# Overview
This section describes AI application development using tools,
runtimes, and frameworks supported on Qualcomm platforms. This document is useful for
anyone who intends to train models, fine tune models for deployment, and build AI
applications for use on Qualcomm platforms.
This document covers the following topics:
1. Running inference using LiteRT or QAIRT.
2. Preparing models for deployment using Qualcomm AI Hub, Edge Impulse, or
Qualcomm AI Runtime SDK (QAIRT).
3. Integrating models into deployable applications using Qualcomm Intelligent
Multimedia SDK (IM SDK) or QAIRT.
You can bring pretrained models from ONNX, PyTorch, TensorFlow, or LiteRT and run them
efficiently on Qualcomm platforms. Qualcomm supports multiple AI inference accelerators,
such that AI inference can run on NPUs, CPUs, and GPUs.
## AI architecture
## AI software
Use the following software frameworks, libraries, and tools to
deploy AI workloads. Pretrained models (except for LiteRT models)
need to be converted to an executable format with the selected SDK
before running them. LiteRT models can be run directly using TFLite
Delegate.
AI workflow software and frameworks
| Software | Description |
| --- | --- |
| [Qualcomm AI Hub](https://docs.qualcomm.com/doc/80-70029-15B/topic/ai-hub.html) | Qualcomm AI Hub simplifies on-device AI development by providing tools, runtimes, and a library of
preoptimized models. You can also bring your own model and data for optimization before deploying to
Qualcomm platforms. |
| [LiteRT](https://docs.qualcomm.com/doc/80-70029-54/) | LiteRT enables high-performance, on-device AI. Use LiteRT to natively run quantized models
(Python or C++) on CPU and NPU using Qualcomm AI Engine Direct delegates. |
| [Edge Impulse](https://docs.qualcomm.com/doc/80-70029-15B/topic/edge-impulse.html) | Use Edge Impulse to build and train AI models using audio, image, and other sensor data or bring your
own model in a variety of formats. |
| [Qualcomm AI Runtime SDK (QAIRT)](https://docs.qualcomm.com/doc/80-70029-15B/topic/qairt.html) | QAIRT SDK is a unified software package that integrates Qualcomm AI tools, including AI Engine Direct and
Neural Processing SDK. It enables developers to port and deploy AI models on Qualcomm hardware accelerators
(CPU, GPU, NPU) and supports popular frameworks such as TensorFlow, PyTorch, LiteRT, and ONNX for efficient
model execution across Snapdragon platforms. |
| [Qualcomm AI Model Efficiency Toolkit (AIMET)](https://quic.github.io/aimet-pages/releases/latest/index.html) | AIMET is an open-source library used to optimize deep learning models for edge devices.
It provides advanced techniques like quantization and compression to reduce model size
and improve performance without sacrificing accuracy. |
| [Qualcomm® GenAI Inference Engine (Genie)](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-10/index_Genie.html) | Use Genie to orchestrate AI microservices and multimodal workflows using Qualcomm AI runtimes. |
| [Qualcomm Intelligent Multimedia SDK (IM SDK)](https://docs.qualcomm.com/doc/80-70029-15B/topic/develop-your-own-application-im-sdk.html) | Use IM SDK to build high-performance multimedia and AI vision pipelines on Qualcomm
platforms. It includes GStreamer plugins, AI runtime integration, and messaging support to accelerate
development for robotics, surveillance, and embedded AI. |
| [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/) | QIR SDK is a robotics-focused software development kit for Qualcomm Linux platforms. It includes
ROS-based modules, hardware-accelerated nodes, and cross-compilation tools for building intelligent
robotic systems. |
## AI hardware
- **Qualcomm Kryo™ CPU**: Best-in-class CPU with high performance and
remarkable power efficiency.
- **Qualcomm Adreno GPU**: Suitable to run AI workloads with
balanced power and performance. AI workloads are accelerated with
OpenCL kernels. The GPU can also be used to accelerate model
pre/postprocessing.
- **Qualcomm Hexagon Tensor Processor (HTP)**: Also known as
NPU/DSP/HMX, suitable to run AI workloads with low-power and
high-performance. For optimized performance, pretrained models need
be quantized to one of the supported precisions.
Important
- Ensure that the host computer uses Ubuntu 22.04.
- The commands in this document are compatible with Qualcomm Linux 1.8.
Verify your Qualcomm Linux release version by running the commands described in the
[development kit user guide](https://docs.qualcomm.com/doc/80-70029-251/topic/set_up_the_device.html#verify-the-qualcomm-linux-version).
If your release version isn’t 1.8, [update your software](https://docs.qualcomm.com/doc/80-70029-251/topic/upgrade-rb3gen2-software.html#upgrade-rb3gen2-software)
- Sample applications and AI procedures in this document are compatible with the
[supported versions](https://docs.qualcomm.com/doc/80-70029-51/topic/introduction.html#supported-component-versions).
Ensure you download the matching SDKs to your host computer before starting AI/ML development.
Last Published: Apr 02, 2026
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