On the other hand, Google will make a shit load of money when they make TPUs available on gcloud. Papers like this are great marketing for them.
And to make things simple, let's do it all in FP16 because INT8 on Volta ~= 1/2 a first generation TPU, but FP16 ~= 3 first generation TPUs at INT8 (sad, right?), an accident that occurred because P100 didn't support INT8, but consumer variants did.
So, 5,064/3 = 1,688 Volta GPUs ~= $5000 per hour, probably half that reserved, a quarter of that in spot.
Say you need a week to train this, so $200K-$800K...
You can buy DGX-1Vs off-label for about $75K. Say they costs $20K annually to host. Say you use them for 3 years, so total TCO is ~135K, which comes down to $0.64/hour.
Conclusion: p3.8xl spot instances are currently a steal! But I don't have ~$200K burning a hole in my pocket, so I guess I'm out of luck.
Further, they used 5,000 (first generation) TPUs at 90 INT8 TOPS each, page 4, to run the network during MCTS and 64 (second generation) TPUs to train this thing according to the methods. That's a nice mix of using INT8 for inference and FP16ish for training IMO.
In contrast, I personally own 8 GTX Titan XP class GPUs and 8 more GTX Titan XM GPUs across 4 desktops in my home network. I'd love to experiment with algorithms like this, but I suspect I'd get just about nowhere due to insufficient sampling. These algorithms are insanely inefficient at sampling at the beginning. So I guess I will seed the network with expert training data to see if that speeds things up.
That said, more brilliant work from David Silver's group! But not all of us have 5,000 TPUs/GPUs just sitting around so there's still a lot more work/research to make this more accessible to less sexy problems.