You should consider passing through your Nvidia GPU to a virtual machine in order to do compute tasks on; that way, your host machine won’t be infected with proprietary Nvidia drivers (I’m assuming you need CUDA for your compute tasks). The only performance differences you’ll notice is less available system RAM (you will have access to all of your VRAM), and very slightly less CPU performance, due to running two operating systems at the same time (barely even noticable, TBH). This is the option that I would personally recommend.
If you want to try a super hacky solution which might not work for everything you need, you can try using the open source, recently released ZLUDA translation layer to perform CUDA tasks on your AMD GPU.
The reason Hyprland doesn’t work with proprietary Nvidia drivers is due to Nvidia refusing to implement the accepted Wayland standard in favor of their own, home-rolled solution which is incompatible. AFAIK, only GNOME and KDE implement that standard.
You should consider passing through your Nvidia GPU to a virtual machine in order to do compute tasks on; that way, your host machine won’t be infected with proprietary Nvidia drivers (I’m assuming you need CUDA for your compute tasks). The only performance differences you’ll notice is less available system RAM (you will have access to all of your VRAM), and very slightly less CPU performance, due to running two operating systems at the same time (barely even noticable, TBH). This is the option that I would personally recommend.
If you want to try a super hacky solution which might not work for everything you need, you can try using the open source, recently released ZLUDA translation layer to perform CUDA tasks on your AMD GPU.
https://github.com/vosen/ZLUDA
The reason Hyprland doesn’t work with proprietary Nvidia drivers is due to Nvidia refusing to implement the accepted Wayland standard in favor of their own, home-rolled solution which is incompatible. AFAIK, only GNOME and KDE implement that standard.