Litert Runtime

This 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; source repository) on the CPU or the GPU, with the runtime bundled in the APK or from Google Play services. Don't use the deprecated org.tensorflow.lite.Interpreter API. For open text language models using .litertlm files, suggest the litert-lm skill.

Prerequisites

• A Kotlin Android project (minSdk must be 24 or higher, as declared by the LiteRT 2.3.0 AARs).

• The runtime that Step 1 picks: the Google Play services runtime (com.google.android.gms:play-services-litert, the default; see LiteRT in Google Play services), or the bundled runtime (implementation("com.google.ai.edge.litert:litert:2.3.0") and implementation("com.google.ai.edge.litert:litert-gpu:2.3.0") from Google Maven). For the bundled runtime, from 2.3.0 the GPU accelerator is the second artifact: with litert alone the app builds, and CompiledModel.create with CompiledModel.Options(Accelerator.GPU) throws LiteRtException because the APK has no GPU accelerator library. With CompiledModel.Options(Accelerator.GPU, Accelerator.CPU) it doesn't throw: the model runs on the CPU while the app reports the GPU, and only Logcat shows it (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.

• A verified .tflite model and its input contract (input dimensions, mean, standard deviation, channel order, and tensor layout). For models with a verified recipe, inspect the LiteRT model recipes.

Critical mandates

• Single-thread confinement : One single-thread executor or confined coroutine dispatcher must own the CompiledModel instance. Creation, inference, and closing must execute on that same background thread, never on the main thread.

• Buffer lifecycle : Allocate TensorBuffer instances once using createInputBuffers and createOutputBuffers, reuse them across inferences, and close all input and output buffers before closing the CompiledModel.

• Synchronous timing : model.run enqueues work on the GPU and returns before GPU execution completes. Always measure model.run and output buffer readback (outputs[0].readFloat) together.

• Warm-up and exception safety : Execute one warm-up inference inside the helper constructor's init block so first-run GPU shader compilation doesn't block the first user interaction, and call close in catch before rethrowing so neither CompiledModel nor TensorBuffer handles leak if buffer allocation or warm-up throws.

• Runtime choice : Before Step 1, you must read LiteRT in Google Play services and choose the runtime. Use the Google Play services runtime unless the app needs the NPU, custom operators, or a Support library class that this skill can't replace (such as MetadataExtractor), or also targets devices without Google Play services. Never put both runtimes in the same build variant.

• Migration : When the app already uses org.tensorflow.lite code, you must read the "Migrating from legacy TFLite Interpreter" section in full before you edit any file. Migrated code must not import any org.tensorflow.lite class (including DataType), every legacy GPU choice (GpuDelegate, or the Support library's Model.Device.GPU) becomes CompiledModel.Options(Accelerator.GPU, Accelerator.CPU), and an NNAPI choice maps to that same GPU option.

Detailed steps

Step 1: Configure Gradle and place the model file

Search the project for libs.versions.toml, gradle.properties, app/build.gradle.kts, and any existing org.tensorflow.lite imports before editing build files. Then pick the runtime:

• Google Play services runtime (default) : Use it unless one of the bundled runtime cases applies, also when the request doesn't name a runtime. You must read LiteRT in Google Play services now: its dependencies replace the ones in this step, and its Environment replaces the environment property of Step 2. Use version 17.0.0 exactly, and run ./gradlew assembleDebug (not --dry-run) after you add it. Switch to the bundled runtime only when that build reports Could not find com.google.android.gms:play-services-litert:17.0.0, and tell the user, also when the user asked for Google Play services. Never leave the build broken.

• Bundled runtime : Use it only when the app needs the NPU, custom operators, or a Support library class that this skill can't replace (such as MetadataExtractor), when the app also targets devices without Google Play services, or when the user asks for it. FileUtil, ImageProcessor, TensorImage, and TensorLabel don't count: replace them as LiteRT in Google Play services describes. For the bundled runtime, configure minSdk and add the LiteRT runtime and GPU accelerator dependencies in app/build.gradle.kts:

  android { defaultConfig { minSdk = 24 } }

  dependencies { implementation("com.google.ai.edge.litert:litert:2.3.0") implementation("com.google.ai.edge.litert:litert-gpu:2.3.0") }

Place bundled models at app/src/main/assets/model.tflite (AGP stores .tflite files uncompressed by default). Deliver a model that exceeds the base APK size limit through Play for On-Device AI as an AI pack, or download it into context.filesDir (bundletool --local-testing installs AI packs locally without Google Play). Read an install-time AI pack through AssetManager. Load a fast-follow or on-demand AI pack or a downloaded file from its on-disk filePath using CompiledModel.create(filePath, CompiledModel.Options(accelerator), environment) in place of the asset call in Step 2. Obtain the pack's directory from the AI Delivery library (com.google.android.play:ai-delivery) using AiPackManagerFactory.getInstance(context).getPackLocation(name)?.assetsPath(), which returns null until the pack has finished downloading.

With 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 (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 doesn't work: CompiledModel and Accelerator are in litert-api, and the build then fails at compileDebugKotlin.

Step 2: Implement the inference helper class

Isolate CompiledModel and its TensorBuffer instances in a dedicated AutoCloseable helper class free of Android UI types:

import android.content.Context
import com.google.ai.edge.litert.Accelerator
import com.google.ai.edge.litert.CompiledModel
import com.google.ai.edge.litert.Environment
import com.google.ai.edge.litert.TensorBuffer

const val INPUT_SIZE = 224 * 224 * 3
private val environment by lazy { Environment.create() }

class Classifier(context: Context, accelerator: Accelerator) : AutoCloseable {
    private val model = CompiledModel.create(
        context.assets,
        "model.tflite",
        CompiledModel.Options(accelerator),
        environment
    )
    private val inputs = mutableListOf<TensorBuffer>()
    private val outputs = mutableListOf<TensorBuffer>()

    init {
        try {
            inputs += model.createInputBuffers()
            outputs += model.createOutputBuffers()
            infer(FloatArray(INPUT_SIZE))
        } catch (e: Exception) {
            close()
            throw e
        }
    }

    fun infer(input: FloatArray): FloatArray {
        inputs[0].writeFloat(input)
        model.run(inputs, outputs)
        return outputs[0].readFloat()
    }

    override fun close() {
        (inputs + outputs).forEach { it.close() }
        model.close()
    }
}

Accelerator.CPU requires nothing from the device. Accelerator.GPU compiles the graph for the GPU; when the GPU can't execute the model, the constructor throws LiteRtException (from create for an operator the GPU doesn't support, and on the Android emulator from create or from buffer allocation). A missing asset or a file that isn't a valid model also throws 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 rather than on every create call. The constructor ends with one warm-up inference (the first GPU run includes shader compilation) and closes the model and its buffers if any step in init 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 produces an incorrect result. For int8 or uint8 tensors, use writeInt8 and readInt8 on TensorBuffer instead of writeFloat and readFloat.

When memory matters, use the following options:

• Load from a path : create(context.assets, ...) keeps a copy of the asset's bytes in the native heap while the model is open. Copy the asset once into context.noBackupFilesDir (a downloaded or AI pack file is already on disk) and call create(filePath, ...) so the file is mapped and reclaimable. Version the staged filename, bump the version when the bundled model changes, and delete the previous version's file. Copy to a temporary file and rename it so a copy interrupted by process death is never loaded:

  import android.content.Context import com.google.ai.edge.litert.Accelerator import com.google.ai.edge.litert.CompiledModel import java.io.File

  fun stageModelAsset( context: Context, assetName: String = "model.tflite", version: String = "v1" ): File = File(context.noBackupFilesDir, "$version-$assetName").also { file -> if (!file.isFile) { context.noBackupFilesDir.listFiles()?.forEach { if (it.name.endsWith("-$assetName")) it.delete() } val tmp = File("${file.path}.tmp") context.assets.open(assetName).use { input -> tmp.outputStream().use { output -> input.copyTo(output) } } check(tmp.renameTo(file)) } }

  fun memoryOptions( context: Context, accelerator: Accelerator = Accelerator.CPU, assetName: String = "model.tflite", version: String = "v1" ): CompiledModel.Options = CompiledModel.Options(accelerator).apply { val prefix = "$assetName-" val cache = File(context.codeCacheDir, "$prefix$version.xnnpack_cache") context.codeCacheDir.listFiles()?.forEach { if (it.name.startsWith(prefix) && it.name.endsWith(".xnnpack_cache") && it != cache) { it.delete() } } cpuOptions = CompiledModel.CpuOptions( numThreads = 4, xnnPackFlags = 0x23, xnnPackWeightCachePath = cache.path ) }

• Weight cache (xnnPackWeightCachePath): Written on the first load (~1x an FP32 or ~2x an FP16 model's file size) and mapped on later loads, moving packed FP32 weights off the heap. Version the cache filename and delete outdated caches.

• Transient indirection buffer (xnnPackFlags = 0x23) : Default 0x3 plus flag 0x20, replacing per-operator convolution pointer tables with one shared buffer (byte-identical outputs, ~1% latency overhead at 4 threads, ~3% single-threaded). Because the value replaces the default, 0x20 alone leaves int8 and uint8 operators to fallback kernels. cpuOptions is ignored on the GPU.

• Close after a task: Closing frees the runtime workspace; recreating with a warm cache takes ~20 ms, whereas the first inference after any load pays a full warm-up run, so close when the next use isn't imminent.

Step 3: Wire the ViewModel and Compose screen

Drive the helper from a single-thread executor inside an AndroidViewModel, and fall back to Accelerator.CPU when GPU compilation fails:

import android.app.Application
import androidx.lifecycle.AndroidViewModel
import com.google.ai.edge.litert.Accelerator
import com.google.ai.edge.litert.LiteRtException
import java.util.concurrent.Executors
import kotlinx.coroutines.CoroutineScope
import kotlinx.coroutines.SupervisorJob
import kotlinx.coroutines.asCoroutineDispatcher
import kotlinx.coroutines.cancel
import kotlinx.coroutines.flow.MutableStateFlow
import kotlinx.coroutines.flow.StateFlow
import kotlinx.coroutines.flow.asStateFlow
import kotlinx.coroutines.flow.update
import kotlinx.coroutines.launch

data class UiState(
    val ready: Boolean = false,
    val accelerator: Accelerator = Accelerator.CPU,
    val result: List<Float>? = null,
    val error: String? = null
)

class MainViewModel(app: Application) : AndroidViewModel(app) {
    private val executor = Executors.newSingleThreadExecutor()
    private val scope = CoroutineScope(
        SupervisorJob() + executor.asCoroutineDispatcher()
    )
    private var classifier: Classifier? = null
    private val _uiState = MutableStateFlow(UiState())
    val uiState: StateFlow<UiState> = _uiState.asStateFlow()

    fun load(preferred: Accelerator = Accelerator.GPU) {
        scope.launch {
            if (classifier != null) return@launch
            val appContext = getApplication<Application>()
            val selected = try {
                classifier = Classifier(appContext, preferred)
                preferred
            } catch (e: LiteRtException) {
                if (preferred != Accelerator.CPU) {
                    try {
                        classifier = Classifier(appContext, Accelerator.CPU)
                        Accelerator.CPU
                    } catch (cpuError: LiteRtException) {
                        _uiState.value = UiState(error = cpuError.message)
                        return@launch
                    }
                } else {
                    _uiState.value = UiState(error = e.message)
                    return@launch
                }
            }
            _uiState.value = UiState(ready = true, accelerator = selected)
        }
    }

    fun classify(input: FloatArray) {
        scope.launch {
            try {
                classifier?.infer(input)?.let { result ->
                    _uiState.update {
                        it.copy(result = result.toList(), error = null)
                    }
                }
            } catch (e: LiteRtException) {
                _uiState.update { it.copy(error = e.message) }
            }
        }
    }

    override fun onCleared() {
        scope.launch { classifier?.close() }.invokeOnCompletion {
            scope.cancel()
        }
        executor.shutdown()
    }
}

One single-thread executor owns the model: creation, inference, and closing execute only on it, never on the main thread. The ViewModel keeps its own scope because viewModelScope is canceled before onCleared runs, so a close launched there wouldn't run; onCleared cancels scope once close completes. load is a no-op when a model is already loaded across configuration changes; a failed load leaves classifier null. classify reports any LiteRtException (in package com.google.ai.edge.litert) in error and keeps the model.

The following Compose screen takes the picture (bitmap) from the photo picker or CameraX and runs preprocess in the click handler (a few milliseconds for 224x224; move preprocess onto the executor for images exceeding 224x224). viewModel and collectAsStateWithLifecycle come from androidx.lifecycle:lifecycle-viewmodel-compose and androidx.lifecycle:lifecycle-runtime-compose:

import android.graphics.Bitmap
import androidx.compose.foundation.layout.Column
import androidx.compose.material3.Button
import androidx.compose.material3.Text
import androidx.compose.runtime.Composable
import androidx.compose.runtime.LaunchedEffect
import androidx.compose.runtime.getValue
import androidx.lifecycle.compose.collectAsStateWithLifecycle
import androidx.lifecycle.viewmodel.compose.viewModel
import com.google.ai.edge.litert.Accelerator

@Composable
fun MainScreen(
    bitmap: Bitmap,
    viewModel: MainViewModel = viewModel()
) {
    val state by viewModel.uiState.collectAsStateWithLifecycle()
    LaunchedEffect(Unit) { viewModel.load(Accelerator.GPU) }
    Column {
        state.error?.let { Text(it) }
        Button(
            onClick = { viewModel.classify(preprocess(bitmap)) },
            enabled = state.ready
        ) {
            Text("Run (${state.accelerator})")
        }
        state.result?.let { scores ->
            Text("Top class: ${scores.indices.maxBy { i -> scores[i] }}")
        }
    }
}

Step 4: Preprocess inputs to match the model contract

Convert the Bitmap into the model's input with preprocess. Match the model's exact input dimensions, normalization mean and standard deviation, channel order, and memory layout (this example uses NHWC, RGB, scaled to -1.0..1.0). Mismatched mean or std values produce corrupted predictions that look like a broken model:

import android.graphics.Bitmap

fun preprocess(
    bitmap: Bitmap,
    size: Int = 224,
    mean: Float = 127.5f,
    std: Float = 127.5f
): FloatArray {
    val scaled = checkNotNull(
        Bitmap.createScaledBitmap(bitmap, size, size, true)
            .copy(Bitmap.Config.ARGB_8888, false)
    )
    val pixels = IntArray(size * size).also {
        scaled.getPixels(it, 0, size, 0, 0, size, size)
    }
    val out = FloatArray(size * size * 3)
    pixels.forEachIndexed { i, p ->
        out[i * 3] = ((p shr 16 and 0xFF) - mean) / std
        out[i * 3 + 1] = ((p shr 8 and 0xFF) - mean) / std
        out[i * 3 + 2] = ((p and 0xFF) - mean) / std
    }
    return out
}

The scaled bitmap is copied to Bitmap.Config.ARGB_8888 because ImageDecoder returns Bitmap.Config.HARDWARE bitmaps by default on Android 9 and higher (including from a photo picker Uri), scaling preserves that config, and getPixels can't read HARDWARE bitmaps; the copy is of the 224x224 scaled bitmap rather than the full image. Scaling doesn't preserve the aspect ratio; crop first if the model requires center cropping. An audio or embedding model replaces preprocess with its own input encoding and keeps the inference class.

Step 5: Verify output on a physical device

Run the app on a physical device that runs Android from Android Studio. Compare the app's output with the model's reference output for one fixed input on Accelerator.CPU first and then on Accelerator.GPU; when verifying model outputs on a physical device, you must follow device verification.

Migrating from legacy TFLite Interpreter

An existing app on org.tensorflow.lite.Interpreter (from org.tensorflow:tensorflow-lite* or from com.google.ai.edge.litert:litert 1.x) moves to the CompiledModel code of Steps 1 to 5 in seven steps, in this order. Before you change any file of an existing app, you must read migration, which gives the commands and code for each step. An app on the Interpreter API in Google Play services (play-services-tflite-java) follows LiteRT in Google Play services instead:

1. Find the runtime: Read the build files and the resolved dependencies, and note the delegate, the thread count, and the Support library classes that the app uses.

2. Capture a golden output first : While the legacy runtime is still in the project, record the app's output for one fixed input on the CPU. This step needs a device or an emulator. Run the capture test with am instrument, not connectedDebugAndroidTest, which uninstalls the app and deletes the file.

3. Swap the dependencies : Delete every legacy artifact line (never leave it commented out) and add the dependencies of the runtime that Step 1 picks: by default those of LiteRT in Google Play services, with the same fallback to the bundled runtime when Gradle can't find play-services-litert. On the Play services runtime, replace the Support library classes as LiteRT in Google Play services describes. On the bundled runtime, an app keeps them with com.google.ai.edge.litert:litert-support:2.3.0, with the package org.tensorflow.lite.support renamed to com.google.ai.edge.litert.support (LiteRT Support migration guide).

4. Replace the delegates : GpuDelegate becomes CompiledModel.Options(Accelerator.GPU, Accelerator.CPU), so the CPU runs the operators that the GPU doesn't support. An NNAPI choice in the app maps to the same GPU option, not to Accelerator.CPU alone or to an NPU accelerator. setNumThreads(n) becomes new CPU options with cpuOptions = CompiledModel.CpuOptions(numThreads = n).

5. Rewrite the inference code : Replace Interpreter and FileUtil.loadMappedFile with the Classifier helper of Step 2, created and run only on the single-thread executor of Step 3, never in a constructor that the main thread calls. When no caller retries on the CPU as in Step 3, create the helper's GPU option with the CPU as a second accelerator (CompiledModel.Options(Accelerator.GPU, Accelerator.CPU)). An interpreter.runSignature(inputs, outputs, key) call becomes the helper's buffers and run call with the signature key, as follows. This form runs on both runtimes, also after the app changes runtime or upgrades LiteRT; when the signature has more than one input or output, you must follow the signature rules in migration:

   Copy

   inputs += model.createInputBuffers("serving_default")
   outputs += model.createOutputBuffers("serving_default")
   model.run(inputs, outputs, "serving_default")
   

6. Verify in two stages: The build passes and the searches for legacy references print nothing; then the migrated output matches the golden output on a device.

7. Report what changed and what didn't migrate : When the app used NNAPI, tell the user that it now runs on the GPU with the CPU as a second accelerator, which changes the speed and the numerical results of a quantized model that ran on a DSP or an NPU before; point them to NPU acceleration with LiteRT for the NPU. Tell the user about the TensorFlow Lite Task Library, C++ modules, and Select TensorFlow operators, which this skill doesn't migrate.

Migration mandates that apply to every migrated app:

• No org.tensorflow.lite imports : Migrated code must not import any org.tensorflow.lite class, including org.tensorflow.lite.DataType; the LiteRT artifacts keep these classes only for backward compatibility. Replace Support calls that need DataType (TensorImage(DataType.FLOAT32), TensorBuffer.createFixedSize, and TensorLabel) with preprocess of Step 4, the CompiledModel buffers, and labels paired with the output scores. Remove every existing org.tensorflow.lite import line, not only the ones that you add.

Antipatterns

• Never create, run, or close CompiledModel on the Android main thread.

• Never allocate new TensorBuffer instances on every inference call; failing to close per-frame TensorBuffer objects leaks C++ heap memory.

• Never benchmark model.run without including output buffer readback (outputs[0].readFloat).

• Never mix org.tensorflow:tensorflow-lite* artifacts (such as org.tensorflow:tensorflow-lite-support) with com.google.ai.edge.litert:* artifacts; both ship the org.tensorflow.lite.* classes and cause duplicate class build failures.

• Never use Accelerator.GPU on LiteRT 2.3.0 without adding the com.google.ai.edge.litert:litert-gpu:2.3.0 dependency alongside com.google.ai.edge.litert:litert:2.3.0; with the CPU as a second accelerator, the model silently runs on the CPU.

• Never set xnnPackFlags = 0x20 on its own to get the transient indirection buffer; the value replaces the runtime's default 0x3, so pass 0x23.

Checklist

• [ ] Is minSdk set to 24 or higher and are both com.google.ai.edge.litert:litert:2.3.0 and com.google.ai.edge.litert:litert-gpu:2.3.0 added in app/build.gradle.kts?

• [ ] Are all legacy org.tensorflow:tensorflow-lite* dependencies and org.tensorflow.lite.* imports removed?

• [ ] Do CompiledModel.create, model.run, and close all execute on the same single-thread background dispatcher?

• [ ] Are TensorBuffer instances allocated once, reused across calls, and closed before model.close, including when init or warm-up throws?

• [ ] Does load execute one warm-up inference and fall back to Accelerator.CPU if Accelerator.GPU throws LiteRtException?

• [ ] Does the project compile with ./gradlew assembleDebug?

• [ ] If memory matters, is the model created from a path with a versioned weight cache and xnnPackFlags = 0x23 on the CPU, and closed after a task when the next use isn't imminent?