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Comment by wongarsu | Hacker News Reader

Having a few hundred consumer GPUs or a few dozen "datacenter" GPUs should be within the reach of any University department, and at least Nvidia also seems happy to sponsor University setups (after all new research creates demand from the industry).

Sure, this doesn't compete with Google's data-centers. But that's assuming Universities are for some reason competing against private industry. That's not how any other engineering discipline works, so it's a bit odd to just assume without discussion.

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"Having a few hundred consumer GPUs or a few dozen "datacenter" GPUs should be within the reach of any University department"

That was funny - however not even close to reality. I have to work on a GTX 1080 (not TI)...

I get you can’t do all your work on them, but 24 hours of 16 GPUs is $215, using the newest instance type on-demand.

It’s within the reach of many grants to afford a few scaled runs of a technology as a demonstration of behavior at scale.

A month ago Nvidia had a grant program running to get rid of refurbished^w^w^w^w donate 1-4 Titan Vs based on a 1-2 page application [1]. When my university started offering a CUDA course we got ~15 top of the line GTX cards sponsored by Nvidia. Buying 100 GTX1080TI with 11GB with supporting hardware is in the range of 100 thousand Euro/USD (before applying education discounts and asking for sponsorships). Not money spent on a dime, but not outrageously expensive either (the article mentions OpenAI spending millions on cloud GPU resources, compared to that spending 100k on something you get to keep is nothing)

[1] https://developer.nvidia.com/academic_gpu_seeding

Why can't they afford it? I remember when I worked at a physics lab at University and each team had many pieces of equipment each costing more than $100k. You'd get quite a lot of compute for that kind of money, especially since AI research doesn't need any other equipment.

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