iOS On-Device AI Models

Production-ready guide for implementing on-device AI models in iOS apps using Apple's Foundation Models framework and MLX Swift.

When to Use This Skill

• Implementing local LLM inference in iOS apps
• Building chat interfaces with Foundation Models
• Integrating Vision Language Models (VLMs)
• Adding text embeddings or image generation
• Implementing tool/function calling with LLMs
• Managing multi-turn conversations
• Optimizing memory usage for on-device models
• Supporting internationalization in AI features

Core Principles

1. Availability First - Always check model availability before initialization
2. Stream Responses - Provide progressive UI updates for better UX
3. Session Persistence - Reuse LanguageModelSession for multi-turn conversations (Foundation Models)
4. Memory Awareness - Use quantized models and monitor memory usage
5. Async Everything - Load models asynchronously, never block the main thread
6. Locale Support - Use supportsLocale(_:) and locale instructions for Foundation Models

Quick Reference

Framework Comparison

Topic	Guide
Framework comparison and selection	framework-selection.md

Foundation Models (Apple's Framework)

Topic	Guide
Setup and configuration	foundation-models/setup.md
Chat patterns and conversations	foundation-models/chat-patterns.md

MLX Swift (Advanced Features)

Topic	Guide
Setup and configuration	mlx-swift/setup.md
Chat patterns with custom models	mlx-swift/chat-patterns.md
Vision Language Models (VLMs)	mlx-swift/vision-patterns.md
Tool calling, embeddings, structured gen	mlx-swift/advanced-patterns.md
Model quantization with MLX-LM	mlx-swift/quantization.md

Shared (Both Frameworks)

Topic	Guide
Best practices and optimization	shared/best-practices.md
Error handling and recovery	shared/error-handling.md
Testing strategies	shared/testing.md

Quick Decision Trees

Which framework should I use?

Do you need advanced features like:
- Vision Language Models (VLMs)
- Image generation
- Custom models beyond the system model
├── Yes → MLX Swift (references/mlx-swift/)
└── No → Is this a standard chat interface?
    ├── Yes → Foundation Models (simpler, recommended)
    └── No → Check framework-selection.md for guidance

Where should I start?

New to on-device AI?
└── Start with Foundation Models:
    1. Read framework-selection.md
    2. Follow foundation-models/setup.md
    3. Implement foundation-models/chat-patterns.md

Need advanced features?
└── Use MLX Swift:
    1. Read framework-selection.md
    2. Follow mlx-swift/setup.md
    3. Choose pattern:
       - Chat: mlx-swift/chat-patterns.md
       - Vision: mlx-swift/vision-patterns.md
       - Advanced: mlx-swift/advanced-patterns.md

Where should my model loading code live?

Is this model shared across features?
├── Yes → Create @Observable service in app/services/
└── No → Is it feature-specific?
    ├── Yes → Create @Observable class in feature/
    └── No → Load inline with @State (simple cases only)

How should I handle conversations?

Foundation Models:
└── Reuse LanguageModelSession for context
    (references/foundation-models/chat-patterns.md #multi-turn)

MLX Swift:
└── Implement custom context management
    (references/mlx-swift/chat-patterns.md)

What generation parameters should I use?

What's the use case?

Factual answers (summaries, facts)
└── temperature: 0.1-0.3

Balanced (chat, Q&A)
└── temperature: 0.6-0.8

Creative (storytelling, ideas)
└── temperature: 0.9-1.2

See references/shared/best-practices.md for details

Resources

• MLX Swift Examples
• Foundation Models Docs
• Hugging Face Model Hub
• MLX-LM Quantization
• MLX Community Models