Rating: 4.5/5
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. cuda toolkit 12.6
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. Rating: 4