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Core ML

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description Core ML Overview

Core ML is Apple's native framework, providing deep learning model deployment optimized specifically for Apple silicon (Neural Engine, GPU). If your target deployment is exclusively iOS or macOS, using Core ML ensures the absolute best performance and lowest power consumption. It integrates seamlessly into Xcode and the Apple developer ecosystem, making the development cycle highly streamlined for Apple users.

help Core ML FAQ

Can Core ML run a PyTorch model on an iPhone without sending data to a server?

Yes, convert the model with Core ML Tools and bundle the resulting Core ML model in the app. Apple describes on-device execution across the CPU, GPU, and Neural Engine, so that inference path does not require a network connection. [Apple Core ML documentation](https://developer.apple.com/documentation/coreml/)

Does Core ML automatically use the Neural Engine?

Core ML can dispatch work across the CPU, GPU, and Neural Engine based on the model and device, and developers can constrain the allowed compute units when needed. A model is not guaranteed to run entirely on the Neural Engine. [Apple Core ML documentation](https://developer.apple.com/documentation/coreml/)

Can Core ML handle more than image classification?

Yes. Apple lists object detection, image categorization, Natural Language text tasks, Speech, and Sound Analysis as areas that can use Core ML, while Create ML models can be used directly in Apple apps.

Is Core ML for Mac apps too, or only iPhone apps?

Core ML is used across Apple platforms including iOS, macOS, watchOS, and tvOS, with exact API availability depending on the operating system. It is not a general Windows or Android runtime, so cross-platform apps usually need another inference path.

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