> ## Documentation Index
> Fetch the complete documentation index at: https://dragonwingdocs.qualcomm.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Run inference

> Run a prepared AI model on Qualcomm Dragonwing IoT platforms. Choose the runtime or integration path: LiteRT, the QAIRT SDK C++ APIs, or the Qualcomm IM SDK.

This is the **run-inference** step of the [Choose your journey](../topic/ai-ml-developer-workflow) flow. After your model is [prepared](../map/compile-and-optimize-model), choose how to execute it on the device. The right path depends on your language, model format, and whether you are building a full camera/video pipeline. All runtimes can target the Qualcomm Kryo CPU, Adreno GPU, and Hexagon NPU (HTP).

Select the runtime that matches your application.

<CardGroup cols={2}>
  <Card title="LiteRT" icon="layer-group" href="../topic/litert-overview">
    High-performance on-device inference from Python or C++ using Qualcomm AI Engine Direct delegates. Best for quick, Python-friendly workflows.
  </Card>

  <Card title="QAIRT SDK C++ APIs" icon="code" href="../topic/develop-your-own-application-qairt-cpp">
    Low-level C++ control over model execution and the inference backend (QNN or SNPE).
  </Card>

  <Card title="Qualcomm IM SDK" icon="video" href="../topic/develop-your-own-application-im-sdk">
    Build real-time camera, video, and vision pipelines with GStreamer: zero-copy buffers and GPU pre/post-processing. Use it for any AI media pipeline.
  </Card>
</CardGroup>

<Note>
  Building a robotics application? The [Qualcomm Intelligent Robotics (QIR) SDK](https://www.thundercomm.com/rubik-pi-3/en/docs/rubik-pi-3-user-manual/1.0.0-u/Application%20Development%20and%20Execution%20Guide/Robotics-Sample-Applications/Robotics%20Sample%20Applications/) adds ROS-based modules and hardware-accelerated nodes on top of these runtimes.
</Note>