Google’s new video gives a simple look at TPUs and how they handle heavier AI workloads, which is useful if you’ve ever wondered why these chips matter so.
Look — TPUs matter because they’re built to chew through matrix math (the big multiply-accumulate stuff) with high bandwidth and predictable throughput, instead of being a general-purpose chip that’s constantly context-switching. The unsexy win is efficiency: you get more training/inference per watt and per rack, which is why Google keeps doubling down on them.
