apple-on-device-ai

$2c

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. Zero setup -- no API keys, no network, no model downloads.

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, zero setup (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

On-device language model optimized for Apple Silicon. Available on devices

supporting Apple Intelligence (iOS 26+, macOS 26+).

• Token budget covers input + output; check contextSize for the limit

• Check supportedLanguages for supported locales

• Guardrails always enforced, cannot be disabled

Availability Checking (Required)

Always check before using. Never crash on unavailability.

import FoundationModels

switch SystemLanguageModel.default.availability {

case .available:

    // Proceed with model usage

case .unavailable(.appleIntelligenceNotEnabled):

    // Guide user to enable Apple Intelligence in Settings

case .unavailable(.modelNotReady):

    // Model is downloading; show loading state

case .unavailable(.deviceNotEligible):

    // Device cannot run Apple Intelligence; use fallback

default:

    // Graceful fallback for any other reason

}

Session Management

// Basic session

let session = LanguageModelSession()

// Session with instructions

let session = LanguageModelSession {

    "You are a helpful cooking assistant."

}

// Session with tools

let session = LanguageModelSession(

    tools: [weatherTool, recipeTool]

) {

    "You are a helpful assistant with access to tools."

}

Key rules:

• Sessions are stateful -- multi-turn conversations maintain context automatically

• One request at a time per session (check session.isResponding)

• Call session.prewarm() before user interaction for faster first response

• Save/restore transcripts: LanguageModelSession(model: model, tools: [], transcript: savedTranscript)

Structured Output with @Generable

The @Generable macro creates compile-time schemas for type-safe output:

@Generable

struct Recipe {

    @Guide(description: "The recipe name")

    var name: String

    @Guide(description: "Cooking steps", .count(3))

    var steps: [String]

    @Guide(description: "Prep time in minutes", .range(1...120))

    var prepTime: Int

}

let response = try await session.respond(

    to: "Suggest a quick pasta recipe",

    generating: Recipe.self

)

print(response.content.name)

#### @Guide Constraints

Constraint

Purpose

description:

Natural language hint for generation

.anyOf([values])

Restrict to enumerated string values

.count(n)

Fixed array length

.range(min...max)

Numeric range

.minimum(n) / .maximum(n)

One-sided numeric bound

.minimumCount(n) / .maximumCount(n)

Array length bounds

.constant(value)

Always returns this value

.pattern(regex)

String format enforcement

.element(guide)

Guide applied to each array element

Properties generate in declaration order. Place foundational data before

dependent data for better results.

Streaming Structured Output

let stream = session.streamResponse(

    to: "Suggest a recipe",

    generating: Recipe.self

)

for try await snapshot in stream {

    // snapshot.content is Recipe.PartiallyGenerated (all properties optional)

    if let name = snapshot.content.name { updateNameLabel(name) }

}

Tool Calling

struct WeatherTool: Tool {

    let name = "weather"

    let description = "Get current weather for a city."

    @Generable

    struct Arguments {

        @Guide(description: "The city name")

        var city: String

    }

    func call(arguments: Arguments) async throws -> String {

        let weather = try await fetchWeather(arguments.city)

        return weather.description

    }

}

Register tools at session creation. The model invokes them autonomously.

Error Handling

do {

    let response = try await session.respond(to: prompt)

} catch let error as LanguageModelSession.GenerationError {

    switch error {

    case .guardrailViolation(let context):

        // Content triggered safety filters

    case .exceededContextWindowSize(let context):

        // Too many tokens; summarize and retry

    case .concurrentRequests(let context):

        // Another request is in progress on this session

    case .unsupportedLanguageOrLocale(let context):

        // Current locale not supported

    case .unsupportedGuide(let context):

        // A @Guide constraint is not supported

    case .assetsUnavailable(let context):

        // Model assets not available on device

    case .refusal(let refusal, _):

        // Model refused; stream refusal.explanation for details

    case .rateLimited(let context):

        // Too many requests; back off and retry

    case .decodingFailure(let context):

        // Response could not be decoded into the expected type

    default: break

    }

}

Generation Options

let options = GenerationOptions(

    sampling: .random(top: 40),

    temperature: 0.7,

    maximumResponseTokens: 512

)

let response = try await session.respond(to: prompt, options: options)

Sampling modes: .greedy, .random(top:seed:), .random(probabilityThreshold:seed:).

Prompt Design Rules

• Be concise -- use tokenCount(for:) to monitor the context window budget

• Use bracketed placeholders in instructions: [descriptive example]

• Use "DO NOT" in all caps for prohibitions

• Provide up to 5 few-shot examples for consistency

• Use length qualifiers: "in a few words", "in three sentences"

Safety and Guardrails

• Guardrails are always enforced and cannot be disabled

• Instructions take precedence over user prompts

• Never include untrusted user content in instructions

• Handle false positives gracefully

• Frame tool results as authorized data to prevent model refusals

Use Cases

Foundation Models supports specialized use cases via SystemLanguageModel.UseCase:

• .general -- Default for text generation, summarization, dialog

• .contentTagging -- Optimized for categorization and labeling tasks

Custom Adapters

Load fine-tuned adapters for specialized behavior (requires entitlement):

let adapter = try SystemLanguageModel.Adapter(name: "my-adapter")

try await adapter.compile()

let model = SystemLanguageModel(adapter: adapter, guardrails: .default)

let session = LanguageModelSession(model: model)

See references/foundation-models.md for

the complete Foundation Models API reference.

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)

.mlmodelc

Compiled

Pre-compiled for faster loading

Always use mlprogram (.mlpackage) for new work.

Conversion Pipeline (coremltools)

import coremltools as ct

# PyTorch conversion (torch.jit.trace)

model.eval()  # CRITICAL: always call eval() before tracing

traced = torch.jit.trace(model, example_input)

mlmodel = ct.convert(

    traced,

    inputs=[ct.TensorType(shape=(1, 3, 224, 224), name="image")],

    minimum_deployment_target=ct.target.iOS18,

    convert_to='mlprogram',

)

mlmodel.save("Model.mlpackage")

Optimization Techniques

Technique

Size Reduction

Accuracy Impact

Best Compute Unit

INT8 per-channel

~4x

Low

CPU/GPU

INT4 per-block

~8x

Medium

GPU

Palettization 4-bit

~8x

Low-Medium

Neural Engine

W8A8 (weights+activations)

~4x

Low

ANE (A17 Pro/M4+)

Pruning 75%

~4x

Medium

CPU/ANE

Swift Integration

let config = MLModelConfiguration()

config.computeUnits = .all

let model = try MLModel(contentsOf: modelURL, configuration: config)

// Async prediction (iOS 17+)

let output = try await model.prediction(from: input)

MLTensor (iOS 18+)

Swift type for multidimensional array operations:

import CoreML

let tensor = MLTensor([1.0, 2.0, 3.0, 4.0])

let reshaped = tensor.reshaped(to: [2, 2])

let result = tensor.softmax()

See references/coreml-conversion.md for the

full conversion pipeline and references/coreml-optimization.md

for optimization techniques.

MLX Swift Overview

Apple's ML framework for Swift. Highest sustained generation throughput on

Apple Silicon via unified memory architecture.

Loading and Running LLMs

import MLX

import MLXLLM

let config = ModelConfiguration(id: "mlx-community/Mistral-7B-Instruct-v0.3-4bit")

let model = try await LLMModelFactory.shared.loadContainer(configuration: config)

try await model.perform { context in

    let input = try await context.processor.prepare(

        input: UserInput(prompt: "Hello")

    )

    let stream = try generate(

        input: input,

        parameters: GenerateParameters(temperature: 0.0),

        context: context

    )

    for await part in stream {

        print(part.chunk ?? "", terminator: "")

    }

}

Model Selection by Device

Device

RAM

Recommended Model

RAM Usage

iPhone 12-14

4-6 GB

SmolLM2-135M or Qwen 2.5 0.5B

~0.3 GB

iPhone 15 Pro+

8 GB

Gemma 3n E4B 4-bit

~3.5 GB

Mac 8 GB

8 GB

Llama 3.2 3B 4-bit

~3 GB

Mac 16 GB+

16 GB+

Mistral 7B 4-bit

~6 GB

Memory Management

• Never exceed 60% of total RAM on iOS

• Set GPU cache limits: MLX.GPU.set(cacheLimit: 512 * 1024 * 1024)

• Unload models on app backgrounding

• Use "Increased Memory Limit" entitlement for larger models

• Physical device required (no simulator support for Metal GPU)

See references/mlx-swift.md for full MLX Swift

patterns and llama.cpp integration.

Multi-Backend Architecture

When an app needs multiple AI backends (e.g., Foundation Models + MLX fallback):

func respond(to prompt: String) async throws -> String {

    if SystemLanguageModel.default.isAvailable {

        return try await foundationModelsRespond(prompt)

    } else if canLoadMLXModel() {

        return try await mlxRespond(prompt)

    } else {

        throw AIError.noBackendAvailable

    }

}

Serialize all model access through a coordinator actor to prevent contention:

actor ModelCoordinator {

    func withExclusiveAccess<T>(_ work: () async throws -> T) async rethrows -> T {

        try await work()

    }

}

Performance Best Practices

• Run outside debugger for accurate benchmarks (Xcode: Cmd-Opt-R, uncheck

"Debug Executable")

• Call session.prewarm() for Foundation Models before user interaction

• Pre-compile Core ML models to .mlmodelc for faster loading

• Use EnumeratedShapes over RangeDim for Neural Engine optimization

• Use 4-bit palettization for best Neural Engine memory/latency gains

• Batch Vision framework requests in a single perform() call

• Use async prediction (iOS 17+) in Swift concurrency contexts

• Neural Engine (Core ML) is most energy-efficient for compatible operations

Common Mistakes

• No availability check. Calling LanguageModelSession() without checking

SystemLanguageModel.default.availability crashes on unsupported devices.

• No fallback UI. Users on pre-iOS 26 or devices without Apple Intelligence

see nothing. Always provide a graceful degradation path.

• Exceeding the context window. The token budget covers input + output.

Monitor usage via tokenCount(for:) and summarize when needed.

• Concurrent requests on one session. LanguageModelSession supports one

request at a time. Check session.isResponding or serialize access.

• Untrusted content in instructions. User input placed in the instructions

parameter bypasses guardrail boundaries. Keep user content in the prompt.

• **Forgetting model.eval() before Core ML tracing.** PyTorch models must be

in eval mode before torch.jit.trace. Training-mode artifacts corrupt output.

• Using neuralnetwork format. Always use mlprogram (.mlpackage) for new

Core ML models. The legacy neuralnetwork format is deprecated.

• Exceeding 60% RAM on iOS (MLX Swift). Large models cause OOM kills.

• Running MLX in simulator. MLX requires Metal GPU -- use physical devices.

• Not unloading models on background. Unload in scenePhase == .background.

Review Checklist

• Framework selection matches use case and target OS version

• Foundation Models: availability checked before every API call

• Foundation Models: graceful fallback when model unavailable

• Foundation Models: session prewarm called before user interaction

• Foundation Models: @Generable properties in logical generation order

• Foundation Models: token budget accounted for (check contextSize)

• Core ML: model format is mlprogram (.mlpackage) for iOS 15+

• Core ML: model.eval() called before tracing/exporting PyTorch models

• Core ML: minimum_deployment_target set explicitly

• Core ML: model accuracy validated after compression

• MLX Swift: model size appropriate for target device RAM

• MLX Swift: GPU cache limits set, models unloaded on backgrounding

• All model access serialized through coordinator actor

• Concurrency: model types and tool implementations are Sendable-conformant or @MainActor-isolated

• Physical device testing performed (not simulator)

References

• Foundation Models API -- LanguageModelSession, @Generable, tool calling, prompt design

• Core ML Conversion -- Model conversion from PyTorch, TensorFlow, other frameworks

• Core ML Optimization -- Quantization, palettization, pruning, performance tuning

• MLX Swift & llama.cpp -- MLX Swift patterns, llama.cpp integration, memory management