Comment on How Much Energy Does AI Use? The People Who Know Aren’t Saying

• Xaphanos@lemmy.world ⁨1⁩ ⁨day⁩ ago

  Well, I work at an AI hyperscaler. I can tell you how much my facility uses, and how much each rack uses, but don’t have any way to determine what the customer is doing on that server. Or even which servers a given customer is using. Is it being used heavily for queries? How many? Of what kind? We don’t know. Only what the rack/row/pod/hall is consuming.

  Also, does the network gear overhead count? How do you apportion that?

  We have no visibility into the customer workload. Some of our customers use our systems for scientific research. Drugs, etc. How do you tally that?

  I’m not saying that it is impossible, just that if the customer won’t pay for that report, we’re not going to spend money to build the systems to produce it.

  Do I agree? No. But I’m just a grunt.

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  • Zeoic@lemmy.world ⁨1⁩ ⁨day⁩ ago

    Im sure they can do the simple math of: we pay for x power, we have y customers. x / y would be a rough but probably pretty accurate number if we are talking tens of thousands to millions of customers.

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  • silence7@slrpnk.net ⁨1⁩ ⁨day⁩ ago

    You can produce a remarkably good estimate by looking at CPU and GPU utilization out of procfs and profiling a handful of similar machines power use with similar utilization and workloads.

    Network is less than 5% of power use for non-GPU loads; probably less for GPU.

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    • DarkDarkHouse@lemmy.sdf.org ⁨1⁩ ⁨day⁩ ago

      Sure, you can do that at an aggregate level, but then how do you divide it by customer? And even then, some setups will be more efficient than others, so you’d only get that setup’s usage.

      And even if you do that and can narrow it down to a single user and a single prompt, you can still only roughly predict how long it will think and how long the response will be.

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      • silence7@slrpnk.net ⁨1⁩ ⁨day⁩ ago

        By customer is easy: they’re each renting specific resources. A fractional cloud instance (excepting the sma burst able ones) is tied to specific CPUs and GPUs. And there are records of who rented which one when being kept already.

        You might not be able to break out specific individual queries, but computing averages is completely straightforward

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