[angrygoat]

Google claim 29x better performance-per-Watt with TPUs than contemporary GPUs[0]. Interesting to contrast that to the images-per-$ figure in this post, which is more like 2x.

I assume there's a high capital cost for this new hardware, but when they scale it up I wonder if the ratio of cost TPU to GPU will trend towards the ratio of power-per-Watt between the platforms? Seems like a natural limit, even if it never quite gets there.

[0] https://cloud.google.com/blog/big-data/2017/05/an-in-depth-l...

[dgacmu]

That was TPUv1 (inference only). This article is about the new Cloud TPU (or TPUv2 as they call it), which handles both inference and training. The competitive landscape also changed a lot in the interim - NVidia added tensor cores to Volta to accelerate deep learning computation.

[zitterbewegung]

I think you might want to factor that they are adding their own fees? Also, I think the market may have changed since then (AWS lowered prices and new GPUs have come out). Google's workload is also different than the benchmark that is given in this.

[gumby]

> Google claim 29x better performance-per-Watt with TPUs than contemporary GPUs[0]. Interesting to contrast that to the images-per-$ figure in this post, which is more like 2x.

But you aren't paying for the electricity, you're paying for processing, which is an unconnected parameter. They only "sell" these chips per use, not on the open market

Presuming power is a major cost input (which I assume) their profit/op is much higher. So they could sell for less than an equivalent GPU and make more money. But they think they can get away with value pricing it (processing more/unit time is presumably worth it to many customers) and more power to them.

(that last bit was not an intentional pun; only noticed after typing it)

[aje403]

Maybe Google will pivot from high tech into crypto mining