Add PyTorch 2.8.0 to JP6 cu128 index & jtop JetPack version missing on Orin AGX with JetPack 6.2.1

I noticed two issues on my Jetson Orin AGX running JetPack 6.2.1:

  1. The PyPI index for jp6/cu128 (https://pypi.jetson-ai-lab.io/jp6/cu128) does not include PyTorch 2.8.0, whereas the jp6/cu126 index does. It would be very helpful if PyTorch 2.8.0 could be added to the cu128 index.

  2. I have installed CUDA 12.8 and cuDNN 9.10.2 manually. As a result, jtop reports the JetPack version as missing. Could you clarify if this has any consequences, and suggest any recommended steps to address or mitigate this?

Hi,

We have a container with CUDA 12.9+PyTorch 2.8.
Please check if this can meet your requirements:

nvcr.io/nvidia/pytorch:25.08-py3-igpu

Thanks.

Hi,

Thanks for your replies so far. I want to clarify that my use case does not prefer using a container (Docker / Jetson container) — I would like a native / system-level installation of PyTorch 2.8.0 + CUDA 12.8 (cu128) on JetPack 6.2.1 (Orin AGX).

If there is any existing non-container build (wheel, apt package, or binary) for that combination, I’d greatly appreciate a link. Otherwise, could someone share step-by-step instructions or patches to build it from source (with CUDA support)?

Also, there was a second question earlier in this thread that hasn’t gotten a response — could someone help with that as well?

Thank you in advance.

For your second question, this post may help.

Jtop fix to show Jetpack 6.2.1 - Jetson & Embedded Systems / Jetson AGX Orin - NVIDIA Developer Forums

Thank you for sharing the jtop fix — following the guide, jtop now correctly shows JetPack 6.2.1. Really appreciate it! 🙏

Additionally, I’m still hoping someone can help with the first issue: PyTorch 2.8.0 is missing from the JP6/cu128 PyPI index for Jetson Orin AGX with JetPack 6.2.1 and CUDA 12.8. If anyone has a non-container installation method (wheel, apt package, or source build instructions), I would be very grateful if you could share it.

Hi,

Due to limited resources, we now only support the default JetPack 6 and JetPack 7 environments.
But you should be able to build it from the source.

There is a sharing from @whitesscott that tries to enable the MPI for PyTorch.
You can follow the command to build PyTorch from the source (can skip the MPI part):

Thanks.