apple-on-device-ai

On-device AI inference with structured output on iOS uses Foundation Models with @Generable and @Guide macros for type-safe generation. It requires iOS 26+ or macOS 26+ for Apple Intelligence, with availability checking and session prewarming to ensure reliable execution.

Running LLM inference privately on Apple Silicon is best handled by MLX Swift for high-throughput workflows, utilizing GPU cache controls and coordinator-based serialization to prevent memory contention. Model size must be guided by device RAM with background unloading support.

Tool calling with Apple Foundation Models works by defining Tool schemas that the model can invoke during generation sessions. It requires proper session management, availability checks, and error handling to ensure the inference flow responds correctly to tool invocations.

Core ML supports custom on-device NLP and vision models through a conversion pipeline using coremltools to the mlprogram format. Optimization involves quantization, palettization, and pruning tradeoffs alongside compute-unit configuration for performance tuning.

A fallback strategy is required when Apple Intelligence is unavailable for on-device AI, using availability-aware routing to coordinate multi-backend fallback across Foundation Models, Core ML, MLX Swift, and llama.cpp GGUF inference to maintain functionality.

On-device AI inference crashes or contends for memory on iOS due to unserialized concurrent access and unmanaged GPU caches. It requires coordinator actors for serialization, context budgeting, and physical-device testing to prevent resource contention and ensure stability.