qualcomm/ai-hub-apps

The Qualcomm® AI Hub apps are a collection of state-of-the-art machine learning models optimized for performance (latency, memory etc.) and ready to deploy on Qualcomm® devices.

452

stars

1

commits

Python

primary language

Sep 11, 2026

updated

deeplearning
demos
inference
inference-api
inference-engine
machine-learning
machinelearning
onnx
pytorch
qnn
tensorflow-lite

README

Qualcomm® AI Hub Apps

The Qualcomm® AI Hub Apps are a collection of sample apps and tutorials to help deploy machine learning models on Qualcomm® devices.

Each app is designed to work with one or more models from Qualcomm® AI Hub Models.

With this repository, you can...

  • Explore apps optimized for on-device deployment of various machine learning tasks.
  • View open-source app recipes for running Qualcomm® AI Hub Models on local devices.
  • Find tutorials for end-to-end workflows.

Overview

Supported runtimes

Supported Deployment Targets

  • Android 11 Red Velvet Cake & Newer, API v30+
  • Windows 11
  • Ubuntu 24.04+

Supported compute units

Chipsets supported for NPU Acceleration

Weight and activation type required for NPU Acceleration:

  • Floating Point: FP16 (All Snapdragon® chipsets with Hexagon® Architecture v69 or newer)
  • Integer : INT8 or INT16 (All Snapdragon® chipsets)

NOTE: Some of these apps will run without NPU acceleration on non-Snapdragon® chipsets.

Getting Started with Apps

  1. Search for your desired OS & app in the tables below.

  2. The README of the selected app will contain build & installation instructions.

Contributors: See CONTRIBUTING.md for dev setup, repo architecture, and how to add new apps.

Android App Directory

TaskLanguageInference APISpecial Tags
ChatAppJava/C++Genie SDKLLM, GenAI
Image ClassificationJavaTensorFlow Lite
Object DetectionJavaTensorFlow LiteOpenCV, Live Camera Feed
Semantic SegmentationJavaTensorFlow LiteOpenCV, Live Camera Feed
Super ResolutionJavaTensorFlow Lite
WhisperKit (Speech to Text)*VariousTensorFlow Lite
GenieX Chat AndroidKotlin/JavaGenieX

Windows App Directory

TaskLanguageInference APISpecial Tags
ChatAppC++Genie SDKLLM, GenAI
Image ClassificationC++ONNXOpenCV
Object DetectionC++ONNXOpenCV
Super ResolutionC++ONNXOpenCV
Whisper Speech-to-TextPythonONNX
Stable Diffusion Image GenerationPythonONNX
GenieX Chat Windows*GoGenieX
SAM3 Segmentation WindowsPythonONNX

Ubuntu App Directory

TaskLanguageInference APISpecial Tags
Hand Gesture RecognitionPythonTensorFlow LiteGStreamer
Posenet Pose EstimationPythonTensorFlow LiteGStreamer
YamNet Audio ClassificationPythonTensorFlow Lite
Portrait SegmentationPythonTensorFlow Lite

* Source available on GitHub; not included in the CLI release.

Tutorials Directory

TutorialTopic
LLM on-device deploymentExporting and deploying Large Language Model (LLM) using Genie SDK
LLM on-device deployment with GenieXRunning and exporting Large Language Models (LLMs) with the GenieX runtime

LICENSE

Qualcomm® AI Hub Apps is licensed under BSD-3. See the LICENSE file.

Contributors

qaihm-bot

1 commits

qualcomm/ai-hub-apps

The Qualcomm® AI Hub apps are a collection of state-of-the-art machine learning models optimized for performance (latency, memory etc.) and ready to deploy on Qualcomm® devices.

452

stars

1

commits

Python

primary language

Sep 11, 2026

updated

deeplearning
demos
inference
inference-api
inference-engine
machine-learning
machinelearning
onnx
pytorch
qnn
tensorflow-lite

README

Qualcomm® AI Hub Apps

The Qualcomm® AI Hub Apps are a collection of sample apps and tutorials to help deploy machine learning models on Qualcomm® devices.

Each app is designed to work with one or more models from Qualcomm® AI Hub Models.

With this repository, you can...

  • Explore apps optimized for on-device deployment of various machine learning tasks.
  • View open-source app recipes for running Qualcomm® AI Hub Models on local devices.
  • Find tutorials for end-to-end workflows.

Overview

Supported runtimes

Supported Deployment Targets

  • Android 11 Red Velvet Cake & Newer, API v30+
  • Windows 11
  • Ubuntu 24.04+

Supported compute units

Chipsets supported for NPU Acceleration

Weight and activation type required for NPU Acceleration:

  • Floating Point: FP16 (All Snapdragon® chipsets with Hexagon® Architecture v69 or newer)
  • Integer : INT8 or INT16 (All Snapdragon® chipsets)

NOTE: Some of these apps will run without NPU acceleration on non-Snapdragon® chipsets.

Getting Started with Apps

  1. Search for your desired OS & app in the tables below.

  2. The README of the selected app will contain build & installation instructions.

Contributors: See CONTRIBUTING.md for dev setup, repo architecture, and how to add new apps.

Android App Directory

TaskLanguageInference APISpecial Tags
ChatAppJava/C++Genie SDKLLM, GenAI
Image ClassificationJavaTensorFlow Lite
Object DetectionJavaTensorFlow LiteOpenCV, Live Camera Feed
Semantic SegmentationJavaTensorFlow LiteOpenCV, Live Camera Feed
Super ResolutionJavaTensorFlow Lite
WhisperKit (Speech to Text)*VariousTensorFlow Lite
GenieX Chat AndroidKotlin/JavaGenieX

Windows App Directory

TaskLanguageInference APISpecial Tags
ChatAppC++Genie SDKLLM, GenAI
Image ClassificationC++ONNXOpenCV
Object DetectionC++ONNXOpenCV
Super ResolutionC++ONNXOpenCV
Whisper Speech-to-TextPythonONNX
Stable Diffusion Image GenerationPythonONNX
GenieX Chat Windows*GoGenieX
SAM3 Segmentation WindowsPythonONNX

Ubuntu App Directory

TaskLanguageInference APISpecial Tags
Hand Gesture RecognitionPythonTensorFlow LiteGStreamer
Posenet Pose EstimationPythonTensorFlow LiteGStreamer
YamNet Audio ClassificationPythonTensorFlow Lite
Portrait SegmentationPythonTensorFlow Lite

* Source available on GitHub; not included in the CLI release.

Tutorials Directory

TutorialTopic
LLM on-device deploymentExporting and deploying Large Language Model (LLM) using Genie SDK
LLM on-device deployment with GenieXRunning and exporting Large Language Models (LLMs) with the GenieX runtime

LICENSE

Qualcomm® AI Hub Apps is licensed under BSD-3. See the LICENSE file.

Contributors

qaihm-bot

1 commits

Languages

Python

25.5%

Java

23.0%

Shell

20.0%

PowerShell

12.2%

C++

8.3%

Kotlin

7.4%

Dockerfile

3.2%