When is all sm_120 supported in nightly?

gosohanlatex6: <p>When is all sm_120 supported in nightly?</p>
<p>I switched to gigabyte 5060ti graphics card on a 1660 and installed the driver properly. Since then, automatic1111 hasn’t run. So I deleted the existing pytorch118 and reinstalled it with nightly pytorch128. So pytorch only has 1 binary.</p>
<p>However, the same problem arises.<br>
The statement does not support sm_120.<br>
Is it possible that some sm_120s are not supported?<br>
When is all sm_120 supported in nightly?</p>

NVIDIA GeForce RTX 5060 Ti with CUDA capability sm_120 is not compatible with the current PyTorch installation.
The current PyTorch install supports CUDA capabilities sm_50 sm_60 sm_61 sm_70 sm_75 sm_80 sm_86 sm_90.
If you want to use the NVIDIA GeForce RTX 5060 Ti GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/

C:\Users\나현준&gt;pip list | findstr torch
torch              2.8.0.dev20250513+cu128
torchaudio         2.6.0.dev20250514+cu128
torchvision        0.22.0.dev20250514+cu128
<p>C:\Users\Na Hyunjun&gt;<br>
C:\Users\나현준&gt;pip show torch<br>
Name: torch<br>
Version: 2.8.0.dev20250513+cu128<br>
Summary: Tensors and Dynamic neural networks in Python with strong GPU acceleration<br>
Home-page: <a href="https://pytorch.org/" rel="noopener nofollow ugc">https://pytorch.org/</a><br>
Author: PyTorch Team<br>
Author-email: <a href="mailto:packages@pytorch.org">packages@pytorch.org</a><br>
License: BSD-3-Clause<br>
Location: c:\users\나현준\appdata\local\programs\python\python310\lib\site-packages<br>
Requires: filelock, fsspec, jinja2, networkx, sympy, typing-extensions<br>
Required-by: torchaudio, torchvision</p>
<p>C:\Users\나현준&gt;pip show torchvision<br>
Name: torchvision<br>
Version: 0.22.0.dev20250514+cu128<br>
Summary: image and video datasets and models for torch deep learning<br>
Home-page: <a href="https://github.com/pytorch/vision" class="inline-onebox" rel="noopener nofollow ugc">GitHub - pytorch/vision: Datasets, Transforms and Models specific to Computer Vision</a><br>
Author: PyTorch Core Team<br>
Author-email: <a href="mailto:soumith@pytorch.org">soumith@pytorch.org</a><br>
License: BSD<br>
Location: c:\users\나현준\appdata\local\programs\python\python310\lib\site-packages<br>
Requires: numpy, pillow, torch<br>
Required-by:</p>
<p>C:\Users\나현준&gt;pip show torchaudio<br>
Name: torchaudio<br>
Version: 2.6.0.dev20250514+cu128<br>
Summary: An audio package for PyTorch<br>
Home-page: <a href="https://github.com/pytorch/audio" class="inline-onebox" rel="noopener nofollow ugc">GitHub - pytorch/audio: Data manipulation and transformation for audio signal processing, powered by PyTorch</a><br>
Author: Soumith Chintala, David Pollack, Sean Naren, Peter Goldsborough, Moto Hira, Caroline Chen, Jeff Hwang, Zhaoheng Ni, Xiaohui Zhang<br>
Author-email: <a href="mailto:soumith@pytorch.org">soumith@pytorch.org</a><br>
License:<br>
Location: c:\users\나현준\appdata\local\programs\python\python310\lib\site-packages<br>
Requires: torch<br>
Required-by:</p>
<p>&lt;Check if it’s viable&gt;<br>
Python 3.10.6 (tags/v3.10.6:9c7b4bd, Aug  1 2022, 21:53:49) [MSC v.1932 64 bit (AMD64)] on win32<br>
Type “help”, “copyright”, “credits” or “license” for more information.</p>
<blockquote>
<blockquote>
<blockquote>
<p>import torch<br>
print(torch.version.cuda)<br>
12.8<br>
print(torch.cuda.get_device_capability())<br>
(12, 0)<br>
exit()</p>
</blockquote>
</blockquote>
</blockquote>

ptrblck: <p>All PyTorch binaries with CUDA 12.8 support sm_120 and the earliest were released for Linux in February. Based on the error message you are using an older binary built with an older CUDA toolkit. I would recommend executing a simple test after installing the right binary via:</p>
<pre data-code-wrap="python"><code class="lang-python">print(torch.__version__)
print(torch.cuda.version)
print(torch.cuda.get_arch_list())
print(torch.cuda.get_device_properties(0))
print(torch.randn(1).cuda())
</code></pre>
<p>Execute this code snippet standalone without any other application and post the output here.</p>

gosohanlatex6: <p><div class="lightbox-wrapper"><a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/3X/3/1/31d374b12cc61b8311a13973949ae32b8ffe3ac8.png" data-download-href="https://discuss.pytorch.org/uploads/default/31d374b12cc61b8311a13973949ae32b8ffe3ac8" title="image"><img src="https://discuss.pytorch.org/uploads/default/original/3X/3/1/31d374b12cc61b8311a13973949ae32b8ffe3ac8.png" alt="image" data-base62-sha1="76Mr9XyUmGMukDJClTsq7r7AW4U" width="690" height="94" data-dominant-color="1A1A1A"><div class="meta"><svg class="fa d-icon d-icon-far-image svg-icon" aria-hidden="true"><use href="#far-image"></use></svg><span class="filename">image</span><span class="informations">1137×156 5.16 KB</span><svg class="fa d-icon d-icon-discourse-expand svg-icon" aria-hidden="true"><use href="#discourse-expand"></use></svg></div></a></div></p>
<p>under code is starting page of automatic1111<br>
<div class="lightbox-wrapper"><a class="lightbox" href="https://discuss.pytorch.org/uploads/default/original/3X/6/8/682904f9d3bd091c2163d173ba8a34003d786f50.png" data-download-href="https://discuss.pytorch.org/uploads/default/682904f9d3bd091c2163d173ba8a34003d786f50" title="image"><img src="https://discuss.pytorch.org/uploads/default/original/3X/6/8/682904f9d3bd091c2163d173ba8a34003d786f50.png" alt="image" data-base62-sha1="eRrxwjFUNT7C98M6oSdxyn4vFCg" width="690" height="206" data-dominant-color="1C1A1A"><div class="meta"><svg class="fa d-icon d-icon-far-image svg-icon" aria-hidden="true"><use href="#far-image"></use></svg><span class="filename">image</span><span class="informations">957×286 8.6 KB</span><svg class="fa d-icon d-icon-discourse-expand svg-icon" aria-hidden="true"><use href="#discourse-expand"></use></svg></div></a></div></p>

ptrblck: <p>Thank you for confirming the PyTorch binary itself works fine as seen in your first screenshot!<br>
The output shows CUDA 12.8 is used and the binaries support compute capabilities 6.1 to 12.0. The last line of code also shows you are able to use your Blackwell GPU by creating a random tensor on the GPU.</p>
<p>I’m not familiar with <code>automatic1111</code> and guess it’s using another virtual environment with another (potentially older) PyTorch binary.</p>