A lot of deep learning workloads aren't necessarily bounded by things like disk transfers, though, but stuff like bandwidth to the card itself. You probably have enough RAM to just pre-warm your disk cache anyway if you have exceedingly large training samples. This is more useful for things like massive CAD models, if I had to guess.

On Nvidia if you want this, I think you can basically achieve it using GPUDirect anyway if you really want to bypass CPU transfers and go straight from SSD to GPU, via PCIe. I guess it depends exactly on how this card is connected to the disk.

Having it on-card is pretty convenient, though. GPUDirect has practical limitations on what boards/systems it will work with.

I'm not sure the 2TB of onboard NVMe would help machine learning tasks. Most machine learning tasks are already well designed for the ~16GB limit on standard GPUs.

The product page suggests that this sort of GPU can handle streaming 8k video. So maybe the card was designed for OpenCL accelerated video editing or some other 3d-task (maybe 3d rendering) with very large texture data.

Why? One of my DL rigs runs on Win 10 reliably. Radeons anyway won't be a good candidate for DL, though for mining are best.