# Does LiteRT Support On-Device Training? Current Capabilities and Future Roadmap > LiteRT currently offers inference-only runtime. Discover its capabilities and explore the planned roadmap for future on-device training support in this technical overview. - Repository: [google-ai-edge/LiteRT](https://github.com/google-ai-edge/LiteRT) - Tags: deep-dive - Published: 2026-03-13 --- **LiteRT does not currently support on-device training and functions strictly as an inference-only runtime, though the feature is actively planned for a future release according to the official project roadmap.** LiteRT (formerly TensorFlow Lite) serves as Google's high-performance runtime for on-device AI, but developers looking to implement **LiteRT on-device training** will find that the current architecture supports only inference operations. The `google-ai-edge/LiteRT` repository explicitly positions the framework as an inference engine optimized for CPU, GPU, and NPU acceleration, with training workflows required to occur off-device using standard TensorFlow or other frameworks. ## Current Architecture and Limitations ### Inference-Only Design Philosophy LiteRT's core architecture focuses exclusively on model execution rather than learning. According to the repository's [`README.md`](https://github.com/google-ai-edge/LiteRT/blob/main/README.md) (lines 23-27), LiteRT emphasizes "advanced GPU/NPU acceleration" and "runtime for on-device AI" without mentioning training capabilities. The API surface exposes only inference primitives such as `Interpreter` and `CompiledModel`, with no training-related symbols available in the public interface. ### Missing Training Components A training-capable runtime requires back-propagation, gradient computation, optimizer steps, and weight update mechanisms. The LiteRT source tree contains no implementation of these components. The [`tflite/g3doc/guide/index.md`](https://github.com/google-ai-edge/LiteRT/blob/main/tflite/g3doc/guide/index.md) file explicitly states: "Unsupported on-device training, however it is on our Roadmap" (lines 23-25), confirming the architectural gap. ## Official Documentation and Roadmap Status The LiteRT project maintains transparency regarding its current limitations. The official guide index located at [`tflite/g3doc/guide/index.md`](https://github.com/google-ai-edge/LiteRT/blob/main/tflite/g3doc/guide/index.md) directly addresses the question of **LiteRT on-device training** support, noting that while the feature is unsupported in current releases, it appears on the project's public roadmap. This documentation aligns with the repository's [`README.md`](https://github.com/google-ai-edge/LiteRT/blob/main/README.md), which describes LiteRT as an inference runtime optimized for edge deployment across diverse hardware targets including mobile CPUs, GPUs, and dedicated neural processing units. ## Practical Workflow for Model Deployment Since **LiteRT on-device training** is not available, developers must follow a three-stage workflow: off-device training, model conversion, and edge inference. ### Off-Device Training and Conversion First, train your model using standard TensorFlow or other deep learning frameworks. Once training completes, convert the saved model to the TensorFlow Lite format (`.tflite`) using the LiteRT conversion tools. This process optimizes the model for edge execution while stripping away training-specific operations. ### Inference Implementation Example The following Python example demonstrates loading a converted model using LiteRT bindings and executing inference. Note the absence of any training API: ```python import litert # Load the compiled model for inference only model = litert.CompiledModel.from_file("my_model.tflite") # Create interpreter and allocate tensors interpreter = model.create_interpreter() interpreter.allocate_tensors() # Prepare input data input_tensor = interpreter.get_input_tensor(0) input_tensor.set_data(my_input_data) # Execute inference interpreter.invoke() # Retrieve results output = interpreter.get_output_tensor(0).data ``` This code utilizes the `CompiledModel` and `Interpreter` classes exclusively for forward-pass execution, reflecting LiteRT's current inference-only architecture. ## Summary - LiteRT currently functions as an **inference-only runtime** with no support for on-device training operations. - The official documentation in [`tflite/g3doc/guide/index.md`](https://github.com/google-ai-edge/LiteRT/blob/main/tflite/g3doc/guide/index.md) explicitly states that on-device training is unsupported but planned for future releases. - Core API classes like `CompiledModel` and `Interpreter` expose only forward-pass primitives without back-propagation or optimizer capabilities. - Developers must train models off-device using TensorFlow, convert them to `.tflite` format, and deploy for inference using LiteRT's hardware-accelerated runtime. ## Frequently Asked Questions ### Does LiteRT support on-device training in any beta or experimental capacity? No. LiteRT does not currently support on-device training in any capacity, including beta or experimental features. The runtime architecture lacks the necessary components for gradient computation and weight updates required for training. ### What is the recommended workflow if I need to update my model based on user data? The recommended approach involves collecting data on the device, transmitting it to a server for off-device training with standard TensorFlow, converting the updated model to `.tflite` format, and pushing the new model to devices for inference via LiteRT. ### Will on-device training be added to LiteRT in the future? Yes. According to the official documentation in [`tflite/g3doc/guide/index.md`](https://github.com/google-ai-edge/LiteRT/blob/main/tflite/g3doc/guide/index.md), on-device training is explicitly listed on the LiteRT roadmap. However, no specific timeline or release version has been announced for this feature. ### Can I use TensorFlow Lite's legacy on-device training APIs with LiteRT? No. LiteRT represents a new runtime architecture that does not inherit TensorFlow Lite's experimental training APIs. LiteRT focuses exclusively on optimized inference execution across CPU, GPU, and NPU hardware targets.