Ai Hub Apps By Quic Sourcepulse

Leo Migdal

-Nov 18, 2025, 10:47 PM

Discover and explore top open-source AI tools and projects—updated daily. The Qualcomm® AI Hub Apps repository provides a collection of open-source sample applications and tutorials for deploying machine learning models on Qualcomm® devices. It targets developers looking to optimize on-device AI performance, offering recipes for various ML tasks and end-to-end workflow guidance. The apps leverage Qualcomm® AI Hub Models and support multiple runtimes including TensorFlow Lite, ONNX, and the Genie SDK for generative AI. Deployment targets are Android (API v30+) and Windows 11, with compute options spanning CPU, GPU, and NPU (Hexagon HTP). NPU acceleration requires specific Snapdragon chipsets and FP16 or INT8 precision.

To get started, locate the desired OS and app within the repository's folders. Each app's README contains specific build and installation instructions. Supported deployment targets include Android 11 (API v30+) and Windows 11. NPU acceleration is optimized for Snapdragon chipsets (e.g., 8 Elite, 8 Gen 3/2/1, 888/888+). This repository is maintained by Qualcomm. Specific community channels or roadmaps are not detailed in the provided README.

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. Weight and activation type required for NPU Acceleration: NOTE: These apps will run without NPU acceleration on non-Snapdragon® chipsets. Search for your desired OS & app in this folder, or in the app directory at the bottom of this file. On-device image recognition with ONNX Runtime

Accelerated object detection with YOLO‑X Learn real-time image processing with TFLite and OpenCV Text-to-image generation on Snapdragon® NPUs High-performance image enhancement on Snapdragon® NPUs pip install qai-hub-models Copy PIP instructions Popular Machine Learning models optimized for Qualcomm chipsets.

The Qualcomm® AI Hub Models are a collection of state-of-the-art machine learning models optimized for deployment on Qualcomm® devices. See supported: On-Device Runtimes, Hardware Targets & Precision, Chipsets, Devices Some models (e.g. YOLOv7) require additional dependencies. View the model README (at qai_hub_models/models/model_id) for installation instructions. 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.

RepositoryStats indexes 719,750 repositories, of these quic/ai-hub-apps is ranked #138,339 (81st percentile) for total stargazers, and #197,145 for total watchers. Github reports the primary language for this repository as Java, for repositories using this language it is ranked #7,953/31,618. quic/ai-hub-apps is also tagged with popular topics, for these it's ranked: machine-learning (#2,757/9223), pytorch (#1,948/6562), deeplearning (#165/449), onnx (#134/415), inference (#146/380), machinelearning (#94/281) quic/ai-hub-apps has 1 open pull request on Github, 0 pull requests have been merged over the lifetime of the repository. Github issues are enabled, there are 5 open issues and 102 closed issues. There was an error while loading.

Please reload this page. The Qualcomm® AI Hub Models are a collection of state-of-the-art machine learning models optimized for performance (latency, memory etc.) and ready to deploy on Qualcomm® devices. The Qualcomm® AI Hub Models are a collection of state-of-the-art machine learning models optimized for performance (latency, memory etc.) and ready to deploy on Qualcomm® devices. We currently support Python >=3.8 and <= 3.10. We recommend using a Python virtual environment (miniconda or virtualenv). Once the environment is setup, you can install the base package using:

Some models (e.g. YOLOv7) require additional dependencies. You can install those dependencies automatically using: Discover and explore top open-source AI tools and projects—updated daily. Model hub for on-device Qualcomm AI inference The Qualcomm® AI Hub Models repository provides a curated collection of state-of-the-art machine learning models optimized for deployment on Qualcomm® devices.

It targets developers and researchers aiming for efficient on-device inference, offering a wide range of pre-trained models across computer vision, audio, multimodal, and generative AI tasks. The project leverages Qualcomm's AI optimization expertise to deliver models with improved latency and memory footprints. Models can be compiled and profiled using the Qualcomm® AI Hub platform, which supports various Qualcomm® runtimes (AI Engine Direct, LiteRT) and hardware targets (CPU, GPU, NPU). This allows for efficient deployment and performance evaluation on specific Qualcomm chipsets and devices. Some models may require specific hardware features or have limited precision support on older chipsets. The full suite of features, including compilation and profiling, necessitates an account and API token for the Qualcomm® AI Hub.

This document provides an introduction to the qai_hub_models repository, a Python package containing 140+ state-of-the-art machine learning models optimized for deployment on Qualcomm devices. This overview covers the package structure, model catalog organization, and the high-level workflow for compiling and deploying models to target hardware. Scope: This page introduces the overall system architecture and core concepts. For detailed information about specific subsystems: Sources: README.md1-356 qai_hub_models/_version.py1-7 The qai_hub_models package (version 0.41.0) is a PyPI-installable library that provides pre-optimized implementations of machine learning models designed to run efficiently on Qualcomm hardware.

Each model includes: The package supports three primary on-device runtimes:

Ai Hub Apps By Quic Sourcepulse

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