# Common Issue RuntimeError: CUDA error

**URL:** <https://ask.cyberinfrastructure.org/t/common-issue-runtimeerror-cuda-error/2943>\
**Category:** Q&A\
**Tags:** systems, gpu, cuda, pytorch\
**Created:** [September 19, 2023, 7:50pm UTC](https://ask.cyberinfrastructure.org/t/common-issue-runtimeerror-cuda-error/2943 "2023-09-19T19:50:46Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![ShrutiDongare](https://ask.cyberinfrastructure.org/letter_avatar_proxy/v4/letter/s/c2a13f/32.png) [@ShrutiDongare](https://ask.cyberinfrastructure.org/u/ShrutiDongare)\
**Post date:** [September 19, 2023, 7:50pm UTC](https://ask.cyberinfrastructure.org/t/common-issue-runtimeerror-cuda-error/2943/1 "2023-09-19T19:50:46Z")

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I am running into ‘RuntimeError: CUDA error: no kernel image is available for execution on the device’ multiple times. I tried to manually configure Pytorch and its compatible cuda version. The versions that did not work are below,

PyTorch==1.12.1 cuda 11.3,11.6, etc  
PyTorch==1.13.1 cuda 11.7,11.2, etc

I partially understood the issue, I am using machine with GPU Rtx 3080 with compute capability of 8.6 which requires cuda binaries to be compiled with the sm\_86 capability to work properly. PyTorch 2 is the minimum version that officially supports it which is not compatible with the sim. PyTorch 1 built with cuda 11 has a highest compute capability of sm\_75. But do that have access to 2000 series GPU. Is there any alternate solution to this problem?

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<div class="post-metadata">

**Author:** ![Jobair.16](https://ask.cyberinfrastructure.org/letter_avatar_proxy/v4/letter/j/bb73d2/32.png) [@Jobair.16](https://ask.cyberinfrastructure.org/u/Jobair.16)\
**Post date:** [November 2, 2023, 8:20pm UTC](https://ask.cyberinfrastructure.org/t/common-issue-runtimeerror-cuda-error/2943/2 "2023-11-02T20:20:46Z")

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The “RuntimeError: CUDA error: no kernel image is available for execution on the device” error occurs when the CUDA version, GPU architecture, and software are incompatible. The NVIDIA RTX 3080 GPU has a compute capability of 8.6, which requires software compiled with this compute capability.

Given your constraints, here are some solutions:

- Custom build PyTorch from source with the CUDA toolkit targeting the sm\_86 compute capability. This ensures compatibility between your GPU and PyTorch version, but it can be complex.
- Use NVIDIA GPU Cloud (NGC) Docker containers for deep learning frameworks, including PyTorch. NGC containers are optimized for NVIDIA hardware, but they may not have the exact version you need.
- Downgrade to a GPU of the 2000 series (e.g., RTX 2080), which has a compute capability of 7.5. This would be compatible with PyTorch built with CUDA 11 targeting sm\_75.
- Try TensorFlow or another deep learning framework, which may have different compatibility points or more recent builds that support sm\_86.
- Seek community builds or contact PyTorch developers for potential solutions or workarounds.