Qualcomm® AI Hub Models

The Qualcomm® AI Hub Models are a collection of state-of-the-art machine learning models optimized for deployment on Qualcomm® devices.

• List of Models by Category
• On-Device Performance Data
• Device-Native Sample Apps

See supported: On-Device Runtimes, Hardware Targets & Precision, Chipsets, Devices

Install Python Package

Choose the package that fits your use case (both provide the qai-hub-models command-line interface):

# Lightweight CLI for:
# - browsing the catalog (model info, performance, & evals)
# - downloading deployable model assets
pip install qai-hub-models-cli

or

# The full package, for exporting from source, evaluating models, and running end-to-end demos. Includes the CLI.
#
# NOTE for Snapdragon X Elite and Snapdragon X2 Elite users:
# AMDx64 (64-bit) Python is required on Windows.
# Installation will fail when using Windows ARM64 Python.
pip install qai_hub_models

Browse and Download Models

Use the CLI to browse the catalog and download deployable assets:

qai-hub-models models # browse the catalog
qai-hub-models info mobilenet_v2  # model details + download options
qai-hub-models numerics mobilenet_v2  # model evaluation results
qai-hub-models perf mobilenet_v2  # model performance results
qai-hub-models fetch mobilenet_v2 --runtime tflite --precision float # download a deployable asset
# ... and more

The CLI also offers a Python API. See the CLI README for full usage instructions.

Export from Source and Run A Model on a Physical Device

All models in our directory can be compiled and profiled on a hosted Qualcomm® device.

The downloads and numerics / performance results published with each release are generated by the export and evaluate commands outlined below.

1. Configure AI Hub Workbench Access

Create a Qualcomm® ID, use it to login to Qualcomm® AI Hub Workbench, and configure your API token:

qai-hub configure --api_token API_TOKEN

2. Install Model-Specific Dependencies

qai-hub-models install yolov7

3. Compile, Export, and Evaluate the model.

# Quantize the floating point model to w8a8, convert it to LiteRT format, and profile it on the Samsung Galaxy S25.
qai-hub-models export yolov7 --target-runtime tflite --precision w8a8 --device "Samsung Galaxy S25 (Family)"

# Download a dataset and run evaluation of the LiteRT model on the Samsung Galaxy S25.
qai-hub-models evaluate yolov7 --hub-model-id 

Export and evaluate use Qualcomm® AI Hub Workbench to:

1. If applicable, Quantize the model (see: Quantization on AI Hub Workbench)
2. Compile the model for the chosen device and target runtime (see: Compiling Models on AI Hub Workbench).
3. Download the model.
4. Profile the compiled model on a real device in the cloud (see: Profiling Models on AI Hub Workbench).
5. Evaluate the model on a subset of a dataset on a real device in the cloud, and compare on-device model output with PyTorch output (see: Running Inference on AI Hub Workbench)

Sample Applications

Native applications that can run our models (with pre and post processing) on physical devices are published in the AI Hub Apps repository.

Python applications are defined for all models (from qai_hub_models.models.\ import App). These apps wrap model inference with pre- and post-processing steps written using torch & numpy. These apps are optimized to be an easy-to-follow example, rather than to minimize prediction time.

End-To-End Model Use-Case Demos

Most models have demos that run the model end-to-end (including pre/postprocessing).

1. Install model dependencies.

qai-hub-models install yolov7

2. Demo using Local Inference on CPU / GPU via PyTorch

# Use PyTorch to predict and draw bounding boxes on the provided image.
#
# This runs the model using standard PyTorch kernels via your local CPU or GPU (no NPU support).
qai-hub-models demo yolov7 [--image ] [--help]

3. Demo using Accelerated inference on a cloud-hosted Qualcomm Device

# Upload the image and compiled model to a cloud-hosted device,
# run the model, download results, and draw bounding boxes.
#
# This uses a hardware-accelerated target runtime (eg. Lite RT), rather than PyTorch, to run the model.
# Pre and postprocessing may still require numpy and/or PyTorch.
# However, we strive to include as much pre/postprocessing in the accelerated model as is feasible.
#
# NOTE: Not all models have a cloud-hosted demo; some are PyTorch only.
qai-hub-models demo yolov7 [--image ...] --eval-mode on-device --hub-model-id 

End-to-end demos:

1. Preprocess human-readable input into model input
2. Run model inference either locally via PyTorch-CPU, or on a cloud-hosted Qualcomm® device.
3. Postprocess model output to a human-readable format

Model Support Data

On-Device Runtimes

Device Hardware & Precision

*Some older chipsets do not support fp16 inference on their NPU.

Chipsets

• Snapdragon 8 Elite Gen 5, 8 Elite, 8 Gen 3, 8 Gen 2, and 8 Gen 1 Mobile Platforms
• Snapdragon X2 Elite, Snapdragon X Elite Compute Platforms
• SA7255P, SA8295P, and SA8775P Automotive Platforms
• QCS 6490, QCS 8250, QCS 9075, and QCS 8550 IoT Platforms
• QCS8450 XR Platform

and many more.

Devices

• Samsung Galaxy S21, S22, S23, S24, and S25 Series
• Xiaomi 12, 13, 15, and 17
• Snapdragon X Elite CRD and Snapdragon X2 Elite CRD (Compute Reference Device)
• Qualcomm RB3 Gen 2, RB5 Gen 2, IQ-8, IQ-9

and many more.

Creating a New Model Recipe with Claude

Want to onboard your own model? Follow the instructions in EXTERNAL_ONBOARDING.md to create a new model recipe using Claude.

Model Directory

Generative AI

Computer Vision

Multimodal

Audio

Need help?

Slack: https://aihub.qualcomm.com/community/slack

GitHub Issues: https://github.com/qualcomm/ai-hub-models/issues

Email: ai-hub-support@qti.qualcomm.com.

LICENSE

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