local-ai-models

To run on-device LLM inference in iOS apps, initialize a LanguageModelSession using Foundation Models or MLX Swift. You must check Apple Intelligence availability, then stream responses to user prompts while managing sessions and errors.

Yes, Foundation Models supports Vision Language Models (VLMs), image generation, and text embeddings directly on-device. This allows you to process visual inputs and generate multimedia content offline within your iOS application.

Setting up offline AI features with MLX Swift requires the MLX Swift library and a supported device. You need to configure session management, handle errors, and adapt prompts to locale and model capabilities for responsive local inference.

Yes, on-device iOS AI supports tool calling, multi-turn conversations, and structured generation. These features enable you to build responsive, offline assistants that maintain context and interact with external functions without server calls.

Implementing local AI models provides offline inference and responsive AI features without sending user data to servers. This approach enhances privacy and ensures consistent performance regardless of network connectivity using Foundation Models.