/apple-on-device-ai

name: apple-on-device-ai
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."

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

**When to use:** Running specific open-source LLMs (Llama, Mistral, Qwen, Gemma) on Apple Silicon with maximum throughput. Research and prototyping.

**Best for:**

• Highest sustained token generation on Apple Silicon
• Running Hugging Face models from `mlx-community`
• Research requiring automatic differentiation
• Fine-tuning workflows on Mac

llama.cpp

**When to use:** Cross-platform LLM inference using GGUF model format. Production deployments needing broad device support.

**Best for:**

• GGUF quantized models (Q4_K_M, Q5_K_M, Q8_0)
• Cross-platform apps (iOS + Android + desktop)
• Maximum compatibility with open-source model ecosystem

Quick Reference

| Scenario | Framework | |---|---| | Text generation on Apple Intelligence devices (iOS 26+) | Foundation Models | | Structured output from on-device LLM | Foundation Models (`@Generable`) | | Image classification, object detection | Core ML | | Custom model from PyTorch/TensorFlow | Core ML + coremltools | | Running specific open-source LLMs | MLX Swift or llama.cpp | | Maximum throughput on Apple Silicon | MLX Swift | | Cross-platform LLM inference | llama.cpp | | OCR and text recognition | Vision framework | | Sentiment analysis, NER, tokenization | Natural Language framework | | Training custom classifiers on device | Create ML |

Apple Foundation Models Overview

Use the system language model for short generation, summarization, tagging, structured output, and tool-augmented tasks on Apple Intelligence devices. Gate every entry point before creating a session:

import FoundationModels

switch SystemLanguageModel.default.availability {
case .available:
    guard SystemLanguageModel.default.supportsLocale(Locale.current) else {
        // Use locale fallback before generating
        break
    }
    // Proceed with model usage
case .unavailable(.appleIntelligenceNotEnabled):
    // Guide user to enable Apple Intelligence in Settings
case .unavailable(.modelNotReady):
    // System model assets are not ready; show loading state
case .unavailable(.deviceNotEligible):
    // Device cannot run Apple Intelligence; use fallback
case .unavailable(let reason):
    // Unknown or future unavailable reason; use fallback and log reason
}

Then create a session and keep its shared context budget small:

let session = LanguageModelSession {
    "You are a helpful cooking assistant."
}
session.prewarm()
let response = try await session.respond(to: "Suggest a quick pasta recipe")

Required guardrails:

• Sessions are stateful and accept one request at a time; serialize access and

check `isResponding` before issuing another response.

• Instructions, tools, schemas, prompts, transcripts, and output share the

context window. Register only necessary tools and keep schemas compact.

• Resolve the locale with `supportsLocale(_:)`; do not raw-match language lists.
• Keep untrusted user content in prompts, never instructions. System guardrails

remain active, so handle refusal and other generation errors with fallback UI.

Load [the Foundation Models reference](references/foundation-models.md) when the task needs `@Generable`, `@Guide`, streaming, tool definitions, transcripts, generation options, custom adapters, prompt design, or detailed error handling.

Core ML Overview

Apple's framework for deploying trained models. Automatically dispatches to the optimal compute unit (CPU, GPU, or Neural Engine).

Model Formats

| Format | Extension | When to Use | |---|---|---| | `.mlpackage` | Directory (mlprogram) | All new models (iOS 15+) | | `.mlmodel` | Single file (neuralnetwork) | Legacy only (iOS 11-14) | | `.mlm