{"slug":"google-ai-edge-litert-runtime","name":"litert-runtime","description":"Creates an Android app that runs a .tflite model on the CPU or the GPU with the LiteRT CompiledModel API in Kotlin. Use this skill to build a new app around a vision, audio or embedding model, or to add on-device inference to an existing app - the dependency, where the model file goes, the inference class, the ViewModel and screen, and checking the output on a device.","long_description":"---\nname: litert-runtime\ndescription: Creates an Android app that runs a .tflite model on the CPU or the GPU with the LiteRT CompiledModel API in Kotlin. Use this skill to build a new app around a vision, audio or embedding model, or to add on-device inference to an existing app - the dependency, where the model file goes, the inference class, the ViewModel and screen, and checking the output on a device.\nlicense: Apache-2.0\nmetadata:\n  last-updated: '2026-10-09'\n  keywords: [LiteRT, CompiledModel, tflite, Android app, GPU]\n---\n\nThis skill provides step-by-step guidance for building an Android app that runs a `.tflite` model with the LiteRT CompiledModel API (`com.google.ai.edge.litert:litert` 2.x; overview: https://ai.google.dev/edge/litert/android, sources: https://github.com/google-ai-edge/litert/blob/main/README.md) on the CPU or the GPU. The Interpreter API is not covered. For language models, use the `litert-lm` skill: https://github.com/google-ai-edge/litert-samples/blob/main/skills/litert-lm/SKILL.md (LiteRT-LM: https://github.com/google-ai-edge/LiteRT-LM/blob/main/README.md).\n\n## Prerequisites\n\n- A Kotlin Android project (Android Studio's Empty Activity template is enough). The LiteRT 2.3.0 AARs declare `minSdk` 24.\n- The dependencies in the app-level `build.gradle.kts`, from Google Maven: `implementation(\"com.google.ai.edge.litert:litert:2.3.0\")` and `implementation(\"com.google.ai.edge.litert:litert-gpu:2.3.0\")`. From 2.3.0 the GPU accelerator is the second artifact: with `litert` alone the app builds, and `CompiledModel.create` with `Accelerator.GPU` throws `LiteRtException` because the APK has no GPU accelerator library (logcat: `GPU accelerator could not be loaded and registered`). No manifest entry is needed: `litert-api`, which `litert` depends on, declares the OpenCL driver libraries (`uses-native-library`) in its own manifest.\n- A `.tflite` model and its input requirements (input size, mean/std, channel order). Models with a LiteRT recipe: https://github.com/google-ai-edge/litert-samples/blob/main/models/README.md\n\n## Detailed steps\n\n### 1. Set up the project and place the model\n\nIn the app-level `build.gradle.kts`, set `minSdk = 24` in `defaultConfig`. Put the model at `app/src/main/assets/model.tflite` (AGP stores `.tflite` files uncompressed by default). A model too large for the APK is delivered by Play for On-device AI (beta) as an AI pack, or downloaded by the app into `context.filesDir`: https://developer.android.com/google/play/on-device-ai (its bundletool `--local-testing` installs the packs without the store). An install-time AI pack is read through the `AssetManager`, as that page shows. A fast-follow or on-demand pack and a downloaded file are loaded from their path with `CompiledModel.create(filePath, CompiledModel.Options(accelerator), environment)` in place of the asset call in step 2. The pack's directory comes from the AI Delivery library (`com.google.android.play:ai-delivery`): `AiPackManagerFactory.getInstance(context).getPackLocation(name)?.assetsPath()`, null until the pack has downloaded.\n\nWith 2.3.0 nothing is added to `gradle.properties`: `litert` 2.3.0 declares the namespace `com.google.ai.edge.litert.impl` and its dependency `litert-api` 2.3.0 declares `com.google.ai.edge.litert`, so AGP 9.x builds the project. With 2.2.0 or 2.1.6 both libraries declare `com.google.ai.edge.litert`, and AGP 9.x stops at `processDebugMainManifest` (https://github.com/google-ai-edge/LiteRT/issues/8474; AGP 8.x reports it as a warning and builds); for those two versions add `android.uniquePackageNames=false` to `gradle.properties`, and remove it when moving to 2.3.0, because it also hides the same clash between any other two libraries. Excluding `litert-api` instead does not work: `CompiledModel` and `Accelerator` are in it, and the build then fails at `compileDebugKotlin`.\n\n### 2. Write the inference class\n\n```kotlin\nimport android.content.Context\nimport com.google.ai.edge.litert.Accelerator\nimport com.google.ai.edge.litert.CompiledModel\nimport com.google.ai.edge.litert.Environment\nimport com.google.ai.edge.litert.TensorBuffer\n\nconst val INPUT_SIZE = 224 * 224 * 3\nprivate val environment by lazy { Environment.create() }\n\nclass Classifier(context: Context, accelerator: Accelerator) : AutoCloseable {\n    private val model = CompiledModel.create(context.assets, \"model.tflite\", CompiledModel.Options(accelerator), environment)\n    private val inputs = mutableListOf<TensorBuffer>()\n    private val outputs = mutableListOf<TensorBuffer>()\n\n    init {\n        try {\n            inputs += model.createInputBuffers()\n            outputs += model.createOutputBuffers()\n            infer(FloatArray(INPUT_SIZE))\n        } catch (e: Exception) {\n            close()\n            throw e\n        }\n    }\n\n    fun infer(input: FloatArray): FloatArray {\n        inputs[0].writeFloat(input)\n        model.run(inputs, outputs)\n        return outputs[0].readFloat()\n    }\n\n    override fun close() {\n        (inputs + outputs).forEach { it.close() }\n        model.close()\n    }\n}\n```\n\n`Accelerator.CPU` needs nothing from the device. `Accelerator.GPU` compiles the graph for the GPU; when the GPU cannot take the model, the constructor throws `LiteRtException`: from `create` for an op the GPU does not support, and on the Android emulator (API 36, arm64) from `create` or from the buffers. A missing asset and a file that is not a model also throw `LiteRtException` from `create`. The buffers are created once with the model, reused for every inference and closed before the model. One `Environment` serves every model in the process and stays open, so LiteRT loads the GPU accelerator library once and not on every `create`. The constructor ends with one inference as the warm-up (the first GPU run includes shader compilation) and closes the model and its buffers if any step throws. `INPUT_SIZE` is the model's input element count: `writeFloat` throws on a longer array and writes a shorter one without an error, so a wrong size shows up as a wrong result.\n\n### 3. Wire a ViewModel and the screen\n\n```kotlin\ndata class UiState(val ready: Boolean = false, val result: List<Float>? = null, val error: String? = null)\n\nclass MainViewModel(app: Application) : AndroidViewModel(app) {\n    private val executor = Executors.newSingleThreadExecutor()\n    private val scope = CoroutineScope(SupervisorJob() + executor.asCoroutineDispatcher())\n    private var classifier: Classifier? = null\n    private val _uiState = MutableStateFlow(UiState())\n    val uiState: StateFlow<UiState> = _uiState.asStateFlow()\n\n    fun load(accelerator: Accelerator = Accelerator.CPU) {\n        scope.launch {\n            if (classifier != null) return@launch\n            try {\n                classifier = Classifier(getApplication<Application>(), accelerator)\n                _uiState.value = UiState(ready = true)\n            } catch (e: LiteRtException) {\n                _uiState.value = UiState(error = e.message)\n            }\n        }\n    }\n\n    fun classify(input: FloatArray) {\n        scope.launch {\n            try {\n                classifier?.infer(input)?.let { result -> _uiState.update { it.copy(result = result.toList(), error = null) } }\n            } catch (e: LiteRtException) {\n                _uiState.update { it.copy(error = e.message) }\n            }\n        }\n    }\n\n    override fun onCleared() {\n        scope.launch { classifier?.close() }.invokeOnCompletion { scope.cancel() }\n        executor.shutdown()\n    }\n}\n```\n\nOne single-thread executor owns the model: create, run and close happen only on it, never on the main thread. The ViewModel keeps its own scope because `viewModelScope` is cancelled before `onCleared()` runs, so a close launched there would not run; `onCleared()` cancels the scope once the close is done. `load()` does nothing when a model is already loaded, so the screen can call it again after a rotation; a `load()` that fails leaves `classifier` `null`. `classify()` reports a `LiteRtException` (package `com.google.ai.edge.litert`) in `error` and keeps the model. The screen below takes the picture (`bitmap`) from the photo picker or CameraX and runs `preprocess()` in the click handler (a few milliseconds for 224 by 224; move it onto the executor for bigger pictures); it does not fall back on its own: if `load(Accelerator.GPU)` ends in `error`, call `load(Accelerator.CPU)`. `viewModel()` and `collectAsStateWithLifecycle()` come from `androidx.lifecycle:lifecycle-viewmodel-compose` and `androidx.lifecycle:lifecycle-runtime-compose` (2.10.0); every import the code blocks need is listed in [imports](references/imports.md):\n\n```kotlin\n@Composable\nfun MainScreen(bitmap: Bitmap, viewModel: MainViewModel = viewModel()) {\n    val state by viewModel.uiState.collectAsStateWithLifecycle()\n    LaunchedEffect(Unit) { viewModel.load(Accelerator.GPU) }\n    Column {\n        state.error?.let { Text(it) }\n        Button(onClick = { viewModel.classify(preprocess(bitmap)) }, enabled = state.ready) { Text(\"Run\") }\n        state.result?.let { Text(\"Top class: ${it.indices.maxBy { i -> it[i] }}\") }\n    }\n}\n```\n\n### 4. Preprocess by the model's input requirements\n\nTurn the bitmap into the model's input with [`preprocess()`](references/preprocess.md). Use the model's own size, mean/std, channel order and layout (that example is NHWC, RGB, scaled to -1..1); a wrong mean/std looks exactly like a broken model. An audio or embedding model replaces `preprocess()` with its own input encoding and keeps the inference class.\n\n### 5. Run on a device and check the output\n\nRun the app on a physical device from Android Studio. Compare the app's output with the model's reference output for one fixed input, on the CPU first and then on the GPU; follow [verification](references/verify.md). Moving an existing Interpreter-API app to the CompiledModel API is a separate skill: https://github.com/google-ai-edge/litert-samples/blob/main/skills/litert-compiled-model-migration/SKILL.md\n","tagline":"Creates an Android app that runs a .tflite model on the CPU or the GPU with the LiteRT CompiledModel API in Kotlin. 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