Concur. I threw 2x GTX 980s in one of our testbeds two years ago just to make it more generally useful, giving us a 20 GPU cluster. Next time we do it, I'll a

Concur. I threw 2x GTX 980s in one of our testbeds two years ago just to make it more generally useful, giving us a 20 GPU cluster. Next time we do it, I'll aim for 4x1080ti or the equivalent. It's painful to power and cool those in lower-end university machine rooms, but it's possible. 10x 4x1080ti machines is only about 15kW, which is high but not insane. It's also only about $60k USD. Also something that a pool of faculty can afford. I've been experimenting with really low-end builds for that design recently. https://goo.gl/photos/6bDLjJqGAwhG7hGP9 https://goo.gl/photos/6bDLjJqGAwhG7hGP9 That's a consumer/gaming motherboard (asrock supercarrier), which doesn't have enough PCIe bandwidth to support the cards, but part of what we're researching are ways to reduce synchronization bandwidth. I wouldn't recommend that route as a general approach, though - not flexible enough for future uses. The 8x 1080ti Supermicro build posted a few days ago is probably a better choice: https://news.ycombinator.com/item?id=14508928 https://news.ycombinator.com/item?id=14508928 The problem is that one student can easily tie up the entire cluster for half the duration of her Ph.D. Machine learning people have voracious appetites for compute. :)