Google's Gemma 4 changes what on-device AI inference looks like on Android. By tapping Qualcomm Snapdragon and MediaTek Dimensity NPUs, Gemma 4 delivers cloud-quality text generation directly on the device — no network request needed.
For developers building apps that handle sensitive data, or apps that need to work reliably offline, this is significant. This guide walks through how to integrate Gemma 4 into an Android app using Antigravity as your IDE, from setup to production-ready patterns.
What Makes Gemma 4 Different
Gemma 4 is Google's latest open model family, optimized specifically for edge deployment. A few things stand out:
- NPU-native optimization: Runs efficiently on Qualcomm Snapdragon and MediaTek Dimensity chips via dedicated AI accelerators
- Android AICore integration: Android 14+ devices can access Gemma 4 through the standard AICore API, reducing integration friction
- Strong multilingual support: Japanese, Korean, and other non-English languages perform well on-device
- Low latency: Typical response times on mid-range devices are under a second for short prompts
- Data stays on device: Nothing leaves the handset, which matters for healthcare, finance, and legal applications
Setting Up with Antigravity
Antigravity handles Android development well, and the prompt-based workflow makes Gemma 4 integration more approachable than working with raw SDK docs.
Step 1: Project Setup
Start a new Android project in Antigravity and prompt:
Create a Kotlin Android project targeting Android 14 and above.
I want to add on-device text generation using Gemma 4 via Android AICore.
Please add the necessary dependencies to build.gradle.kts.
The agent will pull in the correct Google AICore dependencies and scaffold the initial project structure.
Step 2: Check AICore Availability
Not all devices support Android AICore, so you need a check before attempting to use it:
suspend fun checkAiCoreAvailability(): Boolean {
return try {
val downloadedModel = DownloadedModel.create(
context,
DownloadConfig.assetUri("gemma-4-2b.task")
)
downloadedModel != null
} catch (e: Exception) {
false
}
}When prompting Antigravity to generate this, add: "Include a fallback to Cloud API for devices that don't support AICore." This gets you a dual-path implementation without having to write the branching logic yourself.
Step 3: Text Generation Implementation
Once you've confirmed AICore availability, the inference itself is straightforward:
class GemmaTextGenerator(private val context: Context) {
private var inferenceModel: InferenceModel? = null
suspend fun initialize() {
inferenceModel = InferenceModel.create(
context,
InferenceModel.Config.Builder()
.setModelPath("gemma-4-2b.task")
.build()
)
}
suspend fun generateText(prompt: String): String {
return inferenceModel?.generateResponse(prompt)
?: "Model not initialized"
}
fun close() {
inferenceModel?.close()
}
}Antigravity will generate the boilerplate and also handle lifecycle binding (tying close() to the ViewModel's onCleared()), error handling, and coroutine scoping — things that are easy to miss when writing this from scratch.
Practical Use Cases
The Gemma 4 + Android AICore combination works best in these scenarios:
Offline document assistance: Users can get text correction, summarization, and drafting help even without connectivity. Travel apps, note-taking tools, and writing assistants are natural fits.
Privacy-sensitive data handling: Medical, legal, and financial apps often can't send user data to external servers. On-device inference eliminates that risk entirely.
Intelligent form autocomplete: Analyze what users are typing locally and surface context-aware suggestions. The fact that this happens on-device can be a genuine user trust signal.
Local transcription and summarization: Combined with an on-device speech recognition model, you can offer meeting transcription and summary features without any cloud dependency.
Tips for Prompting Antigravity Effectively
A few patterns that work well when building Gemma 4 features in Antigravity:
Be explicit about the SDK surface you want. Prompts like "use Android AICore, Kotlin Coroutines, and proper ViewModel lifecycle management" give the agent enough context to avoid generating outdated or naive patterns.
Specify the fallback behavior upfront. Antigravity handles branching logic well when you describe both the happy path and the degraded path in the same prompt.
Ask for Wi-Fi-only model download logic. On-device models can be large. Adding "only download the model on Wi-Fi" to your prompt produces the WorkManager-based download scheduling code automatically.
Performance Considerations
On-device inference with Gemma 4 is fast, but a few things to keep in mind:
The first-run model load takes a few seconds. Use a splash screen or loading indicator to keep the UX smooth. Memory pressure can cause the process to be killed in the background, so manage coroutine scopes carefully with the ViewModel lifecycle.
For streaming responses (word-by-word output), prompt Antigravity to implement the Flow-based streaming API rather than blocking calls — this keeps your UI responsive during generation.
Looking back
Gemma 4 with Android AICore represents a meaningful shift for mobile developers who want to add AI features without accepting the tradeoffs of cloud dependency. Antigravity accelerates the integration with its agentic code generation, handling the repetitive parts so you can focus on the product decisions that actually matter.
If you haven't tried on-device AI for your Android projects yet, Gemma 4 is the most practical starting point available right now.