[bee_rider]

One would have to be pretty out of the loop to think that GPU memory was in the terabyte range, though.

[gambiting]

Enterprise grade workstations and servers can be configured with 1TB of ram though, so it's not completely inconceivable that you could have a dedicated GPU workstation with 1TB of graphics ram. I mean the Tesla A100 cards have 40GB of VRam each so you'd only need 26 of them to have 1TB of VRAM in a single machine. While I have no idea if such a machine exists, it certainly could exist, 26 GPUs wouldn't fill even half of a server cabinet.

Edit: actually Quattro A6000 cards ship with 48GB vram each, so you only need 22 of those to have 1TB total.

[penagwin]

Here you go friends

> Radeon Pro SSG (2016) https://www.amd.com/en/products/professional-graphics/radeon...

[monocasa]

That's a GPU with a terabyte SSD on it, not a terabyte of VRAM.

[adgjlsfhk1]

It would be really interesting if micron or intel collabed to make a gpu with a ton of 3d xpoint.

[simias]

I'm not super knowledgeable in machine learning but given that system RAM (not VRAM) in the TB range is really not that rare these days for high end workstations I assumed that the poster meant that you'd have 1TB of working RAM per GPU for the learning job (which would be paged in and out of VRAM as necessary).

I guess the lesson to be learned here is that if you want to use an implausibly huge amount of RAM to make a point, a TB is not safe anymore. Go for Exabytes instead, that should be unambiguous for a couple of years.

[bee_rider]

Haha, that might just be the right takeaway!