Hi,
I am reporting a specific sm_110 PyTorch compatibility issue on Jetson AGX Thor. I have isolated which environment
works and which fails.
System:
- Device: Jetson AGX Thor
- Jetson Linux: R38.4.0
- Kernel: 6.8.12-tegra
- Driver Version: 580.00
- CUDA Version: 13.0
- etc nv_tegra_release:
R38 (release), REVISION: 4.0, GCID: 43443517, BOARD: generic, EABI: aarch64, DATE: Wed Dec 31 00:15:19 UTC 2025
KERNEL_VARIANT: oot
TARGET_USERSPACE_LIB_DIR=nvidia
TARGET_USERSPACE_LIB_DIR_PATH=usr lib aarch64-linux-gnu nvidia
What WORKS — official NVIDIA container:
Container: nvcr.io nvidia pytorch:25.08-py3
- torch: 2.8.0a0+34c6371d24.nv25.08
- CUDA: 13.0
- cuDNN: 91200
- Conv2d forward pass: OK
- ResNet18 inference: OK
Working ResNet18 command:
docker run --rm --runtime=nvidia --gpus all --ipc=host
–ulimit memlock=-1 --ulimit stack=67108864
nvcr.io nvidia pytorch:25.08-py3
python3 -c “import torch, torchvision; print(‘torch:’, torch.version);
print(‘device:’, torch.cuda.get_device_name(0));
model = torchvision.models.resnet18(weights=None).cuda().eval();
x = torch.randn(1,3,224,224,device=‘cuda’); y = model(x); print(‘ok’, y.shape)”
Output:
torch: 2.8.0a0+34c6371d24.nv25.08
device: NVIDIA Thor
ok torch.Size([1, 1000])
What FAILS — pip-installed PyTorch nightly:
- Install method: pip install (to ~ .local lib python3.12 site-packages )
- torch: 2.12.0.dev20260317+cu128
- CUDA: 12.8
- cuDNN: 91900
- Python: 3.12.3
- torchvision: not installed
- triton: 3.6.0+git9844da95
Failing command:
import torch, torch.nn as nn
m = nn.Conv2d(3,16,3,1,1).cuda().eval()
x = torch.randn(1,3,224,224, device=‘cuda’)
y = m(x)
Full warning + error output:
lib python3.12 site-packages torch cuda init.py:375: UserWarning: Found GPU0 NVIDIA Thor
which is of compute capability (CC) 11.0.
The following list shows the CCs this version of PyTorch was built for and the hardware CCs it supports:
- 8.0 which supports hardware CC >=8.0,<9.0 except {8.7}
- 9.0 which supports hardware CC >=9.0,<10.0
- 10.0 which supports hardware CC >=10.0,<11.0 except {10.1}
- 12.0 which supports hardware CC >=12.0,<13.0
Please follow the instructions at to install a PyTorch release that supports
one of these CUDA versions: 13.0
python3.12 site-packages torch cuda init.py:493: UserWarning:
NVIDIA Thor with CUDA capability sm_110 is not compatible with the current PyTorch installation.
The current PyTorch install supports CUDA capabilities sm_80 sm_90 sm_100 sm_120.
If you want to use the NVIDIA Thor GPU with PyTorch, please check the instructions at
torch.AcceleratorError: CUDA error: no kernel image is available for execution on the device
Key detail: the PyTorch build supports sm_80, sm_90, sm_100, sm_120 — but sm_110 is missing from that list. Thor is CC
11.0 (sm_110), so it falls in the gap between sm_100 and sm_120.
Relevant pip packages from failing environment:
torch 2.12.0.dev20260317+cu128
triton 3.6.0+git9844da95
cuda-bindings 12.9.4
cuda-toolkit 12.8.1
nvidia-cublas-cu12 12.8.4.1
nvidia-cuda-runtime-cu12 12.8.90
nvidia-cudnn-cu12 9.19.0.56
nvidia-nvjitlink-cu12 12.8.93
Question:
The official nvcr.io nvidia pytorch:25.08-py3 container (torch 2.8.0, CUDA 13.0) works perfectly on Thor. But the
pip-installable PyTorch nightly (torch 2.12.0.dev+cu128, CUDA 12.8) explicitly lists sm_110 as unsupported and fails
with no kernel image.
- Is sm_110 support expected to be included in future PyTorch pip releases for CUDA 12.8+?
- Is the only supported path for Thor currently the NVIDIA-provided container with CUDA 13.0?
- Should sm_110 be added to the PyTorch build matrix, or does Thor require a JetPack-specific PyTorch build?
Other notes:
- I captured dmesg and journalctl -k output after testing — I do not see a GPU kernel fault. I mainly see repeated
nvethernet “PCS block lock SUCCESS” lines. - Setting conservative flags (cudnn.benchmark=False, allow_tf32=False) does not help in the failing environment — the
issue is the missing sm_110 kernel image, not a runtime instability.
Thanks
Regards
Ferenc