It's fairly easy, if not super user friendly, to opt out of the Julia-vendored binaries though. If you've ever tried to install CUDA related packages from a po

It's fairly easy, if not super user friendly, to opt out of the Julia-vendored binaries though. If you've ever tried to install CUDA related packages from a poorly configured Python environment you'll have encountered this issue. On the vast majority of machines if you want to use CUDA.jl you just add it as a dependency. There's a reason much of Julia's audience uses things like Spack or even static dependencies in their non-Julia code. If you just want to get work done fussing with PATH issues or the right version of CMake/gcc/LLVM-OMP etc etc can take hours. Once you've gotten the work done, and you know it will load on most users computers, you're ready to deploy. Then just add an Overrides.toml to your environment. From the other side, just like websites there is a barrier caused by "please install W, X, Y, Z dependencies" vs "Pkg.add("GLMakie")"