You assume the application is compute limited and that the extra performance on the Xeon translates into extra performance on a given application. That's probably not a good assumption for this kind of workload.
Why, for embarrassingly parallel workloads (like the ones they mention) it's a totally reasonable assumption. And for something not so parallel the gazillion of ARM nodes is all but useless.
The article mentions Hadoop, big data crunching, web serving and web caching. They may or may not be embarrassingly parallel, but that doesn't mean any of them are typically compute bound.
Look, today's multichip, multicore servers tend to be unbalanced for a lot of workloads. Their massive compute performance often burns power waiting for main memory, disk or network.
You're going to be I/O bound (network or disk), memory bound, or compute bound. It's hard to imagine the Redstone systems besting Xeon based servers in any of the three.
It depends entirely on where your bottlenecks are. If the bottleneck is entirely within your node, then this isn't going to be compelling. If you're doing something that's very light on the resources within your node (serving static content, etc) and your bottleneck is some other system somewhere else, then these sorts of machines could be compelling purely from a space/power POV.
If your nodes are not bound on some local resource, you can as well just run them in virtualization containers on Xeon. The setup will be even more flexible than with (less powerful) ARMs.
If your workload runs on one or two Xeon servers, it probably isn't worth considering something like this. If your workload runs on racks of Xeon servers, it might be.
Then the question is, which hardware delivers the right balance of CPU, memory and IO bandwidth for the lowest capital and operating costs.
Also for what it is worth, each card has 60Gbps of general IO bandwidth, and another 48Gbps of SATA disk bandwidth.
