[shaklee3]

They announced in 2016 they had TPUs. So no, I would not expect that 2 full years later they're just now being available in the public cloud. These are not new products to them; they likely just don't want to deal with supporting them in different configurations.

[ndesaulniers]

> they likely just don't want to deal with supporting them in different configurations.

It is a lot of work. But mainly that TPUv1 only did inference while TPUv2 does training+inference.

[shaklee3]

Exactly my point. It's a lot of work. That's the reason why Nvidia has such a large team doing it, and also why they spent 3 billion dollars to build the V100 ASIC.

[jacksmith21006]

Big difference is Google does the entire stack and also has scale to conceptually be able to create a better solution. But that is theoretical.

Here we can see the results where the Google TPU gets almost twice result per dollar over Nvidia. But then Google should be able to iterate more quickly.

Take the move from using CNN to using capsule networks. The idea for Capsules came from Hinton and Google is going to be there first to optimize in hardware. This is the benefit of playing in all layers of the stack.

Or the using NN for text to speech and offering at scale. Google just has inherent advantages over Nvidia and now we get to see a little more concrete results. But hope we get a lot more similar and see if the Google advantage holds up.