>In total, the median prompt—one that falls in the middle of the range of energy demand—consumes 0.24 watt-hours of electricity, the equivalent of running a standard microwave for about one second
>The report also finds that the total energy used to field a Gemini query has fallen dramatically over time. The median Gemini prompt used 33 times more energy in May 2024 than it did in May 2025, according to Google.
That's very interesting, although I'm still curious about the training resource usage -- not "only" inference. I wonder what is the relative importance of training (i.e., what percentage of the resource usage was in training vs. inference)
[ETA]
Extending on these numbers a bit, a mean human uses 1.25KW of power (Kardashev Level .7 / 8 Gigahumans) and the mean American uses ~8KW of power according to https://en.wikipedia.org/wiki/List_of_countries_by_energy_co.... So if we align AIs to be eco-friendly, they will definitely murder all humans for the sake of the planet /s
One thing I'm missing in the full report is what a 'median prompt' actually looks like. How many tokens? What's the distribution of prompt sizes like? Is it even the same 'median prompt' between 2024 and 2025?
The numbers are cute but we can't actually do anything with them without those details. At least an average could be multiplied by the # of queries to get the total usage.
In 2011, Google claimed that each search query takes about 0.3Wh [1]. Earlier this year, Sam Altman also claimed about 0.3Wh avg use per query for OpenAI.
I'm honestly surprised that they're so similar. I've thought of LLM queries as being far more energy-intense than "just" a Google search, but maybe the takeaway is that ordinary Google searching is also quite energy-intense.
If I as a user just wanted an answer to a dumb question like, say, the meaning of some genZ slang, it seems about an order of magnitude to ask a small LLM running on my phone than to make a google search.
(Check my math: assuming the A16 CPU draws 5 watts peak for 20sec running Gemma or whatever on my iPhone, that’s 0.03Wh to answer a simple query, which is 10x cheaper)
Are training costs (esp. from failed runs) amortized in these estimates?
The original press release and report are at [1], couldn't find a link to them in the article.
> In total, the median prompt—one that falls in the middle of the range of energy demand—consumes 0.24 watt-hours of electricity
If they're running on, say, two RTX 6000s for a total draw of ~600 watts, that would be a response time of 1.44 seconds. So obviously the median prompt doesn't go to some high-end thinking model users have to pay for.
It's a very low number; for comparison, an electric vehicle might consume 82kWh to travel 363 miles. So that 0.24 watt-hours of energy is equivalent to driving 5.6 feet (1.7 meters) in such an EV.
When I hear reports that AI power demand is overloading electricity infrastructure, it always makes me think: Even before the AI boom, shouldn't we have a bunch of extra capacity under construction, ready for EV driving, induction stoves and heat-pump heating?
In his last blog post, Sam Altman also revealed how much power the average chatgpt query uses, and it's in the same ballpark.
> People are often curious about how much energy a ChatGPT query uses; the average query uses about 0.34 watt-hours, about what an oven would use in a little over one second, or a high-efficiency lightbulb would use in a couple of minutes. It also uses about 0.000085 gallons of water; roughly one fifteenth of a teaspoon.
If I'm doing the math right, you can get ~4000 queries per kWh. A quick Google search gave $0.04 per kWh when bought in bulk. So you can get around 100k queries per dollar of electricity.
That's.... a lot cheaper than I would have guessed. Obviously, the data centers cost quite a bit to build. But when you think of $20/mo for a typical subscription. That's not bad?
I asked GPT 5 to extrapolate this to a Max Claude code monthly usage, two sessions a day, just business days. It guessed that would be 21k prompts a month. Google’s proxy numbers give a monthly footprint of ~5.1 kWh, ~5.53 L water, and ~0.64 kg CO₂e for heavy CC use.
That's equivalent to doing less than two miles driving(CO2), one toilet flush (water) and about three dryer loads of laundry.
> One major question mark is the total number of queries that Gemini gets each day, which would allow estimates of the AI tool’s total energy demand.
Yeah, I was more interested in knowing the total amount. A "median" prompt without the information on the total number of prompts is kind of meaningless...
What matters most is training, post-training, fine-tuning and data scraping, not inference IMO. The "prompting destroys the environment" deal always seemed sensationalistic to me, I'm glad it's starting to be debunked, but at the same time we can see IRL the heavy toll the new data centers being built are taking on the energy grid. If the tech was lean at it's heart in terms of energy consumption, we wouldn't be seeing that or even the private nuclear reactor arms race by big tech.
I think overall this seems good, we knew this is where the gains from TPUs would lie and there is a nice agreement here between the need for profit and more pressing planetary (anthroposcenic?) concerns. With things like this, one might ask how much do we consume binge watching something on a streaming service, or loading a page full of js and ads and canvas elements, or even just scrolling a feed for an hour on an app.
Still, there are a lot unswered questions here, and its up in the air precisely how this stuff will continue to integrate into services we already use, or habits we have yet to form at large. What does that scaling look like?
But by far the most troubling thing is the continued combination of flippancy, defensiveness, or silence we get from the AI peiple about even attempting to talk about this. If you are a True Believer, don't you want this to be something that is tackled head on, rather than tucked away? When this has come up before, I always end up seeing a bunch of guys who essentially leave the vibe of "well I am plenty above sea level, my AC is pumping just fine, and I just simply don't care because my productivity has doubled!"
Like isn't this community supposed to be excited about a future, eager to tackle problems? Or is there maybe some intrinsic solipsism to the impressive chatbots that ultimately renders this kind of attitude to its users? It feels like right when we were culturally about to age out of this particular form of obstinacy, we set ourselves up to create a whole new generation of "global warming is fake news" people. Which is a shame. If you're going to be like this, just go all in on accelerationism in all its pseudo-facist darkness, don't just borrow a script from baby boomers!
I was taken aback recently when a Gen-ish Z person told me AI was 'destroying all the water'. I've done data center work, and while I know it is used for cooling, I don't think I've ever personally destroyed any large bodies of water.
There is a perception out there about GenAI and water that goes surprisingly deep. I was told we are will be living in a drought-stricken hellscape, and AI is to blame.
I'd like to know the equivalent energy consumption of a single TikTok video, but that is probably arguing the wrong thing. My bigger question is ... where do they think that water goes? Steam? The assumption is that it is gone forever, and I can't get over how people could just take that at face value.
Do they include AI search summaries here? It would be a big no no in my view.
Google has rolled out AI summaries extensively over the time of the study and they likely use more efficient inference than chatbot prompts to their larger models.
They discuss the median in their paper. But I couldn’t find any breakdown about how the prompts they analyze are distributed across their models.
Market-based vs. location-based carbon accounting strikes again!
Does the market-based approach let Google claim lower emissions? If they’ve bought offsets or RECs, but it doesn’t reflect the actual grid mix at the time a prompt runs.
Location-based numbers give a truer picture of real-world emissions.
Measurements for water consumption seems cherry-picked and incorrect to look better than they actually are. When asked about it, they doubled-down and incorrectly mentioned the study in question (to which they compared against) was incorrect. See https://www.linkedin.com/posts/shaolei-ren-68557415_today-go...
30 comments
[ 1.6 ms ] story [ 43.3 ms ] thread>The report also finds that the total energy used to field a Gemini query has fallen dramatically over time. The median Gemini prompt used 33 times more energy in May 2024 than it did in May 2025, according to Google.
Ҩ.¬_¬.Ҩ
And unlike the human who spent multiple hours writing that article, an LLM would have linked to the original study: https://services.google.com/fh/files/misc/measuring_the_envi...
[ETA] Extending on these numbers a bit, a mean human uses 1.25KW of power (Kardashev Level .7 / 8 Gigahumans) and the mean American uses ~8KW of power according to https://en.wikipedia.org/wiki/List_of_countries_by_energy_co.... So if we align AIs to be eco-friendly, they will definitely murder all humans for the sake of the planet /s
The numbers are cute but we can't actually do anything with them without those details. At least an average could be multiplied by the # of queries to get the total usage.
I'm honestly surprised that they're so similar. I've thought of LLM queries as being far more energy-intense than "just" a Google search, but maybe the takeaway is that ordinary Google searching is also quite energy-intense.
If I as a user just wanted an answer to a dumb question like, say, the meaning of some genZ slang, it seems about an order of magnitude to ask a small LLM running on my phone than to make a google search.
(Check my math: assuming the A16 CPU draws 5 watts peak for 20sec running Gemma or whatever on my iPhone, that’s 0.03Wh to answer a simple query, which is 10x cheaper)
Are training costs (esp. from failed runs) amortized in these estimates?
1: https://googleblog.blogspot.com/2009/01/powering-google-sear...
> In total, the median prompt—one that falls in the middle of the range of energy demand—consumes 0.24 watt-hours of electricity
If they're running on, say, two RTX 6000s for a total draw of ~600 watts, that would be a response time of 1.44 seconds. So obviously the median prompt doesn't go to some high-end thinking model users have to pay for.
It's a very low number; for comparison, an electric vehicle might consume 82kWh to travel 363 miles. So that 0.24 watt-hours of energy is equivalent to driving 5.6 feet (1.7 meters) in such an EV.
When I hear reports that AI power demand is overloading electricity infrastructure, it always makes me think: Even before the AI boom, shouldn't we have a bunch of extra capacity under construction, ready for EV driving, induction stoves and heat-pump heating?
[1] https://cloud.google.com/blog/products/infrastructure/measur...
> People are often curious about how much energy a ChatGPT query uses; the average query uses about 0.34 watt-hours, about what an oven would use in a little over one second, or a high-efficiency lightbulb would use in a couple of minutes. It also uses about 0.000085 gallons of water; roughly one fifteenth of a teaspoon.
https://blog.samaltman.com/the-gentle-singularity
That's.... a lot cheaper than I would have guessed. Obviously, the data centers cost quite a bit to build. But when you think of $20/mo for a typical subscription. That's not bad?
Did I do that right?
That's equivalent to doing less than two miles driving(CO2), one toilet flush (water) and about three dryer loads of laundry.
Yeah, I was more interested in knowing the total amount. A "median" prompt without the information on the total number of prompts is kind of meaningless...
Still, there are a lot unswered questions here, and its up in the air precisely how this stuff will continue to integrate into services we already use, or habits we have yet to form at large. What does that scaling look like?
But by far the most troubling thing is the continued combination of flippancy, defensiveness, or silence we get from the AI peiple about even attempting to talk about this. If you are a True Believer, don't you want this to be something that is tackled head on, rather than tucked away? When this has come up before, I always end up seeing a bunch of guys who essentially leave the vibe of "well I am plenty above sea level, my AC is pumping just fine, and I just simply don't care because my productivity has doubled!"
Like isn't this community supposed to be excited about a future, eager to tackle problems? Or is there maybe some intrinsic solipsism to the impressive chatbots that ultimately renders this kind of attitude to its users? It feels like right when we were culturally about to age out of this particular form of obstinacy, we set ourselves up to create a whole new generation of "global warming is fake news" people. Which is a shame. If you're going to be like this, just go all in on accelerationism in all its pseudo-facist darkness, don't just borrow a script from baby boomers!
- one second of running a toaster, or
- 1/80th of a phone charge,
- lifting 100 pounds to a height of 6 feet,
- muzzle energy of a 9mm bullet,
- driving 6 feet with a Tesla.
GPT-4 is over a trillion parameters. Is there any reason to think they have 2.5x more parameters but somehow use 6x less energy?
I also think this misses a bit the forest from the trees, emissions need to go down across all industries and tech is actively increasing theirs.
There is a perception out there about GenAI and water that goes surprisingly deep. I was told we are will be living in a drought-stricken hellscape, and AI is to blame.
I'd like to know the equivalent energy consumption of a single TikTok video, but that is probably arguing the wrong thing. My bigger question is ... where do they think that water goes? Steam? The assumption is that it is gone forever, and I can't get over how people could just take that at face value.
Aral Sea was destroyed by farm irrigation: https://www.youtube.com/watch?v=oBjDF4SFIlk
Blog post: https://cloud.google.com/blog/products/infrastructure/measur...
Paper: https://services.google.com/fh/files/misc/measuring_the_envi...
Is it a metric for marketing to beat competitors with, like GPU speeds, etc.
"We're more efficient than those Global Warming bastards over at Amazon."
I assume they wouldn't publish them if it cast them in a bad light.
In this thread alone there are many comments multiplying the median to get some sort of totalt, but that's just not how medians work.
If I multiplied my median food spent per day with the number of days per month, I'd get a vastly lower number than what my banking app says.
Google has rolled out AI summaries extensively over the time of the study and they likely use more efficient inference than chatbot prompts to their larger models.
They discuss the median in their paper. But I couldn’t find any breakdown about how the prompts they analyze are distributed across their models.
In a first, Google has released data on how much energy an AI prompt uses
One can rewrite clickbait or info-less headlines, while avoiding editorial. A good one is usually either in the sub-head or first couple sentences:
"Gemini apps ... median prompt ... consumes 0.24 watt-hours of electricity, the equivalent of running a standard microwave for about one second."
Possible contentful headline that fits in 80 chars:
Median Gemini text prompt consumes 0.24Wh energy, same as microwave for a second
Learned something even without a click, can decide if you want to!
---
Even better, skip the reblog and link the research, with a headline from the abstract:
• Median Gemini Apps prompt uses less energy than watching nine seconds of TV — https://services.google.com/fh/files/misc/measuring_the_envi...
Does the market-based approach let Google claim lower emissions? If they’ve bought offsets or RECs, but it doesn’t reflect the actual grid mix at the time a prompt runs.
Location-based numbers give a truer picture of real-world emissions.