The author compares it to the Tesla C2050 at the start of the post. Kinda makes one wonder why not just wait a bit more and go with a consumer card for those tasty TFLOPS. ;) That thought however is destroyed once we think on both cards power consumption difference.

That said, if my desktop GPU could match this sort of Tesla performance if it were “unlocked”, it’s kind of depressing to think about. Folding@home, for example, would majorly benefit from unlocked performance. Given NVIDIA’s major support for F@h, you’d almost imagine that there’d be a rule written in the driver to unlock the performance for just that app. I can only imagine how much more efficient and useful the GPUs could be for that purpose then.

I’d like to make a point to our readers about these cards capabilities when compared with the consumer-grade cards. Contrary to what you may hear being said, cards like your GForce 500 are also capable of true double-precision floating point arithmetic. When it is said Tesla cards “unlock the double precisions floating point performance”, what this means is that indeed the performance level was unlocked, not the ability to perform such operations. So far, Tesla cards can perform a double-precision floating point operation in one GPU cycle, as opposed to the GForce 500 series which require 4 cycles (meaning they are 4 times slower for the same operation).