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Yes—on-device AI can work without an internet connection once the required model and runtime are installed on compatible hardware. The important qualification is that offline operation belongs to a specific model and feature, not automatically to every AI function in an app. You may need internet access for the first model download, and an app may send a request to the cloud if local inference is unavailable or fallback is enabled.
Whether it works in airplane mode depends on the platform, device, operating-system and SDK versions, model installation, supported task, and available CPU, GPU or neural-processing unit (NPU). Check the actual inference path before assuming that a feature is private, available offline, or supported on your device.
What “on-device AI” means
On-device AI, also called local inference, runs a trained model on your phone, computer or another local device instead of sending each prompt to a remote server. Microsoft describes the practical result plainly: “A local device does not require an internet connection to run a model, which can be beneficial in environments with limited connectivity.” See Microsoft’s explanation of cloud and local AI models.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchOffline does not mean that the device created the model itself. The model files, runtime libraries and any optional components must already be present. A model can be downloaded while online, cached locally, and then used with Wi‑Fi and mobile data disabled. If the application has no local model for the requested task, it may refuse the request or use a cloud service instead.
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What must be true for AI to work offline
- The model is installed and cached. Initial downloads normally require internet access. Microsoft’s Foundry Local FAQ says inference becomes local after the model has been downloaded and cached; the first download still needs a connection. Read the Windows AI FAQ.
- The device is supported. A feature can require a particular operating-system build, region, memory capacity, GPU, NPU or other accelerator. Support for one local model does not prove that every model or feature is supported.
- The requested task has a local implementation. A text summarizer may run locally while an image generator, voice feature, web-grounded answer or account action still requires a server.
- The app is configured not to force cloud inference. Hybrid applications can choose local inference first and fall back to a hosted model when local capability is missing. If fallback is enabled, data can leave the device.
- Storage and performance are adequate. Model files consume storage, and larger models need more memory and compute. A device may technically load a model but respond slowly, reject long inputs or run out of memory.
Windows: three different local-AI paths
Windows AI APIs
Microsoft’s Windows AI APIs are tied to Copilot+ PCs with an NPU, according to the current Windows AI FAQ. A normal Windows computer should not be assumed to support every API simply because it runs Windows 11. Applications also need the relevant Windows and SDK versions, and Microsoft advises checking feature readiness and whether an optional model is installed.
Foundry Local
Foundry Local is a separate route for running supported models on Windows devices. It supports a DirectX 12-capable GPU, can select hardware-specific execution providers, and can fall back to the CPU. The model must be downloaded while online. After it is cached, inference runs on the device without a cloud dependency, as described in Microsoft’s FAQ.
CPU fallback improves compatibility but can reduce speed and limit practical model size. A discrete or integrated GPU may provide better throughput, while an NPU can be required by a particular Windows AI API. These are feature-specific requirements, not a universal “AI-ready PC” label.
Windows hybrid behavior
A Windows application can test whether local hardware and an optional model are ready, then select local or cloud inference. Microsoft recommends informing users when a hybrid design sends data to the cloud. A device may therefore appear to support an AI feature while airplane mode causes an error, because the local model is absent or the app’s fallback policy requires a network.
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Android: supported devices and narrowly defined tasks
Google’s Android example uses Gemini Nano through ML Kit’s Prompt API for tasks such as summarizing short text and extracting data from receipts. The documented Firebase integration is available only on specific supported devices; it is experimental, and the Prompt API is beta. Its supported route includes single-turn text generation from text and text-plus-one-image inputs, rather than an unrestricted offline chatbot. Details and device constraints are listed in Firebase’s Android hybrid-inference documentation.
Google reported in a July 2026 Android Developers Blog article that Gemini Nano was running on more than 140 million devices. That is a deployment figure reported by Google, not a count showing that all of those devices support every offline feature. The article is available at Android Developers Blog.
On a supported phone, the local model can process an eligible prompt without a round trip to a server. Unsupported input types, long or complex requests, unavailable model downloads, and app-level policy can still cause a cloud request or a failure. Developers should expose the selected route so a user can tell whether a response was produced locally.
Apple platforms: Foundation Models through Apple Intelligence
Firebase’s Apple integration uses Apple’s Foundation Models framework on devices with Apple Intelligence enabled. Firebase says the model is downloaded as part of enabling Apple Intelligence, and its documented on-device integration supports text generation from text-only input. The hybrid feature is marked experimental. See Firebase’s Apple integration guide.
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That means an iPhone, iPad or Mac may have local text generation while an app’s image, audio, web-search or other capability follows a different path. Enabling Apple Intelligence and completing its model download are prerequisites; being an Apple device alone does not guarantee offline generation.
How to verify that a feature really works offline
- Identify the exact feature. Write down what you need: summarizing text, extracting fields from an image, drafting text, transcription, image generation or something else. “The app has AI” is too broad to test.
- Check the platform documentation. Confirm supported devices, operating-system and SDK versions, region restrictions, accelerator requirements and whether the feature is experimental or beta. Use the vendor documentation linked above rather than relying on a generic device label.
- Install the model while connected. Open the app’s model, AI or language-pack settings and wait for the download to finish. Leave enough internal storage for the model and updates. A partially downloaded model will not provide reliable offline operation.
- Record the fallback setting. Look for wording such as “use cloud when available,” “online enhancement,” or “fallback.” Disable cloud fallback if the app provides that control and you need data to remain local. If no control exists, treat the network path as uncertain.
- Test with connectivity removed. Turn on airplane mode, then separately disable Wi‑Fi and mobile data if necessary. Start a new request that matches the documented local task. Do not use a previously cached answer as proof.
- Inspect the result and logs. Some apps label a response as on-device or cloud. Developer tools may show the selected model and execution provider. A successful response proves only that this task worked on this device under these conditions.
- Restore connectivity for updates. Re-enable the network periodically to receive model, runtime and security updates. Test again after an update because a feature’s requirements or fallback behavior can change.
Offline and cloud AI compared
| Factor | On-device inference | Cloud inference |
|---|---|---|
| Connectivity | Works after setup when the required model is cached and the task is supported; initial download and updates need internet. | Requires a network connection for each request unless the app has a separate local fallback. |
| Compatibility | Restricted by device model, memory, operating system, region and CPU/GPU/NPU capability. | Runs on the provider’s servers; the client device mainly needs a supported app and connection. |
| Task scope | Often limited to documented operations such as short-text generation, summarization or extraction. | Can expose larger models and broader text, image, audio or tool-enabled features, subject to the service. |
| Latency and capability | Avoids network round trips, but local compute and model size constrain speed, context and quality. | Can use larger models, although network latency and outages affect availability. |
| Privacy path | Supported inputs can remain on the device, but this is conditional on local execution and disabled cloud fallback. | Requests are transmitted to the provider under that service’s data-handling policy. |
| Storage and updates | Requires local storage for model files and periodic downloads for updates. | Model storage is largely remote; the app still needs updates and may cache limited data. |
Privacy: local does not automatically mean private
When a supported request is processed locally, the prompt, image or document can stay on the device instead of being uploaded. That benefit ends if the application switches to a hosted model. Hybrid systems may fall back because the hardware is unsupported, the local model is not installed, the input exceeds local limits or the developer has enabled cloud enhancement.
Read the app’s privacy and connectivity controls, and test with the network disabled if the distinction matters. Also account for non-AI traffic: login checks, telemetry, synchronization, advertisements and update services may still contact servers even when the inference itself is local.
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Practical limits of local models
Model size and quality
Large cloud models generally have more parameters, memory and context than a model designed for a phone or laptop. A smaller local model can be fast and useful for a constrained task yet produce weaker reasoning, extraction or writing on difficult inputs. Compare results only for the task you actually need.
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Memory, heat and battery
Loading a model consumes RAM or unified memory. Sustained CPU or GPU use can make a phone or laptop warm, drain the battery and trigger thermal throttling. A request that succeeds once may become slower during a long session.
Input and output restrictions
Documentation may limit local inference to short text, one image, single-turn prompts or a particular output format. Long documents, multiple images, audio, tool calls and current-information questions may require a cloud route or separate local component.
Storage and model lifecycle
Keep free storage for the model, tokenizer, runtime and future versions. Removing a downloaded model, clearing app data or restoring a device can silently eliminate offline capability until the package is downloaded again.
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- “Model unavailable”: reconnect, finish the model download, verify storage, then retry in airplane mode.
- The feature works online but not offline: check whether the app is cloud-only for that task or whether fallback is mandatory. Confirm that the local model is the selected provider.
- Your device is listed but the option is missing: verify the exact regional device variant, operating-system build, app version and required NPU/GPU. Support lists can be narrower than a product family name.
- Responses are extremely slow: use a smaller supported model, close memory-heavy applications, connect to power, or accept cloud inference for that task.
- Only short prompts work: this is often a local context or input limit, not a network failure. Split the material or use the documented cloud route.
- Data appears to leave the device: disable cloud fallback where possible, block network access temporarily, and check the app’s route indicator or developer logs. If the app offers no transparency, do not assume local privacy.
What to expect in real use
Offline AI is most dependable for bounded, repeatable jobs: summarizing a short note, extracting fields from a receipt, rewriting a paragraph or generating a response from a small text input. It is less predictable for current events, web-connected answers, very long documents, multimodal workflows and tasks that depend on server-side tools.
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The right question is therefore not “Does this device have AI?” Ask instead: Which model handles this exact input, where is it executed, what must be downloaded first, and what happens when local execution is unavailable? Answer those four questions and you can tell whether airplane-mode operation is genuine or merely a cloud feature with an offline-looking interface.
Frequently Asked Questions
Can I use an on-device AI feature the first time I turn on airplane mode?
Usually not unless its model and runtime were downloaded and initialized beforehand. Complete setup while online, then test the specific task with airplane mode enabled.
Does a Copilot+ PC make every Windows AI feature offline?
No. Copilot+ hardware and an NPU satisfy requirements for some Windows AI APIs, but each feature still needs its supported model, software version and local implementation. Other Windows tools can use a DirectX 12 GPU or CPU fallback.
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Not necessarily. Verify that the particular request stayed local and that cloud fallback or online enhancement was disabled. Login, telemetry and synchronization traffic can still use the network.
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