Apple On Device AI
Build private, on-device AI features on iPhone, iPad, and Mac with Foundation Models...
Install
npx skills add dpearson2699/swift-ios-skills --skill apple-on-device-ai -g -a claude-code -y- Repository
- dpearson2699/swift-ios-skills1.3k
- Category
- Data and ML
- Updated
- 3mo ago
- License
- See repository
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- No, instructions only
What it does
Build private, on-device AI features on iPhone, iPad, and Mac with Foundation Models, Core ML, MLX Swift, or llama.cpp. Use when choosing an Apple-local model runtime, building an Apple Intelligence chatbot or tool-calling feature, running an LLM on Apple Silicon, converting or compressing a Python model for Core ML, or comparing on-device inference backends. For Swift Core ML loading and prediction code, use the coreml skill.
On-Device AI for Apple Platforms
Guide for selecting, deploying, and optimizing on-device ML models. Covers Apple Foundation Models, Core ML, MLX Swift, and llama.cpp.
Contents
- Framework Selection Router
- Apple Foundation Models Overview
- Core ML Overview
- MLX Swift Overview
- Multi-Backend Architecture
- Performance Best Practices
- Common Mistakes
- Review Checklist
- References
Framework Selection Router
Use this decision tree to pick the right framework for your use case.
Apple Foundation Models
When to use: Text generation, summarization, entity extraction, structured output, and short dialog on iOS 26+ / macOS 26+ devices with Apple Intelligence enabled. No app-managed API key, network round trip, or model hosting; still handle system model asset readiness.
Best for:
- Generating text or structured data with
@Generabletypes - Summarization, classification, content tagging
- Tool-augmented generation with the
Toolprotocol - Apps that need guaranteed on-device privacy
Not suited for: Complex math, code generation, factual accuracy tasks, or apps targeting pre-iOS 26 devices.
Core ML
When to use: Deploying custom trained models (vision, NLP, audio) across all Apple platforms. Converting models from PyTorch, TensorFlow, or scikit-learn with coremltools.
Best for:
- Image classification, object detection, segmentation
- Custom NLP classifiers, sentiment analysis models
- Audio/speech models via SoundAnalysis integration
- Any scenario needing Neural Engine optimization
- Models requiring quantization, palettization, or pruning
