[alecco]

> nvcc from the CUDA toolkit has a compatibility range with the underlying host compilers like gcc. If you install a newer CUDA toolkit on an older machine, likely you'll need to upgrade your compiler toolchain as well, and fix the paths.

Conversely, nvcc often stops working with major upgrades of gcc/clang. Fun times, indeed.

This is why a lot of people just use NVIDIA's containers even for local solo dev. It's a hassle to set up initially (docker/podman hell) but all the tools are there and they work fine.

[embedding-shape]

> This is why a lot of people just use NVIDIA's containers even for local solo dev. It's a hassle to set up initially (docker/podman hell) but all the tools are there and they work fine.

Yeah, which I feel like is fine for one project, or one-offs, but once you've accumulated projects, having individual 30GB images for each of them quickly adds up.

I found that most of my issues went away as I started migrating everything to `ux` for the python stuff, and nix for everything system related. Now I can finally go back to a 1 year old ML project, and be sure it'll run like before, and projects share a bit more data.

[the__alchemist]

What trouble have you had specifically? On both Win and Linux, installing the CUDA toolkit (e.g. v13) just works for me. My use case is compiling kernels (or cuFFT FFI) using nvcc for FFI in rust programs and libs.

[jcelerier]

Yep, right now nvidia libs are broken with clang-21 and recent glibc due to stuff like rsqrt() having throw() in the declaration and not in the definition