Edge computing distributes power across a large area rather than concentrating it, allowing devices to charge during off-peak hours when grid capacity is underutilized (US average power usage is 500 GW while peak production exceeds 1,000 GW), making it more scalable than centralized clusters that face electricity constraints.
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Edge compute vs giant clusters本站添加:
them out. Um so um but but between now and then uh the li the actually the the constraint for for for server side compute uh concentrated compute will be will be electricity.
My my guess is that we start hitting people start getting for where they can't turn the chips on for for for large clusters uh towards the end of this year. they're just the chips are going to be piling up and and not be won't be able to be turned on. Now for edge computers it's a different story.
So if the if if like for for Tesla the the so the AI5 chip is going into our Optimus robot you know uh optimistic um and and so if you have uh AI edge compute that's distributed power now the power is distributed over a large area it's not concentrated um and if you can charge at night you can actually um [snorts] uh use the grid much more effectively because the the actual peak power production in the US is over 1,000 GW. Uh but the average power usage because the dayight cycle is 500. So if you can charge at night, there's an incremental 500 gaw that you can um generate uh you know at night. Um so that that's why Tesla for edge compute is not constrained and we can make a lot of ships uh to make you know very large number of robots and cars. Uh, but if you try to concentrate that compute, you're going to have a lot of trouble turning it
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