[Nokinside]

Nvidia is currently in cashing out phase. They have monopoly and money flows in effortlessly. The cost performance ratio reflects this.

AMD will enter the game soon once they get their software working, Intel will follow.

I suspect that Nvidia will respond with its own specialized machine learning and inference chips to match the cost/performance ratio. As long as Nvidia can maintain high manufacturing volumes and small performance edge, they can still make good profits.

[jacksmith21006]

"The cost performance ratio reflects this."

But the TPUs are half the cost per this article?

Plus Google does the entire stack and can better optimize the hardware versus Nvidia. So it seem Google can improve faster I would think.

If there ever was a huge advantage doing the entire stack it is with neural networks.

A perfect example is Google new speech doing 16k samples a second with a NN.

https://cloudplatform.googleblog.com/2018/03/introducing-Clo...

Do not think Google could offer this service as a competitive cost without the TPUs.

This new method is replacing the method that was far less compute intensive so to offer at a competitive price requires lowering compute cost which suspect is only possible with the TPUs.

[Nokinside]

> But the TPUs are half the cost per this article?

Exactly. Nvidia can match the performance already without 100% specialized processor. It's the just the price they need to cut by optimizing their architecture for tensor processing and reducing their profits when competition emerges.

Google is not in the business of becoming a major chip maker or competing with Nvidia head on. Putting hundreds of millions into new microarchitecture every second year eats lots of resources. They just want competitive market and the prices to go down.

[shaklee3]

I'm not sure what you mean by google does the entire stack. Nvidia writes all of the major CUDA libraries used behind the scenes in the NN libraries, such as cuDNN, cuBLAS, etc. Nvidia can likely improve their hardware significantly faster/more efficiently than Google can because their entire business depends on it. Google has incentive for improving their TPU for internal use, but they don't make any money by selling TPU time on GCP yet.

[ndesaulniers]

> I'm not sure what you mean by google does the entire stack.

Consider that Google has some of the best machine learning researchers, compiler engineers, hardware engineers, and infrastructure in the business working on this.

[shaklee3]

Huh? Machine learning and infrastructure Engineers, yes. Compiler and Hardware engineers? No. What gives you reason to believe they have a lead in either of those departments other than they have a lot of money? They're forced to use the same foundry as Nvidia, and their Hardware team is likely significantly smaller.

[jacksmith21006]

Google been buying up AI resources well before anyone else and has the strongest and deepest team at this point.

It is why so many of the break throughs have come from Google. Great example is winning at Go almost a decade earlier than anyone thought possible.

They probably two of the strongest teams with one the Brain team and then the Deepmind team. But all the other engineers and infrastructure is first rate at Google.

Really at this point do not think the $100B cash is as important as Google already built the team and now experinced resources are far more difficult to get.

The other advantage for Google is their ability to attract the top engineers in addition.

Google just got started a lot earlier on all of this.

[shaklee3]

Google got started a lot earlier on this? Did you read what you are saying? Nvidia has been making hardware longer than Google has been a company. No, Google does not have a better hardware team. Google has the luxury of making a device that is used for a single purpose that they control. Nvidia made a device that can be used for far more and works on commodity hardware. By the way, deepmind/alphago uses Nvidia GPUs, so that was an extremely bad example.

[jacksmith21006]

Google does the applications at scale and then each layer below and a big one is TF. A great example is the recent release of the new text to speech using NN.

[jacksmith21006]

When you use a Google service that uses the TPUs they are indirectly selling the TPUs.