Rating: 4.5/5
As of this review, the mainstream PyTorch release (2.3.1) is built against CUDA 12.1. You can force PyTorch to work with 12.6 by building from source or using LD_LIBRARY_PATH hacks, but expect "driver too old" warnings. The AI/ML ecosystem typically lags by 4-6 months. For production ML, stick to the CUDA version your framework officially supports. cuda toolkit 12.6
Finally, official support for Clang 18 and GCC 13.2 . This is a lifesaver for developers using modern C++ features (C++20/23) in scientific computing. The NVCC frontend feels noticeably more robust with complex template metaprogramming. Rating: 4