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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesAn app’s AI feature can work in airplane mode if the model and the software that runs it are available on the device—and the task does not need a remote service. Airplane mode does not turn cloud-based AI into offline AI; it simply removes the network connection the cloud feature relies on.
Why an AI feature stops when you go offline
In a cloud-based design, the app sends your request over the internet to a server that runs the model, then returns the result. Without a network path, that exchange cannot happen. In an on-device design, the model is stored or made available on your phone, and the phone’s processor runs it locally.
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That distinction applies to the inference step—the model’s processing of a request—not necessarily to everything the app does. A locally run writing assistant might proofread text offline once its model is available. A feature that must retrieve a web page, sync a conversation, or call a remote API still needs a connection for that part of the task.
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Apple describes Core AI as a way to run models on Apple silicon, with models either bundled into an app or downloaded at runtime. On supported Android devices, Google’s AICore system service manages on-device models, including Gemini Nano. These are platform and developer routes, not a guarantee that every app’s AI features run locally.
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What to check before relying on AI offline
- Model availability: The model may already be included with the app or managed by the operating system, but some apps require a download first. Download it while connected.
- Device and software support: A feature may require a specific device, operating-system version, processor, or software service. Google says Android AICore is available on Android 14 and higher, while also warning that feature availability varies by device and manufacturer.
- The full workflow: Check whether the app needs an account check, cloud sync, current online information, or a remote service in addition to local inference.
- Storage and resources: Models take space, and local execution depends on the device and workload. A model file’s size is not the same as the total space an installed app needs.
- Updates: A model might need to be fetched or updated while online. Google says AICore can temporarily retain old and new model versions for up to three days during an update check, which can increase storage use during that period.
Examples of documented on-device options
Apple: Core AI and Core ML
Apple’s Core AI developer documentation describes inference on Apple silicon without a server dependency for that inference path. An app developer can bundle a model in an Xcode project or Swift package, or download it at runtime. For the latter, offline use depends on the model having been downloaded before the connection is lost.
Apple’s Core ML documentation also describes optimization for the CPU, GPU, and Neural Engine. Apple directs developers working with newer architectures and inference techniques toward Core AI. Xcode integration with Core AI requires Apple’s Metal Toolchain, which Apple says is not installed by default; that is a developer setup requirement, not an extra step for an app user.
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Android: AICore and Google AI Edge
Google lists text summarization and smart replies among AICore features that can work without a network connection. Its Android Help documentation says those supported tasks are processed locally. Android version alone does not establish that a particular phone or app supports a feature: Google says availability varies by device and manufacturer.
For developers, Google’s LiteRT framework supports on-device inference, while LiteRT-LM provides an orchestration layer for large language model workloads, including conversation management, streaming output, multimodal input, and tool use. Platform support and API details differ across the stack, so a developer must check the documentation for the specific library and target platform. Google also describes AI Edge Gallery as an experimental app for demonstrating offline generative AI; its experimental status is not a compatibility promise for every phone.
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Local AI still has practical limits
On-device models can be large. In its LiteRT-LM overview, accessed in 2026, Google lists EmbeddingGemma 2 variants at 165 MB for 270M Text, 388 MB for 440M Text-Vision, and 485 MB for 740M Omnimodal. It lists Gemma-4-E2B at 2.58 GB. These are model file sizes, not full app installation sizes.
Speed also depends on the model, device, hardware backend, and workload. Google’s published benchmark tables list 8.3 ms text latency for Gemma-4-E2B on a Pixel 11 Pro using the TPU, and 56 tokens per second GPU decode for that model on an iPhone 17 Pro in another workload. Those are vendor-published results for the named configurations, not independent tests or performance guarantees for other apps and devices.
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Local execution is not automatically better for every task. A feature’s usefulness depends on whether its model can handle the request and whether the device can run it under the conditions that matter to you. No single model size or benchmark result establishes answer quality, battery use, or speed for every phone.
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Does offline AI mean your data stays on your phone?
It can mean that a particular inference task is processed locally, but it does not establish that every part of the app is offline or that no data is ever transmitted. Google says supported AICore tasks are processed locally and that task data is not sent to the cloud or shared over the internet. That statement applies to the documented AICore features; it should not be generalized to an app’s account, sync, backup, telemetry, or model-download behavior.
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Apple describes on-device inference as keeping data private. For any specific app, the meaningful question is which operation runs locally and what other app functions communicate with a service. Review the app’s own privacy information and offline documentation rather than treating “AI” or “on-device” as a blanket privacy guarantee.
How to tell whether your app will work in airplane mode
- While connected, check the feature’s description and help pages for terms such as on-device, offline, or local processing. A generic AI label is not enough to determine where inference runs.
- Complete any required model download or setup before disconnecting. If the app does not say whether a model is ready, try the feature once while online and look for a download prompt or an offline setting.
- Test the exact task with the connection disabled before you need it. Try the full workflow, not just opening the AI screen: sign-in, saved content, sync, or retrieval may behave differently from local inference.
- Separate the parts that fail. If a local task works but web retrieval or sync does not, the app may be using a local model for one step while relying on a service for another.
- Check device-specific support and storage if the feature is unavailable or a download fails. On Android, confirm the phone and feature support rather than relying on the Android version alone.
What airplane mode can—and cannot—tell you
If an AI feature works with airplane mode enabled, that shows the tested task can run without a network connection under those conditions. It does not prove that every feature in the app uses the same route, or that the app never communicates when connectivity returns. If it fails, the cause could be cloud inference, a missing model, unsupported hardware, or another online dependency—not necessarily that the app has no offline AI at all.
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