one assessment suggests that ChatGPT, the chatbot created by OpenAI in San Francisco, California, is already consuming the energy of 33,000 homes. It’s estimated that a search driven by generative AI uses four to five times the energy of a conventional web search. Within years, large AI systems are likely to need as much energy as entire nations.
That doesn’t sound like they’re taking future hardware optimizations into account, we won’t be using GPUs for this purpose forever (as much as Nvidia would like that to be true lol)
Not to mention that increasing usage of AI means AI is producing more useful work in the process, too.
The people running these AIs are paying for the electricity they’re using. If the AI isn’t doing enough work to make it worth that expense they wouldn’t be running them. If the general goal is “reduce electricity usage” then there’s no need to target AI, or any other specific use for that matter. Just make electricity in general cost more, and usage will go down. It’s basic market forces.
I suspect that most people raging about AIs wouldn’t want their energy bill to shoot up, though. They want everyone else to pay for their preferences.
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Anything power saved by hardware design improvements will be consumed by adding more transistors. You will not be seeing a power consumption decrease. Manufacturers of this hardware have been giving talks for the past two years calling for literal power plants to be build co-resident with datacenters.
That was my thought too. I heard a take about how we may see us shift away from GPUs to purpose built PUs as a way to continue process progress now we’re getting pretty small on the silicon scale. Neural nets may be one of these special “PU”s we see.
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The great thing about math is that it’s interchangeable.
https://rpsc.energy.gov/energy-data-facts#collapse-accordion-25-3
https://www.inchcalculator.com/convert/british-thermal-unit-to-kilocalorie/
33,000 households x 77,000,000 Btu/household x 0.252164 kcal/Btu = 640,748,724,000 kcal
https://www.webmd.com/diet/health-benefits-bagels
https://www.healthline.com/nutrition/kcal-vs-calories
640,748,724,000 kcal / 264 kcal/bagel = 2,427,078,500 bagels
Your homework is finding out how much energy the brain consumes in bagels
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The original article doesn’t specify a unit of time:
Based on context clues, it’s probably consumption per year
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That’s some good diligence!
It looks like the ecoflow values are lower:
https://www.inchcalculator.com/convert/kilowatt-to-btu-per-hour/
30 KWh/day x 365 days x 3,412 Btu/KWh = 37,361,400 Btu
Which is half the value I found for 2015. Does ecoflow have more current data and houses are twice as efficient? Maybe. They’re also trying to sell something, so maybe it’s based on data from their products. They don’t mention where they got it from.
The welovecycling conversion is off by 1000 (maybe the kilocalorie threw them off?)
https://www.inchcalculator.com/convert/joule-to-kilocalorie/
1 kcal = 4,184 J so 1 J = 1/4,184 kcal = 0.00023900573613 kcal
Otherwise, your math was right, just off by 3 zeros, so a household is more like 3.6 bagels per hour.
The nist site also doesn’t specify a unit of time, but if it is 20 watts/hour (Wh) we’d only need to move it 3 places for KWh, or 0.020 KWh.
Too many conversions can introduce errors, so we can go from KWh to kcal directly:
https://www.inchcalculator.com/convert/kilowatt-to-kilocalorie-per-hour/
0.020 KWh x 860 kcalh/KWh = 17.2 kcalh
Which, yeah, is not much of a bagel per hour. Keep in mind that the daily recommended calories for an average adult is 2000 kcal.
All in all, this was a fun thought experiment, so thanks for looking into it further!
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I’m not sure if future optimization wouldn’t bring more demand. At least, that’s what my hardware and apps shown in a couple of decades. If another start up would have an ability to train with additional billion or trillion of parameters, I’m sure they would. It also leads to a wider window for poor optimization.