---
title: "Meet Core AI"
description: "Core AI brings Apple's on-device inference stack to apps, with PyTorch conversion, Swift runtime APIs, Xcode tooling, profiling, states, and specialization control."
shoutout: "Unofficial, made with love by Superwall - the best way to monetize your apps."
category: "AI & Machine Learning"
---

# Meet Core AI


[Watch on Apple Developer](https://developer.apple.com/videos/play/wwdc2026/324/)

Core AI brings Apple's on-device inference stack to apps, with PyTorch conversion, Swift runtime APIs, Xcode tooling, profiling, states, and specialization control.

## TL;DR

- Core AI is Apple's on-device model deployment stack for Apple Silicon, spanning Python/PyTorch conversion and optimization, Swift inference APIs, Xcode inspection, debugging, profiling, and ahead-of-time compilation.
- The basic workflow is: export a PyTorch model with `torch.export`, convert it with `coreai_torch`, save a `.aimodel`, inspect it in Xcode, then load an `AIModel` and run an `InferenceFunction` with `NDArray` inputs.
- For iterative or decoder-style workloads, Core AI supports mutable model states; the session demonstrates a transformer key/value cache that avoids recomputing full history and reduces growing inference latency.
- Large models require device-specific specialization before running; Core AI exposes `AIModelCache`, explicit specialization, `SpecializationOptions`, cache management, app-group cache sharing, and ahead-of-time compilation to improve first-use experience.

## What Core AI provides

Core AI is the inference framework used for on-device Apple Intelligence, now exposed for developers building AI features in their own apps. It is designed for modern on-device workloads across Apple platforms and can execute across Apple Silicon CPU, GPU, and Neural Engine.

The framework is not just a runtime API. It covers the deployment lifecycle: model authoring, optimization, conversion, debugging, app integration, profiling, model specialization, ahead-of-time compilation, and lower-level customization when needed.

- Swift framework for loading `.aimodel` assets and running inference with memory-safe APIs.
- Python tooling built around familiar PyTorch workflows for conversion, authoring, optimization, and validation.
- Xcode integration for model inspection, Core AI Instruments profiling, a Core AI debug gauge, and a visual Core AI Debugger.
- Support for advanced needs such as stateful inference, custom GPU kernels with Metal 4, model specialization control, and optimized tight inference loops.

## Convert and validate a PyTorch model

The demo converts a PyTorch snake-action transformer into Core AI format using the Core AI Torch Python package. The model is exported with `torch.export`, including a dynamic sequence-length dimension so the converted model is not fixed to the sample input length.

After conversion, the session validates numerics by running the same input through the original PyTorch model and the Core AI model using the Python runtime bindings, then checking that logits are close enough for the app's use case.

- Use `torch.export.Dim` and `dynamic_shapes` for dynamic dimensions.
- Run Core AI's decomposition table before conversion.
- Use `TorchConverter().add_exported_program(...)` to name model inputs and outputs.
- Save the result as a `.aimodel` asset for Xcode and the Swift runtime.

### Convert a PyTorch model to Core AI

Exports a PyTorch model with a dynamic sequence dimension, converts it to a Core AI graph, and writes a `.aimodel` asset.

```swift
import torch
import coreai_torch

pt_model = SnakeTransformer().load_checkpoint("snake.pt")
example = torch.randn(1, 5, 16)

seq_len = torch.export.Dim("seq_len", min=1, max=256)
exported = torch.export.export(
    pt_model,
    args=(example,),
    dynamic_shapes={"features": {1: seq_len}},
)
exported = exported.run_decompositions(coreai_torch.get_decomp_table())

ai_program = coreai_torch.TorchConverter().add_exported_program(
    exported,
    input_names=["features"],
    output_names=["logits"],
).to_coreai()

ai_program.save_asset("SnakeTransformer.aimodel")
```

### Validate converted model numerics

Compares Core AI output against PyTorch output before leaving the Python environment.

```swift
import numpy as np
from coreai.runtime import AIModel, NDArray

ai_model = await AIModel.load("SnakeTransformer.aimodel")
function = ai_model.load_function("main")

features = make_snake_features(game).astype(np.float32)[np.newaxis]

with torch.no_grad():
    pytorch_logits = pt_model(torch.from_numpy(features)).numpy()[0, -1]

result = await function({"features": NDArray(data=features)})
coreai_logits = result["logits"].numpy()[0, -1]

assert np.max(np.abs(pytorch_logits - coreai_logits)) < 0.01
```

## Integrate a `.aimodel` in a Swift app

Xcode can open a `.aimodel` file and show metadata such as model size, operation distribution, and function signatures. In the demo, the model has one function that takes game-board features and outputs logits for the next movement direction.

The CoreAI Swift framework centers on `AIModel`, `InferenceFunction`, and `NDArray`. Apps typically create the `AIModel` and load the main function when preparing the feature, then construct `NDArray` inputs, run inference, and process output arrays.

- `AIModel` loads and inspects a `.aimodel` asset.
- `InferenceFunction` represents a runnable compute graph, commonly the model's `main` function.
- `NDArray` holds multidimensional input and output data.
- `NDArray.MutableView` and `NDArray.View` provide efficient, memory-safe access to array storage.

### Load and run an inference function

Shows the core Swift runtime flow: load a model, load a function, pass `NDArray` inputs, and read `NDArray` outputs.

```swift
import CoreAI

let model = try await AIModel(contentsOf: modelURL)
let mainFunction = try model.loadFunction(named: "main")!

let inputNDArray: NDArray = nextInput()
var outputs = try await mainFunction.run(inputs: ["input": inputNDArray])

guard let outputNDArray = outputs.remove("output")?.ndArray else {
    throw ModelError.missingOutput
}
```

### Prepare dynamic game features and run inference

The demo's model player builds a float32 `NDArray`, writes game features into it, runs inference, and chooses the direction with the highest logit.

```swift
extension ModelPlayer: SnakePlayer {
    mutating func chooseAction(game: SnakeGame) async throws -> Direction {
        var inputFeatures = NDArray(
            shape: [game.stepCount, hiddenDim],
            scalarType: .float32
        )
        writeFeatures(of: game, into: inputFeatures.mutableView())

        var outputs = try await nextActionFunction.run(
            inputs: ["features": inputFeatures]
        )
        guard let logits = outputs.remove("logits")?.ndArray else {
            throw ModelError.missingOutput
        }
        return predictedDirection(from: logits.view())
    }
}
```

## Profile and optimize with states

The first implementation feeds the full game history into a transformer on every move. Instruments shows inference intervals growing over time, which matches transformer quadratic complexity as sequence length increases.

The optimization uses Core AI states for transformer key/value caches. States are model inputs that are read and updated in-place during inference. After the first step, the app can pass only the newest features while the cached state carries the relevant history.

- In PyTorch, key and value caches are registered as buffers so the exported program treats them as mutable buffers.
- During conversion, `state_names` maps those mutable buffers to Core AI state arguments.
- In Swift, the app owns `NDArray` cache buffers and passes mutable views through `InferenceFunction.MutableViews`.
- The updated implementation keeps game speed steady and reduces the growth rate of inference latency.

### Convert a stateful model with key/value cache states

Adds state names during conversion so mutable key/value cache buffers become Core AI states.

```text
exported = torch.export.export(
    stateful_model,
    args=(example_features, example_position_ids),
    dynamic_shapes={"position_ids": {1: seq_len}},
)
exported = exported.run_decompositions(coreai_torch.get_decomp_table())

ai_program = coreai_torch.TorchConverter().add_exported_program(
    exported,
    input_names=["features", "position_ids"],
    state_names=["keyCache", "valueCache"],
    output_names=["logits"],
).to_coreai()

ai_program.save_asset("SnakeTransformer.aimodel")
```

### Pass mutable state views from Swift

Passes cache buffers as mutable state views so inference reads and updates them in-place.

```swift
var stateViews = InferenceFunction.MutableViews()
stateViews.insert(&keyCache, for: "keyCache")
stateViews.insert(&valueCache, for: "valueCache")

var outputs = try await nextActionFunction.run(
    inputs: ["features": inputFeatures],
    states: stateViews
)
```

## Specialization, caching, and ahead-of-time compilation

A shipped `.aimodel` is a source representation that can run on Apple devices, but it must be specialized for the user's specific device before loading and inference. First specialization can take significant time for very large models, while later loads can be fast from cache.

Core AI exposes APIs to check and manage the default model cache, request specialization explicitly, configure specialization behavior, delete unused entries, control persistence, and share a cache between apps in the same app group. The session recommends avoiding specialization inside user-interactive flows.

Ahead-of-time compilation can move part of the compilation work to the development machine. The compiled model still needs device-specific specialization, but there is less work left on the user's device.

- Use cache checks to gate features or inform users that AI features are being prepared.
- Request specialization after downloading assets or when a user opts into a feature.
- Consult the Core AI documentation for `SpecializationOptions`, cache policies, app-group cache sharing, and ahead-of-time compilation.
- For tight inference loops, lower-level APIs can query optimal `NDArray` memory layouts, preallocate outputs, and pipeline functions with asynchronous values.

### Check the model cache before loading

Checks whether a specialized model is already available before entering a user-visible flow.

```swift
let cache = AIModelCache.default
guard let model = try cache.model(for: modelURL, options: .default) else {
    Task { @MainActor in
        informUser("Preparing AI features. This may take a while...")
    }
    return
}
```

### Request specialization explicitly

Starts specialization independently of model loading so the model can be prepared ahead of use.

```text
try await AIModel.specialize(contentsOf: modelURL)
```

## Resources

- [Core AI PyTorch Extensions](https://apple.github.io/coreai-torch)
- [Core AI Python](https://apple.github.io/coreai-torch/main/coreai-core)
- [Core AI Optimization](https://apple.github.io/coreai-optimization)
- [Core AI](https://developer.apple.com/documentation/CoreAI)
- [Compiling Core AI models ahead of time](https://developer.apple.com/documentation/CoreAI/compiling-core-ai-models-ahead-of-time)
- [Managing model specialization and caching](https://developer.apple.com/documentation/CoreAI/managing-model-specialization-and-caching)

## Related Sessions

- [Dive into Core AI model authoring and optimization](https://wwdc.ai/2026/325)
- [Integrate on-device AI models into your app using Core AI](https://wwdc.ai/2026/326)
- [Optimize custom machine learning operations with Metal tensors](https://wwdc.ai/2026/330)

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