More like rack-sized GPU that takes in half-megawatt of power as 800 volts DC, and requires 10 gallons per second of liquid cooling or it'll catch fire.

Looks like I'll have to get back into casemodding then

Totally agree these won’t go to consumers but even after a crash I’d home they are able to be sold for parts and not just to landfills. If this all goes into the trash that would be even more tragic than it already is.

> Totally agree these won’t go to consumers but even after a crash I’d home they are able to be sold for parts and not just to landfills. If this all goes into the trash that would be even more tragic than it already is.

I'm not an accountant (so I could be wrong), but I'm vaguely under the impression it's sometimes financially beneficial to "write off" inventory, and to do that you have to destroy the items (e.g. https://en.wikipedia.org/wiki/Atari_video_game_burial, https://appleinsider.com/articles/23/05/30/apples-lisa-entom...).

So I kinda wouldn't be surprised if a bunch of these GPUs go in the dump, given that it sounds like they'd be extremely power-hungry and difficult to use outside a hyperscale data center setting.

I went down a rabbit hole of refurbing burnt out crypto gpus. It’s really not too complicated but can be hit or miss. Need to get a box full.

The proper server AI chips come on a board that isn't compatible with consumer pcie lanes and have no built-in fans. Completely different ball game in the ultra dense server world.

> rack based GPU without video ports

Still fine for running APL with the dfns compiler, Futhark or other array languages directly hosted on the GPU itself!

Nope. The scientific community is eagerly waiting for prices to drop.

Single 5090, single H100, a few 4090s, a few Blackwell PRO 6000... that's all there often is as dedicated devices for a faculty (apart from the oversubscribed bigger clusters with big stuff).

Apart from the chairs doing vision, most of them use them headless. So, landfill will be highly unlikely, if things get sold fast enough.

as the ml phd student who volunteered to be the admin for the gpu servers, that list is pretty accurate.