Jetson Orin Nano TensorRT LLM Compatibility Issues

Issue Overview

Users have reported difficulties in running TensorRT and TensorRT LLM (Large Language Models) on the Nvidia Jetson Orin Nano. The main symptoms include:

• Inability to run specific models, such as the BERT QA model.
• Uncertainty regarding the compatibility of TensorRT 10 with JetPack 6, which is the software stack for the Jetson Orin Nano.

The issue arises primarily during the setup phase, specifically when users attempt to implement deep learning models using TensorRT. Users have expressed concerns about missing documentation regarding support for TensorRT LLM on the Orin Nano.

The hardware specifications include the Jetson Orin Nano with JetPack 6, and users are particularly interested in leveraging TensorRT 10 for their applications. The frequency of these issues appears to be consistent among users attempting similar tasks.

The impact on user experience is significant, as many users are unable to utilize advanced deep learning functionalities that are expected from their hardware setup.

Possible Causes

Several potential causes for these issues have been identified:

• Hardware Incompatibilities: The Jetson Orin Nano may not fully support all features of TensorRT LLM due to hardware limitations or design.

• Software Bugs or Conflicts: There may be unresolved bugs in JetPack 6 or TensorRT that prevent proper functionality with LLMs.

• Configuration Errors: Users may not have configured their environments correctly, leading to issues when attempting to run models.

• Driver Issues: Outdated or incompatible drivers could hinder the performance of TensorRT on the Jetson Orin Nano.

• Environmental Factors: Insufficient memory resources might be a factor, as LLMs typically require a significant amount of memory for optimal performance.

• User Errors or Misconfigurations: Incorrect installation or setup steps could lead to the problems experienced by users.

Troubleshooting Steps, Solutions & Fixes

To address the issues related to running TensorRT and TensorRT LLM on the Nvidia Jetson Orin Nano, follow these troubleshooting steps:

1. Verify Software Versions:

   • Ensure you are using JetPack 6 and check for updates on TensorRT 10. You can verify your version with:

     dpkg -l | grep jetpack
     

2. Check Compatibility:

   • Confirm that your applications are compatible with JetPack 6 and TensorRT 10. Refer to NVIDIA’s official documentation for compatibility lists.

3. Install Required Dependencies:

   • Make sure all dependencies required for TensorRT and LLMs are installed. This can typically be done through:

     sudo apt-get install <dependency-package-name>
     

4. Testing Example Models:

   • Try running example models provided by NVIDIA to see if they function correctly. For instance, test the BERT model available at:

     git clone https://github.com/NVIDIA/TensorRT/demo/BERT.git
     

5. Memory Optimization:

   • Since LLMs require large memory resources, consider applying memory optimization techniques as suggested in NVIDIA’s AI Lab resources.

6. Look for Community Solutions:

   • Engage with community forums or check GitHub repositories for solutions shared by other users who faced similar issues.

7. Update Drivers:

   • Ensure your GPU drivers are up-to-date. You can check for driver updates via the NVIDIA website or through JetPack updates.

8. Consult Documentation:

   • Review NVIDIA’s official documentation regarding TensorRT and JetPack to ensure all configurations are correct.

9. Run Diagnostic Commands:

   • Use diagnostic commands to gather system information that may help identify issues:

     nvidia-smi
     

10. Seek Further Assistance:

    • If problems persist after following these steps, consider reaching out to NVIDIA support or posting detailed queries in developer forums.

By following these steps, users can effectively troubleshoot and potentially resolve issues related to running TensorRT and LLMs on the Nvidia Jetson Orin Nano.