One of the reasons 3D artists who are using GPU rendering generally go for liquid cooling (that and it makes the cards single slot)
16.5K seems pretty reasonable for 8x 1080ti with a bit of profit for building it, but unreasonable for only 4x 1080ti. My home-built 4x1080ti box (without quite enough PCIe bandwidth, admittedly) is under $6k. I'm assuming/hoping there's an error there. :)
Screenshot of the order form: https://www.dropbox.com/s/2nm00w1rd6du6ey/Screenshot%202017-...
Oh, also - if I want a quote on both the big server and the little workstation I have to enter my contact info twice? Not particularly customer-friendly.
The server we sell is packaged with software we wrote that makes administering significantly easier. We also provide technical support and even a limited amount of free machine learning consulting. The customers who purchase this server want a headache free solution and aren't as price sensitive as a lone researcher.
notice the custom part box accounts for two more GPUs, I'm not sure why the site doesn't let you add 4 to the GPU section.
This setup ranges from $5250 with 4 GPU to $3240 with 1 GPU. You might want to bump up the PSU for 4 GPU its currently 1500 watts, which may or may not be enough at max load. The article shows a max of ~2800 watts with 8 GPUs
Mobo does not support 4x16 PCIe lanes (that's why they didn't want you to add 4 GPU cards).
Mobo is limited to 64GB RAM.
$520 for two 256GB 840 Pro SSDs? Seriously?
Here's a better mobo: https://www.amazon.com/Motherboards-X99-E-WS-USB-3-1/dp/B00X...
Also, you can literally double the RAM for the same money: http://www.ebay.com/itm/128gb-DDR4-8-Crucial-16gb-DDR4-2400m... (keep in mind that speed of RAM is irrelevant for DL tasks).
Dreamscope doesn't actually make money :) It's a little under break even. It brings in revenue through a $9.99/mo premium subscription, which gives customers higher resolution images.
Bottleneck depends on the workload. If you're training a small/fast network, data bandwidth is a real problem.
That being said, for most cases, a workstation build that provides every GPU with 16 lanes is far less cost effective.
If you don't require a rack mounted server, a cluster of workstations like NVIDIA's DIGITS DevBox is far more cost efficient (and less noisy). I run a compute intensive business (Dreamscopeapp.com) and we opted to build a cluster of desktop-like machines instead of using a rack mounted solution. Another benefit is you don't run into the power issues mentioned in the post.
My start-up actually sells the machine described in this post: https://lambdal.com/nvidia-gpu-server
And a machine inspired by the NVIDIA DIGITS DevBox: https://lambdal.com/nvidia-gpu-workstation-devbox