This is most likely not an issue with Pytorch (alone) but may be the result of other influences. Depending on the hardware being utilized and the packages being used/methods of their installation, it is unsurprising to see some differences in speed between different environments. As an example, a user using an RTX3090 will see some meaningful change in speed when performing specific operations when using cu111+ (libtorch_cuda.so is missing fast kernels from libcudnn_static.a, therefore statically linked cuDNN could be much slower than dynamically linked · Issue #50153 · pytorch/pytorch · GitHub 2).