# Use AI Hub to optimize a model For quick prototyping of models on Qualcomm AI hardware, AI Hub provides a way to optimize, validate, and deploy machine learning models on-device for vision, audio, and speech use cases ![../_images/ai-hub_QLI.png](data:image/png;base64,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) ## Set up your environment 1. Setup your Python environment. Install [miniconda](https://docs.conda.io/projects/miniconda/en/latest/miniconda-install.html) on your machine. Tab Windows Tab macOS/Linux When the installation finishes, open an Anaconda prompt from the Start menu. When the installation finishes, open a new shell window. Set up a Python virtual environment for AI Hub. conda activate Copy to clipboard conda create python=3.10 -n qai_hub Copy to clipboard conda activate qai_hub Copy to clipboard 2. Install git. sudo apt-get install git Copy to clipboard 3. Install the AI Hub Python client. pip3 install qai-hub Copy to clipboard pip3 install "qai-hub[torch]" Copy to clipboard 4. Sign in to AI Hub. Go to [AI Hub](https://aihub.qualcomm.com/) and sign in with your Qualcomm ID to view information about jobs you create. Once signed in, go to *Account > Settings > API Token*. This should provide an API token that you can use to configure your client. 5. Configure the client with your API token using the following command in your terminal. qai-hub configure --api_token Copy to clipboard ## Choose an AI Hub workflow ### Try a pre-optimized model 1. Go to [AI Hub Model Zoo](https://aihub.qualcomm.com/iot/models) to access pre-optimized models available for Qualcomm evaluation kits. 2. Filter models available for your EVK. For example, pre-optimized models for Qualcomm Dragonwing™ RB3 Gen 2 can be downloaded by selecting *Qualcomm QCS6490* as the chipset in the left pane. 3. Select a model from the filtered view to go to the model page. 4. On the model page, select the runtime and precision. 5. Select download to begin model download. The downloaded model is preoptimized and ready for deployment. See [Develop your own AI/ML application](https://docs.qualcomm.com/doc/80-80022-15B/topic/develop-your-own-application.html) for more information about deploying the model. ![../_images/ai-hub-download.png](data:image/png;base64,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) ### Bring your own model 1. Select a pretrained model in PyTorch or ONNX format. 2. Submit a model for compilation or optimization to AI Hub using python APIs. When submitting a compilation job, you must select a device or the chipset for your EVK and the target runtime to compile the model. For RB3Gen2, the LiteRT runtime is supported. | **Chipset** | **Runtime** | **CPU** | **GPU** | **HTP** | | --- | --- | --- | --- | --- | | Qualcomm Dragonwing™ RB3 Gen 2 | LiteRT | INT8,FP16, FP32 | FP16,FP32 | INT8,INT16 | On submission, AI Hub generates a unique ID for the job. You can use this job ID to view job details. 3. AI Hub optimizes the model based your device and runtime selections. - Optionally, you can submit a job to profile or inference the optimized model (using Python APIs) on a real device provisioned from a device farm. - Profiling: Benchmarks the model on a provisioned device and provides statistics, including average inference times at the layer level, runtime configuration, etc. - Inference: Performs inference using an optimized model on data submitted as part of the inference job by running the model on a provisioned device. 4. Each submitted job will be available for review in the AI Hub portal. A submitted compilation job will provide a downloadable link to the optimized model. This optimized model can then be deployed on a local development device like RB3Gen2. The following is an example of the described workflow taken from the [AI Hub documentation](https://app.aihub.qualcomm.com/docs/). In this example, a MobileNet V2 pretrained model from PyTorch is uploaded to AI Hub and compiled to an optimized LiteRT model to run on an RB3Gen2 target. import qai_hub as hub import torch from torchvision.models import mobilenet_v2 import numpy as np # Using pre-trained MobileNet torch_model = mobilenet_v2(pretrained=True) torch_model.eval() # Trace model (for on-device deployment) input_shape = (1, 3, 224, 224) example_input = torch.rand(input_shape) traced_torch_model = torch.jit.trace(torch_model, example_input) # Compile and optimize the model for a specific device compile_job = hub.submit_compile_job( model=traced_torch_model, device=hub.Device("Dragonwing RB3 Gen 2 Vision Kit"), input_specs=dict(image=input_shape), #compile_options="--target_runtime tflite", ) # Profiling Job profile_job = hub.submit_profile_job( model=compile_job.get_target_model(), device=hub.Device("Dragonwing RB3 Gen 2 Vision Kit"), ) sample = np.random.random((1, 3, 224, 224)).astype(np.float32) # Inference Job inference_job = hub.submit_inference_job( model=compile_job.get_target_model(), device=hub.Device("Dragonwing RB3 Gen 2 Vision Kit"), inputs=dict(image=[sample]), ) # Download model compile_job.download_target_model(filename="/tmp/mobilenetv2.tflite") Copy to clipboard Note To deactivate a previously activated `qai_hub` environment use the following command. conda deactivate Copy to clipboard Once the model is downloaded, it’s ready to be used for you to [Develop your own AI/ML application](https://docs.qualcomm.com/doc/80-80022-15B/topic/develop-your-own-application.html). For more details about the AI Hub workflow and APIs, see the [AI Hub documentation](https://app.aihub.qualcomm.com/docs/hub/index.html#examples), explore the [AI Hub tutorial videos](https://www.youtube.com/watch?v=V1CDWYZ7Shw&list=PLxeazpXYyqtOowtUdvigvAgMV5_K1KIrh), or watch the following video about how to profile models in AI Hub.
Last Published: Jun 23, 2026
Note The video above uses Python 3.8 as an example. Python 3.8 and Python 3.10 are supported. Last Published: Jun 23, 2026 [Previous Topic Compile and optimize an AI model](https://docs.qualcomm.com/bundle/publicresource/80-80022-15B/topics/compile-and-optimize-model.md) [Next Topic Use Qualcomm AI Runtime SDK to optimize an AI model](https://docs.qualcomm.com/bundle/publicresource/80-80022-15B/topics/qairt.md)