# Run a LiteRT model on supported runtimes ## LiteRT overview Lite Runtime (LiteRT) is an open-source framework for efficient on-device deep learning inference. TensorFlow provides tools to convert pretrained models (SavedModel or Keras) into the LiteRT format and optimize them for edge deployment. On Qualcomm® Linux®, LiteRT models can be run using the native LiteRT application or through the GStreamer-based Qualcomm Intelligent Multimedia SDK. The framework supports execution on multiple hardware accelerators—CPU, GPU, and Qualcomm Hexagon™ Tensor Processor—via delegates, enables running and benchmarking sample applications, and is optimized for low latency, small model size, and low power consumption on mobile, embedded, and edge devices. The following figure shows the delegates the LiteRT framework uses to run models: ![../_images/litert-arch.png](data:image/png;base64,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) **LiteRT architecture** ## LiteRT on-device inference The LiteRT on-device inference loads the model into an interpreter, which parses the model and uses a delegate to run it. The process includes the following: 1. The inference loads the LiteRT model into a LiteRT interpreter interface, which parses the model to identify the neural network operators present in it. 2. The interpreter interface is further configured to run the model using a delegate. 3. The interpreter invokes a model inference on the provided inputs and saves the corresponding outputs into the buffers provided to the interpreter interface. Qualcomm supports running LiteRT models on the following accelerators using delegates: - CPU - Adreno GPU - NPU The following table lists the delegates and their accelerators: Supported delegates and accelerators | **Delegate** | **Acceleration** | | --- | --- | | XNNPACK delegate | CPU | | GPU delegate | GPU | | Qualcomm^®^ AI Engine direct delegate (QNN delegate) | CPU, GPU, and NPU | ## Next steps - [Run a LiteRT model on CPU](https://docs.qualcomm.com/doc/80-80022-15B/topic/run-litert-model-on-cpu.html) - [Run LiteRT Model on GPU](https://docs.qualcomm.com/doc/80-80022-15B/topic/run-litert-model-on-gpu.html) - [Run LiteRT Model on NPU](https://docs.qualcomm.com/doc/80-80022-15B/topic/run-litert-model-on-npu.html) Last Published: Jun 23, 2026 [Previous Topic Use available frameworks and runtimes](https://docs.qualcomm.com/bundle/publicresource/80-80022-15B/topics/use-available-frameworks-and-runtimes.md) [Next Topic Supported LiteRT runtimes](https://docs.qualcomm.com/bundle/publicresource/80-80022-15B/topics/run-a-litert-model.md)