[msandford]

While we're making hand-wavey comparisons, I'll make one.

NVidia GPUs have a theoretical peak of about 3-5 TFlops for 250 watts. http://en.wikipedia.org/wiki/List_of_Nvidia_graphics_process...

Xeons have a theoretical peak of about 0.5-1 TFlops for 150 watts. http://www.microway.com/hpc-tech-tips/intel-xeon-e5-2600-v3-...

Is that completely apples-to-apples? Probably not since the Xeon is probably talking about double precision floating point versus single precision on the GPU. But for a lot of database applications which don't involve money, single precision floats have a sufficient level of accuracy for the performance improvement to be attractive.

Yeah the performance isn't 100x like it used to be but it's still enough that if you have racks and racks full of machines a 3-10x improvement could be really substantial. Going from $10k/mo in rent to $1k/mo in rent at a datacenter could make or break an early stage startup.

Further as things get cheaper they get used a lot more. Scientists have only two models: the ones they can run but don't really like and the ones they want to run. Adding fidelity to modeling codes isn't an absolute good but it's hard to argue that it makes the world worse.