# Apple On Device AI

Build private, on-device AI features on iPhone, iPad, and Mac with Foundation Models...

- Repository: https://github.com/dpearson2699/swift-ios-skills (1266 stars)
- Category: Data and ML
- License: see repository
- Last updated: 2026-07-31
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add dpearson2699/swift-ios-skills --skill apple-on-device-ai -g -a claude-code -y`
Codex: `npx skills add dpearson2699/swift-ios-skills --skill apple-on-device-ai -g -a codex -y`
Cursor: `npx skills add dpearson2699/swift-ios-skills --skill apple-on-device-ai -g -a cursor -y`
Gemini CLI: `npx skills add dpearson2699/swift-ios-skills --skill apple-on-device-ai -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add dpearson2699/swift-ios-skills`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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](#framework-selection-router)
- [Apple Foundation Models Overview](#apple-foundation-models-overview)
- [Core ML Overview](#core-ml-overview)
- [MLX Swift Overview](#mlx-swift-overview)
- [Multi-Backend Architecture](#multi-backend-architecture)
- [Performance Best Practices](#performance-best-practices)
- [Common Mistakes](#common-mistakes)
- [Review Checklist](#review-checklist)
- [References](#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 `@Generable` types
- Summarization, classification, content tagging
- Tool-augmented generation with the `Tool` protocol
- 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

### MLX Swift

The rest: https://github.com/dpearson2699/swift-ios-skills/blob/HEAD/skills/apple-on-device-ai/SKILL.md