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.

litert-runtime

Prerequisites

Critical mandates