Right. Or billions -- or trillions. Consider something like the Inception-like convolutional model that's one of the workloads in the paper. Training Inception is "relatively" easy -- one week of 48 K80 GPUs. (I'm lying, of course, because you retrain, and you train many times to do hyperparameter optimization, but still).

Then consider the possible applications of that at Google scale -- there are "an awful lot" of images on the web, over 13PB of photos in Google photos last year [1], a gajiggle of photos in street view and google maps, an elephant worth in google plus, and probably a few trillion I'm not even thinking of. :)

Same applies, of course, to Translate, and to RankBrain, also mentioned as NNs running on the TPU. 100B words per day translated [2], and .. many, many, many Google Searches per day, even if RankBrain primarily targets the 15% of never-before-seen queries [3].

Add that to the fact that GPUs are poorly-suited to realtime inference because of the large batch size requirements, and it's a solid first target.

[1] https://en.wikipedia.org/wiki/Google_Photos [2] http://www.k-international.com/blog/google-translate-facts/ [3] https://www.bloomberg.com/news/articles/2015-10-26/google-tu...

(work at Google Brain on Mondays, but speakin' for myself here.)