No. You don't pay google to run a task. You pay google to run a TPU instance for some amount of time. The time you spend leaving it on, setting it up, tearing it down, etc, are all still time you pay for. When they have TPU lambda jobs, that's different, but they don't. From their page:
Virtual machine pricing
In order to connect to a TPU, you must provision a virtual machine (VM), which is billed separately. For details on pricing for VM instances, see Compute Engine pricing.
No, you can't. Under the best case circumstance where you have a V100 and TPU both using tensorflow for something that's optimized for both, the TPU is about 37% better:
The 50% is a number you made up, and isn't based in any real benchmarks. For all other tasks that are not tensorflow tasks, the V100 is the only one that works.
We can see the cost and they are signficantly less for TPU 2.0. But now Google has the 3.0.
But if we look at WaveNet we can see Google must be taking much larger margins with the TPU 2.0 versus Amazon using Nvidia.
Rolling out using a NN at 16k cycles a second and offering at a competitive price to the old way means Google TPUs have to be way more efficient than using anything from Nvidia.
It is hard to believe Google pulled it off. But if we look at WaveNet it suggests that TTS is a solved problem.
It will be how it is done for a very long time and just the NN will improve.
Nvidia honestly needs to get on their game. Google is running a 1000 mph. Iterating to the TPU 3.0 in just a year was a big surprise.
I suspect Capsule networks and dynamic routing which were invented by Google drove the TPU 3.0 but do not know.
Hope Google will share a paper now on the TPU 2.0 and their secrets.
Lucky for Nvidia they will share and Nvidia can copy. But just keeps Nvidia behind.
Oh man... You think Google is performing some kind of secret that Nvidia has no idea how to do? I really don't understand your fanboyism. The only reason the TPU can perform more operations per watt is because it is a severely limited processor. There is nothing special about that. They chose to dedicate more silicon to a smaller number of features, while Nvidia made the processor more general. If anything, Google uses the generic tsmc Fab, well Nvidia has their own subprocess at tsmc. If Nvidia really, really wanted to make a chip that was dedicated just for deep learning and nothing else, they could. But that's only useful for Google.
The big difference is Google works top down. So Google comes up with WaveNet as an example. That then causes a need to optimize the entire stack to be possible to offer at scale.
So WaveNet part of the Google Assistant and things like Duplex. But Nvidia silicon is just not going to make it possible. So Google does the TPUs as it just would not be possible to do at a reasonable price with Nvidia.
They are doing 16k cycles through a NN in real-time and competing against a far less compute intensive technique.
Now do not get me wrong I am long Nvidia and been for a while. Bit disapointed on the hit after an incredible earnings report.
I think they will do well from a investor marketing standpoint as really the only alternative to Google hardware.
But they have a fundamental disadvantage. They just do not have the applications like Google. They do NOT have the data to iterate like Google. It is why they appears to be 2 generations behind Google.
What is the goal of Nvidia? It is hardware for hardware sake? IMO, it should be driven top down and just do not see that happening at Nvidia. Is their goal the singularity?
Versus Google clearly wants to create the singularity and the silicon is in support. That is a very different calculus compared to Nvidia.
We can see it so strongly this week. The Google Keynote was all about AI applications. Then the TPUs are too support. Take a look at the duplex video for example. That is the focus and the silicon is what makes it possible.
But the more success for Google and presentations like Duplex this week and the buzz across the Internet helps Nvidia if the goal is investing. But from an actual technical solution it just points out the problem for Nvidia that much stronger.
Virtual machine pricing In order to connect to a TPU, you must provision a virtual machine (VM), which is billed separately. For details on pricing for VM instances, see Compute Engine pricing.
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