.