### PyTorch 2.5 Compatible CUDA Version PyTorch 2.5 does not officially exist as of the latest updates in the PyTorch release cycle[^1]. The highest stable version of PyTorch at the time of this writing is PyTorch 2.0, which supports CUDA versions up to CUDA 11.8 and CUDA 12.1 depending on the specific build[^4]. If you are referring to a hypothetical or future version of PyTorch (e.g., PyTorch 2.5), it would likely support newer CUDA versions such as CUDA 12.x or later, but this cannot be confirmed until an official announcement is made by the PyTorch development team. For PyTorch versions that are currently available, the compatibility between PyTorch and CUDA can be checked on the official PyTorch installation page at https://pytorch.org/get-started/locally/. This page provides detailed instructions for selecting the appropriate CUDA version based on your GPU architecture and system setup. If you are using a GPU with higher compute capability, such as NVIDIA GeForce RTX 3090 (compute capability sm_86), you must ensure that the installed PyTorch version supports this compute capability. As of now, PyTorch builds typically support compute capabilities up to sm_86 when built with CUDA 11.3 or later[^4]. ```python import torch # Check PyTorch version print(f"PyTorch Version: {torch.__version__}") # Check if CUDA is available print(f"CUDA Available: {torch.cuda.is_available()}") # Check CUDA version print(f"CUDA Version: {torch.version.cuda}") ``` ###