The highest cost for supercomputers isn't the raw hardware - it's the power consumption. This also includes the cooling needs of a large cluster. Over the lifespan of a cluster, these will add up to a significant multiple of their base cost.

* of course, this may not be the ideal way to provide access to these services

"The Virginia Tech team is figuring out how to best to farm out computing jobs so that the GPUs and CPUs do what they do best, without ever going idle, and without spending too much time communicating with one another.

It’s not easy, but they’re using HokieSpeed to build tools for designing and compiling software so that it can be tweaked to run fast on these systems. They’ve also built what they call an “automated runtime system,” which works with the supercomputer’s operating system to speed things up even further."

I think the 'supercomputing for the masses' part comes from the fact that the peak performance of a relatively low-cost GPU is large compared to a CPU, but actually taking advantage of that performance is still difficult. They're aiming to change that with improved software development tools. At least, that's what I gathered from the article.

Here's a link to research they're doing on GPU computing: http://synergy.cs.vt.edu/