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Run AI Locally on Android or iPhone for Free with Google AI Edge Gallery

Google AI Edge Gallery lets you run downloaded AI models on compatible Android phones and iPhones, including offline after setup. Here’s how to start and what to expect.
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Yes—you can run AI directly on a compatible Android phone or iPhone for free using Google AI Edge Gallery. Install the app, download a model while connected to Wi-Fi, and then chat with it on your device; Google says model inference can work without an internet connection. The published minimums are Android 12 or later and iOS 17 or later.

How to get started

  1. Check your operating system. Google lists Android 12 or later and iOS 17 or later as the app’s requirements. These are OS minimums, not a promise that every supported phone will run every model smoothly.
  2. Install Google AI Edge Gallery. Find the app in Google Play or the Apple App Store. The app is free on the US App Store.
  3. Download a model over Wi-Fi. Model files can be large, so check the available storage on your phone and the size shown for the model you select. As one example, Google’s 2026 LiteRT-LM documentation lists Gemma 4 E2B at 2.58 GB; other models can differ.
  4. Try a prompt in AI Chat. Start with a simple question, summary, or draft. Image, audio, and other tools depend on the selected model and app version.
  5. Test it offline. Once the app and model are downloaded, turn off Wi-Fi and mobile data and try another prompt. This checks local inference; web tools and features that retrieve remote content need connectivity.

Google describes AI Edge Gallery as an experimental beta, so its features and store listings may change.

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What local AI can—and cannot—do

Useful for everyday, self-contained tasks

A downloaded model can help draft text, summarize material you provide, or answer straightforward questions without sending the prompt to a remote model for inference. The app’s usefulness depends on the model, the phone’s hardware, and the task.

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Not a substitute for current online information

An offline model cannot look up breaking news, newly published facts, or current web pages. Like other generative AI, it can also give incorrect answers. Verify important claims, and reconnect when you need information from the live web.

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Compatibility, storage, and speed

Meeting the Android or iOS version requirement does not guarantee a fast or complete experience. Google warns that performance depends on device hardware. Its published overview includes measurements from a Samsung S26 Ultra and an iPhone 17 Pro, but those are Google’s device-specific benchmarks—not independent comparisons or results that apply to every phone.

Check the model’s download size before installing it and leave room for other apps and files. If a model runs slowly or is unavailable on your device, try a smaller compatible model if the app offers one. The official materials do not establish a universal phone recommendation or a requirement to buy a new device.

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Privacy: what offline inference means

Google says prompts and model inference stay on the device and that an internet connection is not required for inference. You do need connectivity to download the app and model files first. This claim concerns local inference; it should not be read as a guarantee that every app feature is offline or that the app has no telemetry.

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Google Play’s separate, developer-provided data-safety disclosure says no data is shared with third parties, while listing possible collection of app activity and app info and performance data; it also says data is encrypted in transit. Connected features that use web tools or fetch remote content are outside the offline-inference scenario.

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When developers should look beyond the app

For someone who wants to use AI on a phone, the store app is the straightforward route. Developers building on-device AI into their own products can instead consider Google’s LiteRT-LM, a cross-platform runtime, or the MediaPipe LLM Inference API for iOS. Google’s LiteRT-LM documentation describes support for Android, iOS, web, desktop, and IoT, and lists models including Gemma, Llama, Phi-4, and Qwen. These are development tools, not easier alternatives for ordinary app users.

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