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You can use local AI without spending mobile data on repeated cloud requests, but setup still requires data to install the app and download a compatible model. Do those downloads over Wi-Fi, check storage and device capability first, then test a prompt with both Wi-Fi and mobile data off. Local inference works offline only after the required files are on your device.
What “local AI” can—and cannot—do offline
With local inference, the model runs on your device instead of sending each prompt to a cloud service. Once the app or runtime and a compatible model are installed, supported tasks can work without an internet connection. LM Studio says its model chats and local document processing can work offline when the model files are present; Google AI Edge Gallery describes its inference as on-device. See LM Studio’s offline-operation documentation and the Google AI Edge Gallery project documentation.
That does not make setup data-free. Installing the software, finding and downloading a model, and getting updates require connectivity in at least some workflows. Optional online features may also stop working offline. Download the app and model over Wi-Fi or another trusted unmetered connection, and confirm the model is fully available locally before disconnecting.
Choose a setup that fits your device
| Option | Best fit | What to know |
|---|---|---|
| Google AI Edge Gallery | Trying local models directly on a supported phone or tablet | The project describes the app as experimental. Its README lists Android 12+ and iOS 17+ and supports downloading or loading models. Device and model compatibility still matter. Project documentation |
| LM Studio | Running local chat or document Q&A on a desktop or laptop | Model chat and local document features can work offline once the files are present. Searching for models, downloading models or runtimes, and checking for updates need connectivity. Offline documentation |
| Ollama | A computer-hosted local model service, including certain developer workflows | Google names Ollama as a local provider for Android Studio. That guide is a developer workflow, not evidence of a phone-native Ollama app. Android Developers guide |
| LiteRT-LM | Developers building or testing on-device models across platforms | This is a developer runtime and tooling path, not the simplest consumer app installation. LiteRT-LM overview and LiteRT getting started |
For a phone-first setup, start with an app that supports your operating system and lets you download or load a compatible model. For a computer-first setup, a desktop runtime may offer a more direct path to local chat and document work. Before choosing, check the model format the app accepts, the device’s RAM, and available storage. Smaller models may be easier to accommodate, but model quality and speed vary by model and hardware; local output is not guaranteed to match cloud AI.
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Check compatibility and storage before downloading
Model size is only part of the space you need: leave room for the app or runtime and ordinary device operation. Listed model sizes illustrate the range, but they are not a promise of total installed storage or of the size for every version or quantization. Google AI Edge’s LiteRT-LM overview lists Gemma-4-E2B at 2.58 GB and EmbeddingGemma text variants at 165 MB, 388 MB, and 485 MB. Check the listing for the exact model and variant you intend to use: LiteRT-LM model overview.
Requirements also depend on the specific model and workflow. In its Android Studio local-model guide, last updated September 2, 2026, Google lists Gemma E4B with 12 GB total RAM and 4 GB storage, and Gemma 26B MoE with 24 GB RAM and 17 GB storage. These are examples for that workflow, not universal minimums for local AI. Check your chosen model’s own compatibility details rather than applying those figures to every app or device. Android Developers: Use a local model.
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Set it up over Wi-Fi, then verify offline
- Check the device. Note its operating-system version, free storage, and RAM. For Google AI Edge Gallery, the project README lists Android 12+ and iOS 17+; requirements can change, so confirm the current listing in the project documentation.
- Install the app or runtime while connected to Wi-Fi. Use the official distribution or project route for your platform. Avoid starting model discovery or large downloads over cellular if data is limited.
- Select one model for a specific task. Check the model’s file size, supported format, and device requirements against the app or runtime. Start with one suitable model instead of downloading several alternatives to compare.
- Download the model and wait for completion. Confirm that the app shows it as installed or available locally. If you bring in a model file from another device, use a format supported by the selected runtime; LM Studio documents sideloading, and AI Edge Gallery supports custom models. Neither support statement means every file or transfer method will work on every device. See LM Studio offline operation and the AI Edge Gallery README.
- Test without a connection. Temporarily turn off Wi-Fi and mobile data, open the selected model, and try a simple prompt. If it responds, the inference path you tested is working locally. Model discovery, updates, and optional online features may still need internet.
What to expect from offline use
Google AI Edge Gallery’s project documentation states: “All model inferences happen directly on your device hardware. No internet is required, ensuring total privacy for your prompts, images, and sensitive data.” That describes the app’s inference path; it does not mean every feature in every local-AI app works offline. The practical check is to disconnect and test the particular task you plan to use.
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Quick Recap
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