# Tutorial - Use IR Backend by Using the Qualcomm® AI Engine Direct Delegate

Qualcomm® AI Engine Direct Delegate has provided IR backend for users to generate a DLC from a model.
This tutorial demonstrates how to use the IR backend with the Qualcomm® AI Engine Direct Delegate.
We will go through how to use qtld-net-run to generate a DLC on a specified path.

## Prerequisites

The following list of prerequisites must be met before starting this tutorial:

1. Finish [Tutorial qtld-net-run](https://docs.qualcomm.com/doc/80-63442-10/topic/tutorial_qtld_net_run.html)

## How to Generate DLC by Using qtld-net-run

Please specify <cite>backend</cite> and <cite>ir_dlc_path</cite>.

$ adb shell 'export LD_LIBRARY_PATH=/data/local/tmp/qnn_delegate/:$LD_LIBRARY_PATH &&
                 export ADSP_LIBRARY_PATH="/data/local/tmp/qnn_delegate/" &&
                 cd /data/local/tmp/qnn_delegate/inception_v3_quant/ &&
                 /data/local/tmp/qnn_delegate/qtld-net-run \
                 --model inception_v3_quant.tflite \
                 --input target_raw_list.txt \
                 --output output \
                 --backend ir \
                 --ir_dlc_path /data/local/tmp/qnn_delegate/inception_v3_quant.dlc
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The output should look similar to the following:

TFLite model: [inception_v3_quant.tflite]
    Input list file: [target_raw_list.txt]
    Total number of inferences: [1]
    Using QNN Backend: [ir]
    IR DLC Path: [/data/local/tmp/qnn_delegate/inception_v3_quant.dlc]
    Loaded model successfully.
    
    === Pre-invoke Interpreter State ===
    Line 945: Allocated 1 input tensor(s)
    Line 955: Allocated 1 output tensor(s)
    
    === Invoking Interpreter ===
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You should find the DLC is generated on the specified path.

Last Published: Jun 04, 2026

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