# Add an LLM to your Android app with Arm's AI Chat library

## In this learning path

- [Introduction](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/)
- [Create the Android project](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/setup-1/)
- [Configure the AI Chat library dependency](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/add-ai-chat-lib-2/)
- [Create the UI layouts and message adapter](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/add-layouts-adapter-3/)
- [Implement the main activity logic](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/use-lib-in-code-4/)
- [Download a model and run the app](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/add-model-run-5/)
- [Next Steps](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/android-ai-chat-lib/_next-steps/)

## About this Learning Path

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| Skill level: | Introductory |
| Reading time: | 15 min |
| Last updated: | 05 Aug 2026 |

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| Author: | Ben Clark , Arm |
| Arm IP: | [Cortex-A](https://support.arm.com/?tab=compute-ip&Product%20Type=Application%20Processors) |
| Tags: | [ML](https://learn.arm.com/tag/ml) [Android](https://learn.arm.com/tag/android) [Kotlin](https://learn.arm.com/tag/kotlin) [Neon](https://learn.arm.com/tag/neon) [SVE2](https://learn.arm.com/tag/sve2) [SME2](https://learn.arm.com/tag/sme2) [LLM](https://learn.arm.com/tag/llm) |

### Who is this for?
This is an introductory topic for developers who want to add a local, on-device LLM chat experience using Arm's AI Chat library, Kotlin, and Android Studio.

### What will you learn?
After completing this Learning Path, you'll be able to perform the following tasks:
- Create a simple Android chatbot app scaffold in Android Studio
- Load a mobile-friendly GGUF model on-device and run streamed chat inference

### Prerequisites
Before starting, you'll need the following prerequisites:
- An Android development environment with Android Studio installed
- An Android phone for testing, in Developer Mode, with USB cable for connection
- Basic familiarity with Kotlin and Android app development

### Summary
You’ll build a minimal Android chatbot that runs a local `GGUF` model with Arm’s AI Chat library. First, you’ll configure the Android Studio project, add the Maven dependency, and create a chat UI. Then, you’ll connect the `MainActivity` class to load a mobile-friendly model and stream responses. Finally, you’ll download a `GGUF` model file on the device and run the app to verify on-device output.

### Frequently asked questions

**Which Gradle file should I edit to add the AI Chat dependency?**

Add the dependency in the app module’s `build.gradle.kts`, not the project-level `build.gradle.kts`. Place the implementation for `com.arm:ai-chat:0.1.0` in the dependencies block.

**How do I check my repository configuration so the library resolves?**

Open `settings.gradle.kts` and confirm the top-level repositories include `google()` and `mavenCentral()`. With both present, you can resolve the AI Chat library from Maven Central.

**Which layout file do I replace for the chat UI?**

Replace `activity_main.xml` in `app/src/main/res/layout` with the provided XML. The XML defines a status area, a message list, and a text input with a send button.

**How do I choose a GGUF model that fits my device?**

Select a model that is significantly smaller than your phone’s available RAM. A mobile-friendly example is `google_gemma-3-4b-it-Q4_0.gguf`, a Q4_0‑quantized Gemma 3 4B model that works well with Arm’s KleidiAI and benefits devices with SME2, SVE2, or Neon capabilities.

**What result should I expect when I run the app?**

After you provide the `GGUF` file on the device, you should see the app load the model and stream chat responses into the message list. Check the status area during generation to confirm progress.