[dragontamer]

The Titan V is $3000 (GeForce)

The Tesla V100 is $17,000: http://www.nextwarehouse.com/item/?2782569_g10e

We're talking the difference of ~5.5x the price here for otherwise similar cards.

[shaklee3]

The V100 is $8000 retail. The site you linked is incorrect.

[andars]

It probably depends a lot. There is the SXM vs. PCIe distinction plus discounts for bulk and all kinds of negotiations.

[shaklee3]

No, it's $8000 if you want to buy one now. https://www.thinkmate.com/product/nvidia/900-2g500-0000-000

[Patrick_Devine]

When I was running my p-2-p startup I wanted to try capitalizing on this. It's the reason why GPU instances cost an arm and a leg.

[andars]

But you can already not buy a datacenter's worth of GeForce; they have limitations on the quantity per customer. That seems like a far more effective measure than this clause.

[kuschku]

And yet, many datacenters run hundreds or thousands of GeForce cards. Hetzner, for example, rents out an i7-6700, GTX 1080, 64GBRAM for 117€/month

[krautt]

I've spent some time training tensorflow cnns on nvidia 1080gtx.

It works pretty well, but i couldn't reliably train production models for work on it. it's just too flaky. I mentioned this to our 'trustworthy' dell rep who suggested that a good v100 suite would surely solve all the reliability problems, what with it's greatly increased memory bus, or something...

[jamesblonde]

You mean your algorithm is too flaky. The hardware is fine. A V100 won't fix your convergence problems.

[krautt]

yes. salesman bullshit. i think it was tensorflow that was flaky.

[suresk]

CNN models being "flaky" on GeForce hardware isn't something I've heard of? NVIDIA has made some deliberate decisions to make GeForce cards less attractive for deep learning in terms of performance, but I don't think making them produce incorrect results is in their best interest. What hardware did you test this against?