# Prepare a GenAI model using AI Hub

Qualcomm AI Hub provides a streamlined workflow to prepare and deploy
large language models (LLMs) on Qualcomm Dragonwing™ products using
Qualcomm GenerativeAI Inference Extensions (Genie).

This approach enables efficient on-device execution of generative AI
models by leveraging the neural processing unit (NPU) and optimized binaries.

The following image shows the high-level GenAI model workflow from preparation to
execution.

![../_images/genai-prepare-ai-hub.png](data:image/png;base64,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)

Important

The steps in this section are validated for QCS9100, which uses Hexagon architecture V73. If you follow the link provided on AIHUB, follow the export commands for Snapdragon X Elite because it also supports V73 architecture.

The following are the steps to create LLM model binaries using AIHUB:

- [Prerequisites](https://github.com/quic/ai-hub-apps/tree/main/tutorials/llm_on_genie#requirements)
- [Detailed instructions](https://github.com/quic/ai-hub-apps/tree/main/tutorials/llm_on_genie#step-2-export-qairt-compatible-llm-models-on-the-host-machine)

The following is an overview of LLM on-device deployment:

1. Prepare the model.

    1. Start with the desired LLM (for example, Llama 3.x series) from Hugging Face or another source.
    2. Use the `qai_hub_models` Python package to export the model:

        This process:

        1. Downloads the model weights.
        2. Uploads them to AI Hub for compilation.
        3. Generates QNN binaries split into multiple parts for NPU execution.
        4. Creates a deployable folder (`genie_bundle`) with all required assets (context binaries,
configs, tokenizer).
2. Compile and quantize the model.

    1. AI Hub compiles models into optimized binaries for [Qualcomm AI Runtime (QAIRT) SDK](https://docs.qualcomm.com/doc/80-63442-10/).
    2. AI Hub supports quantization (typically 4-bit internally, though weights may be stored as 8-bit for compatibility).
    3. Export scripts handle splitting large models into prompt processors and token generator components.
3. Deploy the model.

    The following are high-level steps of the deployment process. For detailed instructions and commands, see [Run LLMs with Genie](https://docs.qualcomm.com/doc/80-80022-15B/topic/use-genai-model-with-genie.html#run-llms-with-genie).

    1. Install the Qualcomm AI Runtime (QAIRT) SDK on the target device (Android, Windows, Linux).
    2. Copy the compiled binaries and configuration files to the device.
    3. Use [Qualcomm GenerativeAI Inference Extensions (Genie) CLI tools](https://docs.qualcomm.com/doc/80-63442-10/topic/tools_tools.html)
(for example, `genie-t2t-run`) or [Genie dialog API](https://docs.qualcomm.com/doc/80-63442-10/topic/api-rst_file_include_Genie_GenieDialog_h.html#file-include-Genie-GenieDialog.h) for inference.
    4. Ensure the target device meets the following requirements. The steps in this section are validated for QCS9100, which uses Hexagon architecture V73.

        - Hexagon architecture: v73 or newer
        - Required RAM:

            - 16 GB for 7B models
            - ~12 GB for 3B models
4. Run the model on-device using Genie APIs integrated with [Qualcomm AI Engine Direct](https://docs.qualcomm.com/doc/80-63442-10/topic/index_QNN.html).

    - Genie manages multiple binaries and execution orders for optimal NPU utilization.

Important notes

- AI Hub advantages

    - automatically handles model compilation, quantization, and splitting.
    - Provides pre-optimized models and bring your own model (BYOM) support.
- Genie

    - Simplifies inference by abstracting complex execution steps.
    - Offers APIs for text-to-text and dialogue-based interactions.
- Customization

    - Export flow defaults to 4-bit quantization for runtime efficiency.
    - No direct option to store weights as 4-bit; they remain 8-bit but load as 4-bit during execution.

Last Published: May 14, 2026

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