Amin Vahdat is VP and GM of AI and Infrastructure at Google. He discusses how bursty network communication patterns during massive AI compute jobs impact power grids.
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Vahdat: AI infrastructure build-out is 100x larger than 1990s internet boom
“The internet in the late nineties, early 2000 was big, and we felt like, oh my gosh, can't believe the Build out the rate. This makes it, I mean, 10 X is an understatement. It's a hundred X what the internet was.”
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Vahdat: AI infrastructure deployment bottlenecks will persist for 3 to 5 years
“Like, in other words, literally, you all have some money, you can't spend it all as fast as you want. I think that's going to extend for three, four, five years.”
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Vahdat: AI hardware supply will lag behind demand in short term
“So one worry I have is that the supply isn't actually going to catch up to the demand as quickly as we'd all like.”
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Vahdat: Google TPUs are 10x to 100x more energy efficient than CPUs
“TPU, I'll use that example again because I know it best for certain computation, is somewhere between 10 and a hundred times more efficient per watt, and it's this watt that really matters than a CPU.”
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Vahdat: Bringing a specialized chip to production takes at least 2.5 years
“For the best teams in the world, really from concept to in live in production, the speed of light is two and a half years.”
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Vahdat: Google is driving 10x to 100x reductions in AI inference costs
“What I would say though is that maybe something people don't realize is that we're actually driving massive reductions in the cost of inference. I mean, 10 X's and a hundred X's.”