Why Jetson Orin Nano Miss ~855MB Memory?

Issue Overview

Users of the Nvidia Jetson Orin Nano Developer Kit, particularly the 8GB version, are experiencing a discrepancy in reported memory. When running the tegrastats command, the total available memory is reported as approximately 7337MB, leading to a perceived loss of around 855MB from the expected 8192MB. This issue typically arises during initial setup or when running applications that require significant memory resources, such as large language models. The problem has been noted consistently across different users, indicating a commonality in the hardware or software behavior of the Jetson Orin platform. Users report that this missing memory could potentially be reserved for system processes or other modules, impacting their ability to utilize the full capacity for their applications.

Possible Causes

• Memory Reservation: Some memory may be reserved for system operations, such as CPU cooperation with the BPMP (Boot and Power Management Processor). This is a common practice in embedded systems to ensure stability and performance.
• Device Tree Configuration: The reserved memory settings in the device tree may not be properly configured, leading to reduced accessible memory.
• Unit Conversion Confusion: Different tools may report memory using varying definitions of kilobytes and megabytes (base 10 vs. base 2), which can create misunderstandings about available resources.
• Software Bugs: There could be bugs in the JetPack software or related drivers that misreport available memory.
• Environmental Factors: Power supply inconsistencies or overheating could lead to reduced performance and misreporting of available resources.

Troubleshooting Steps, Solutions & Fixes

1. Check Memory Reservation:

   • Inspect the device tree settings for reserved memory configurations. Use the following command to view current reservations:

     cat /proc/device-tree/reserved-memory
     

   • Adjust settings if necessary to free up reserved memory.

2. Verify Memory Reporting:

   • Use tegrastats and other monitoring tools like jtop to confirm reported memory usage:

     sudo jtop
     

3. Understand Unit Conversions:

   • Be aware of how different tools report memory (1K = 1000 bytes vs. 1K = 1024 bytes). This can lead to apparent discrepancies in reported values.

4. Update Software:

   • Ensure that you are running the latest version of JetPack and related drivers. Updates often include bug fixes that can resolve memory reporting issues.

5. Reboot and Check Environment:

   • Restart the Jetson Orin Nano to clear any temporary states that might affect performance.
   • Ensure that the power supply is stable and within specifications.

6. Utilize Workarounds:

   • If certain applications are failing due to low memory warnings, consider optimizing code to reduce memory footprint or pre-allocating memory instead of dynamically resizing arrays.
   • For example, when using CuPy for array operations, pre-allocate arrays rather than using xp.concatenate iteratively:

     import cupy as xp
     array = xp.empty(60000, dtype=xp.float64)
     

7. Documentation and Community Support:

   • Refer to Nvidia’s official documentation for Jetson Orin Nano for any specific configurations or updates.
   • Engage with community forums for shared experiences and solutions from other users encountering similar issues.

8. Monitor for Memory Leaks:

   • If applications show increasing memory usage over time without release, investigate potential memory leaks in your code or libraries being used.

9. Further Investigation:

   • If problems persist after following these steps, consider reaching out to Nvidia support or community forums with detailed logs and descriptions of your setup for more tailored assistance.

By following these steps, users should be able to diagnose and address the issue of missing memory on their Jetson Orin Nano devices effectively.