Use a local model

Large language models (LLMs) differ in their capabilities. To give you control and flexibility in AI-assisted software development, Android Studio lets you choose a local model, running on your personal machine, to power the IDE's AI functionality.

Choose a model

A local model offers an alternative to the LLM support built into Android Studio; however, Gemini in Android Studio typically provides the best AI experience for Android developers because of the powerful Gemini models. You can select from a variety of Gemini models for your Android development tasks, including the no-cost default model or models accessed with a paid Gemini API key.

Local model capability is a great option if you need to work offline, must adhere to strict company policies on AI tool usage, or are interested in experimenting with open-source research models.

Try the Gemma 4 model

If you need to use a local model, we recommend trying Gemma 4, a state-of-the-art local model available for agentic coding in Android Studio.

The hardware requirements are as follows. We recommend using the Gemma 26B MoE model if you have a high-performance machine.

Model	Total RAM needed	Storage
Gemma E4B	12 GB	4 GB
Gemma 26B MoE	24 GB	17 GB

See the setup guide for instructions on how to download and run Gemma 4 locally on your machine.

Set up local model support

Download and install the latest version of Android Studio.
Install an LLM provider such as LM Studio or Ollama on your local computer.
Add the model provider to Android Studio.
- Go to Settings > Tools > AI > Model Providers
- Select the icon
- Select Local Provider
- Enter a description of the model provider (typically the model provider's name)
- Set the port on which the provider is listening
- Enable a model
Android Studio settings dialog showing the Gemini section with an option to enable offline mode.
Figure 1. Model provider settings.
Download and install a model of your choice.

See the LM Studio and Ollama model catalogs. For the best experience with Agent Mode in Android Studio, select a model that has been trained for tool use.

Android Studio settings dialog showing a list of available local models.
Figure 2. Available local models.
Start your inference environment.

The inference environment serves your model to local applications. Configure a sufficiently large context length token window for optimal performance. For detailed instructions on starting and configuring your environment, see the Ollama or LM Studio documentation.
Select a model.

Open Android Studio. Navigate to the Gemini chat window. Use the model picker to switch from the default Gemini model to your configured local model.

Android Studio Gemini chat window showing the model picker with options for Gemini and a local model.
Figure 3. Model picker.

After you've connected Android Studio to your local model, you can use the chat features within the IDE. All interactions are powered entirely by the model running on your local machine, giving you a self-contained AI development environment.

Consider performance limitations

A local, offline model typically won't be as performant or intelligent as the cloud-based Gemini models. Chat responses from local models are usually less accurate and have higher latency compared to cloud-based models.

Local models are usually not fine-tuned for Android development, and local models can return responses that are uninformed about the Android Studio user interface. Some Android Studio AI features and Android development use cases are nonfunctional with a local model. However, the AI chat feature in Android Studio is generally supported by local models.

For fast, accurate responses on all aspects of Android development and support for all Android Studio features, Gemini in Android Studio, powered by the Gemini models, is your best solution.