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In a first, Google has released data on how much energy an AI prompt uses

125 points by jeffbee 1y ago

FridayoLeary 1y ago
>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.
amitizle 1y ago
Me using AI to summarize the article; Ҩ.¬_¬.Ҩ
- jeffbee 1y ago
  Me sipping coffee with 1000 times more embodied energy while reading the article.
vitorbaptistaa 1y ago
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)
- IAmBroom 1y ago
  Same could be said for anything: human operator, google search algos pre-AI (where indexing = training), etc.
  - beepbooptheory 1y ago
    Is this just meant to be dismissive, or just like a kind of non-answer? That it could be said for anything doesn't make asking it of this specific thing unimportant or uninteresting.
- add-sub-mul-div 1y ago
  Right, and the scraping, the extra storage required, the manufacture of all the extra GPUs, etc. This is them hoping people don't understand that the query is only one part of it and aren't curious enough to ask further questions.
  - xnx 1y ago
    It's rare to see this type of calculation anywhere, though I wish it weren't. A miles per gallon number for a car doesn't count the diesel that went into the equipment to mine the ore to make the steel for the chassis, etc.
- jeffbee 1y ago
  Many papers on large models make claims about the energy used. Google has consistently said that training and inference use about the same fraction of their data center resources.
- cillian64 1y ago
  One random preprint I found (https://arxiv.org/html/2505.09598v2 https://arxiv.org/html/2505.09598v2) estimates inference is 90% of the total energy usage. From some googling around, everything seems to agree that inference dominates (or is at least comparable to) training on the large commonly-used models. I was less surprised that inference dominates training after I read that chatgpt is serving billions of requests per day.
sxp 1y ago
Since a human uses ~100W of power, the .24Watt-hours of energy for an AI prompt is about 40human-seconds [Edit: 9human-seconds] of energy. 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_environmental_impact_of_delivering_ai_at_google_scale.pdf 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_consumption_per_capita 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
- IAmBroom 1y ago
  Consider this your second upvote from me, for the second paragraph.
- Octoth0rpe 1y ago
  > an LLM would have linked to the original study There is a non-trivial chance that the LLM would've added a link to _something_, but links/references seem like a very common thing to hallucinate, no?
  - PhantomHour 1y ago
    The way around that is that is for LLM-based tools to run a regular search engine query in the background and feed the results of that in alongside the prompt. (Usually a two-step process of the LLM formulating the query, then another pass on the results) The used results can then have their link either added to the end result separately, guaranteeing it is correct, or added to the prompt and "telling the LLM to include it", which retains a risk of hallucination, yes. Common to both of these is the failure mode that the LLM can still hallucinate whilst "summarizing" the results, meaning you still have no guarantee that the claims made actually show up in the results.
    - coldtea 1y ago
PhantomHour 1y ago
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.
gcr 1y ago
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? 1: https://googleblog.blogspot.com/2009/01/powering-google-search.html https://googleblog.blogspot.com/2009/01/powering-google-sear...
- gpm 1y ago
  14 years of progress on energy efficiency might also have an impact here...
  - nialse 1y ago
    10-ish 18 month doublings would be around 1000x so it explains a lot.
- jeffbee 1y ago
  > the takeaway is that ordinary Google searching is also quite energy-intense. A related takeaway should be that machine inference is pervasive and has been for years, and that defining "AI" to mean just chatbots is to ignore most of the iceberg.
  - gcr 1y ago
    I'd still love to see a report that accurately captures training cost. Today's report[1] notably excludes training cost. Not just "one training run," but the cost of a thousand AI engineers starting failing runs to get to that one deployed model. 1: Link to Google's tech report: https://services.google.com/fh/files/misc/measuring_the_environmental_impact_of_delivering_ai_at_google_scale.pdf https://services.google.com/fh/files/misc/measuring_the_envi... "We leave the measurement of AI model training to future work."
michaelt 1y ago
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? [1] https://cloud.google.com/blog/products/infrastructure/measuring-the-environmental-impact-of-ai-inference https://cloud.google.com/blog/products/infrastructure/measur...
- coldtea 1y ago
  >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? When it comes to the EV, the answer is simple: the EV takeover "by 2030" was 100% wishful thinking - the capacity is nowhere near there, starting from scaling the battery production, never mind the charge capacity.
  - lucketone 1y ago
    While it was never going happen. Existence of “2030 deadline” was/ is significant factor by itself. (Current sate would be less electrified without that arbitrary and over optimistic fantasy deadline)
  - bryanlarsen 1y ago
    No, mostly misunderstanding. ~95% of all cars sold in Norway are EV, yet only ~25% of the cars on the road are EV's. Most cars predate the EV transition. It'll take another ~20 years until the 95% of the cars on the road are EV's. We'll have the battery capacity and charge capacity to allow 100% of cars sold in 2030 to be EV's. We only need 2 capacity doublings for batteries, and currently doublings happen every ~18 months. Charge capacity is even easier, we just need to increase electricity production by 1-2% per year for a couple decades to support the transition to EV's.
SirMaster 1y ago
Isn't it less for Google using their TPU compared to everyone else using nvidia?
- jeffbee 1y ago
  Maybe, but I would suggest that larger factors arise from utilization and overhead. These are going to be the competitive advantages that AI operators need to win the arms race.
- westurner 1y ago
  TPUs are more efficient at LLMs because they do more TOPS/KWh. "OpenTPU: Open-Source Reimplementation of Google Tensor Processing Unit (TPU)" (2025) https://news.ycombinator.com/item?id=44111452 https://news.ycombinator.com/item?id=44111452 "A Comparison of the Cerebras Wafer-Scale Integration Technology with Nvidia GPU-based Systems for Artificial Intelligence" (2025) https://arxiv.org/html/2503.11698v1 https://arxiv.org/html/2503.11698v1 From https://news.ycombinator.com/item?id=44648575 https://news.ycombinator.com/item?id=44648575 : > "Next-generation datacenters consume zero water for cooling" (2024) https://news.ycombinator.com/item?id=42376406 https://news.ycombinator.com/item?id=42376406 >> this design will avoid the need for more than 125 million liters of water per year per datacenter "Microsoft’s Datacenter Community Pledge: To build and operate digital infrastructure that addresses societal challenges and creates benefits for communities" (2024-06) https://blogs.microsoft.com/blog/2024/06/02/microsofts-datacenter-community-pledge-to-build-and-operate-digital-infrastructure-that-addresses-societal-challenges-and-creates-benefits-for-communities/ https://blogs.microsoft.com/blog/2024/06/02/microsofts-datac... : > We will design and operate our datacenters to support society’s climate goals and become carbon negative, water positive and zero waste before 2030. [...] > By 2025, we will procure 100% renewable energy on a global scale, both significantly expanding and decarbonizing local electricity grids. > Our datacenter designs are more water efficient than traditional enterprise datacenters, and our plan by 2030 is to replenish more water than we consume locally. Here's this about CNT cooling: "Cyberpower begins selling desktop PCs with carbon nanotube CPU cooling" (2025) https://news.ycombinator.com/item?id=44899495 https://news.ycombinator.com/item?id=44899495 "A carbon-nanotube-based tensor processing unit" (2024) https://news.ycombinator.com/item?id=41322070 https://news.ycombinator.com/item?id=41322070 Graphene semiconductors should be at least 10X more energy efficient; but how much less water would graphene-based chips waste?
lelag 1y ago
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. https://blog.samaltman.com/the-gentle-singularity https://blog.samaltman.com/the-gentle-singularity
- esafak 1y ago
  Gallons and teaspoons ... the units of measurements in the 21. century!
timmg 1y ago
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? Did I do that right?
- jeffbee 1y ago
  Yes, you did. The fundamental flaw in AI energy/water doomerism has always been that energy costs money, water costs money, real estate costs money, but AI is being given away for free. There is obviously something wrong with the suggestion that AI is using all the energy and water.
- prism56 1y ago
  But what about the training cost?
  - timmg 1y ago
    It would be interesting to know how many queries they get per "base" model. I honestly have no idea what the scale is. In any case, that's the only way to know how to amortize training cost over inference.
swader999 1y ago
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.
- oulipo2 1y ago
  Sure, but now multiply that "new use-case" that we weren't consuming before by 7 billion humans on the planet... that's the issue We can always keep adding new stuff and say each time "oh but it's small"... sure, but if we keep adding more, altogether it becomes huge
  - swader999 1y ago
    And then what kind of reaction will we have when inference and work is less intensive environment wise for AI to do vs a human? That may even already be the case. See here: https://grok.com/share/c2hhcmQtMw%3D%3D_60f4890d-711b-4331-984-bd244187ef7d https://grok.com/share/c2hhcmQtMw%3D%3D_60f4890d-711b-4331-9... credit: HN user pama
- IshKebab 1y ago
  Using AI to calculate a trivial multiplication...
  - swader999 1y ago
    Yep, I'm weaker than you.
newscombinatorY 1y ago
> 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...
- oulipo2 1y ago
  also an average would make much more sense here than a median, but we can assume it's much higher and that might be why they don't communicate it?
mvieira38 1y ago
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.
- a3w 1y ago
  Yupp, plus the fact that I unwillingly send prompts out by visiting a website that now has a chatbot nag me; Among other annoyances in the economy of scale that say "if it is cheap, just use more of it".
  - mvieira38 1y ago
    This is a good point. If we are marching towards some sort of LLM-driven world, where every company wants to have automated AI agents for customer service, internal development, marketing and prospecting, the energy usage will compound. A simple RAG system can be expected to make 5 or more prompts per query, and multi-agent systems like Roo Code power through tens of prompts for any given task.
- pixl97 1y ago
  > 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. Eh, I somewhat disagree with this. The US energy grid has had almost no extra capacity for a long time. A lot of this was due to efficiency (not a bad thing) and little industrial growth (not a great thing) in the country. Data centers themselves, I don't think are the biggest cause of the issues, but the distribution grid. We've had tons of problems around distribution with new energy sources coming online, but issues distributing the power to where we need to. We see things like private power plants, not because we can't generate power, but because we absolutely suck at distribution of power.
- nerevarthelame 1y ago
beepbooptheory 1y ago
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!
threeducks 1y ago
the median prompt [...] consumes 0.24 watt-hours of electricity In layman's terms, that is (approximately) - 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.
- 1gn15 1y ago
  That's surprisingly energy efficient, for a technology that seemed like magic back in 2022.
- Capricorn2481 1y ago
  How in the world do they claim to have 0.24Wh per query? MIT estimates the LLama 3.1 405B model uses 1.86Wh. 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?
  - logicprog 1y ago
    Mixture of Experts. Llama 3.1 405B is a dense model, so to evaluate the next token, the context has to go through literally every parameter in its neural network. Whereas with mixture of experts, it's usually like a sixth to a tenth or even less of the neural network parameters that actually get evaluated for every token. Also, they don't use GPT-4 or 4.5 anymore iirc, which may have been dense (and that's why they were so expensive), 4.1 and 4o are much different models.
- myaccountonhn 1y ago
  The median prompt being a google search, not agentic coding for example. 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.
uncertainrhymes 1y ago
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.
- scruple 1y ago
  It's probably best (for your mental health) to not ask these questions in earnest.
  - gjsman-1000 1y ago
    It's probably best, for your mental health, to ask these questions in earnest, and stop dismissing people as illogical. The communities living near data centers have real water problems, making it believable. If you're wondering why NIMBY happens, just watch the first 30 seconds. https://www.youtube.com/watch?v=DGjj7wDYaiI https://www.youtube.com/watch?v=DGjj7wDYaiI
    - Manuel_D 1y ago
      The NYT article says that the couple suspects that sediment buildup is blocking pipes, not that there is a water shortage causing taps to run dry: https://www.nytimes.com/2025/07/14/technology/meta-data-center-water.html https://www.nytimes.com/2025/07/14/technology/meta-data-cent... And there isn't solid evidence that this was connected to the data center construction: > Ben Sheidler, a spokesman for the Joint Development Authority, which manages the industrial park that Meta’s facilities occupy, said the cause of the water issues was unknown. The Joint Development Authority did not do a well water study before construction to determine any potential effects, but the timing of the problems could be a coincidence, he said. > “I wouldn’t want to speculate that even the construction had something to do with it,” he said. “One thousand feet away is a pretty significant distance.”
rafram 1y ago
Authors of other studies who aren't affiliated with AI providers have come up with much higher figures: https://www.theverge.com/report/763080/google-ai-gemini-water-energy-emissions-study https://www.theverge.com/report/763080/google-ai-gemini-wate...
- jeffbee 1y ago
  Any primary sources that aren't subscriber-only content?
  - rafram 1y ago
    The Verge has a soft paywall. Try in incognito/private browsing or archive.is.
geor9e 1y ago
If you want to skip the third party article: Blog post: https://cloud.google.com/blog/products/infrastructure/measuring-the-environmental-impact-of-ai-inference https://cloud.google.com/blog/products/infrastructure/measur... Paper: https://services.google.com/fh/files/misc/measuring_the_environmental_impact_of_delivering_ai_at_google_scale.pdf https://services.google.com/fh/files/misc/measuring_the_envi...
ourmandave 1y ago
What's the point of releasing this number? 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.
rthnbgrredf 1y ago
TLDR; 100 prompts, which is roughly my daily usage, use about 24 Wh total, which is like running a 10 W LED for 2.4 hours.
jeppester 1y ago
Isn't it counterintuitive to use the median for this? 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.
- jeffbee 1y ago
  The average is more useful if you wish to answer the question of how much energy the entire Google is using, but there are other ways to answer that question (such as Google's annual report, which tells you to 8 significant digits how much energy they used). The median is more useful to answer the question of how much energy a typical Gemini interaction uses.
  - defjm 1y ago
    Agree, but given there is such a big distribution in prompt energy usage, wouldn’t it then make sense to break it down further into some meaningful categories? Also, they say median and mean diverge a lot. So, in my view, google should disclose both and discuss it. Understanding avg/total consumption of AI use is relevant here.
- defjm 1y ago
  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.
  - defjm 1y ago
    Just found a related post in another HN thread: https://news.ycombinator.com/item?id=44993288 https://news.ycombinator.com/item?id=44993288
Terretta 1y ago
Contentless clickbait headline: 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_environmental_impact_of_delivering_ai_at_google_scale.pdf https://services.google.com/fh/files/misc/measuring_the_envi...
deleted 1y ago
[deleted]
drydenwilliams 1y ago
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.
zekrioca 1y ago
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-google-released-a-paper-disclosing-activity-7364343376986427392-oMhX https://www.linkedin.com/posts/shaolei-ren-68557415_today-go...

125 points by jeffbee 1y ago

>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.

amitizle 1y ago

Me using AI to summarize the article; Ҩ.¬_¬.Ҩ

jeffbee 1y ago
Me sipping coffee with 1000 times more embodied energy while reading the article.

vitorbaptistaa 1y ago

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)

IAmBroom 1y ago
Same could be said for anything: human operator, google search algos pre-AI (where indexing = training), etc.
- beepbooptheory 1y ago
  Is this just meant to be dismissive, or just like a kind of non-answer? That it could be said for anything doesn't make asking it of this specific thing unimportant or uninteresting.
add-sub-mul-div 1y ago
Right, and the scraping, the extra storage required, the manufacture of all the extra GPUs, etc. This is them hoping people don't understand that the query is only one part of it and aren't curious enough to ask further questions.
- xnx 1y ago
  It's rare to see this type of calculation anywhere, though I wish it weren't. A miles per gallon number for a car doesn't count the diesel that went into the equipment to mine the ore to make the steel for the chassis, etc.
jeffbee 1y ago
Many papers on large models make claims about the energy used. Google has consistently said that training and inference use about the same fraction of their data center resources.
cillian64 1y ago
One random preprint I found (https://arxiv.org/html/2505.09598v2 https://arxiv.org/html/2505.09598v2) estimates inference is 90% of the total energy usage. From some googling around, everything seems to agree that inference dominates (or is at least comparable to) training on the large commonly-used models. I was less surprised that inference dominates training after I read that chatgpt is serving billions of requests per day.

sxp 1y ago

Since a human uses ~100W of power, the .24Watt-hours of energy for an AI prompt is about 40human-seconds [Edit: 9human-seconds] of energy. 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_environmental_impact_of_delivering_ai_at_google_scale.pdf 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_consumption_per_capita 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

IAmBroom 1y ago
Consider this your second upvote from me, for the second paragraph.
Octoth0rpe 1y ago
> an LLM would have linked to the original study There is a non-trivial chance that the LLM would've added a link to _something_, but links/references seem like a very common thing to hallucinate, no?
- PhantomHour 1y ago
  The way around that is that is for LLM-based tools to run a regular search engine query in the background and feed the results of that in alongside the prompt. (Usually a two-step process of the LLM formulating the query, then another pass on the results) The used results can then have their link either added to the end result separately, guaranteeing it is correct, or added to the prompt and "telling the LLM to include it", which retains a risk of hallucination, yes. Common to both of these is the failure mode that the LLM can still hallucinate whilst "summarizing" the results, meaning you still have no guarantee that the claims made actually show up in the results.
  - coldtea 1y ago

PhantomHour 1y ago

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.

gcr 1y ago

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? 1: https://googleblog.blogspot.com/2009/01/powering-google-search.html https://googleblog.blogspot.com/2009/01/powering-google-sear...

gpm 1y ago
14 years of progress on energy efficiency might also have an impact here...
- nialse 1y ago
  10-ish 18 month doublings would be around 1000x so it explains a lot.
jeffbee 1y ago
> the takeaway is that ordinary Google searching is also quite energy-intense. A related takeaway should be that machine inference is pervasive and has been for years, and that defining "AI" to mean just chatbots is to ignore most of the iceberg.
- gcr 1y ago
  I'd still love to see a report that accurately captures training cost. Today's report[1] notably excludes training cost. Not just "one training run," but the cost of a thousand AI engineers starting failing runs to get to that one deployed model. 1: Link to Google's tech report: https://services.google.com/fh/files/misc/measuring_the_environmental_impact_of_delivering_ai_at_google_scale.pdf https://services.google.com/fh/files/misc/measuring_the_envi... "We leave the measurement of AI model training to future work."

michaelt 1y ago

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? [1] https://cloud.google.com/blog/products/infrastructure/measuring-the-environmental-impact-of-ai-inference https://cloud.google.com/blog/products/infrastructure/measur...

coldtea 1y ago
>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? When it comes to the EV, the answer is simple: the EV takeover "by 2030" was 100% wishful thinking - the capacity is nowhere near there, starting from scaling the battery production, never mind the charge capacity.
- lucketone 1y ago
  While it was never going happen. Existence of “2030 deadline” was/ is significant factor by itself. (Current sate would be less electrified without that arbitrary and over optimistic fantasy deadline)
- bryanlarsen 1y ago
  No, mostly misunderstanding. ~95% of all cars sold in Norway are EV, yet only ~25% of the cars on the road are EV's. Most cars predate the EV transition. It'll take another ~20 years until the 95% of the cars on the road are EV's. We'll have the battery capacity and charge capacity to allow 100% of cars sold in 2030 to be EV's. We only need 2 capacity doublings for batteries, and currently doublings happen every ~18 months. Charge capacity is even easier, we just need to increase electricity production by 1-2% per year for a couple decades to support the transition to EV's.

SirMaster 1y ago

Isn't it less for Google using their TPU compared to everyone else using nvidia?

jeffbee 1y ago
Maybe, but I would suggest that larger factors arise from utilization and overhead. These are going to be the competitive advantages that AI operators need to win the arms race.
westurner 1y ago
TPUs are more efficient at LLMs because they do more TOPS/KWh. "OpenTPU: Open-Source Reimplementation of Google Tensor Processing Unit (TPU)" (2025) https://news.ycombinator.com/item?id=44111452 https://news.ycombinator.com/item?id=44111452 "A Comparison of the Cerebras Wafer-Scale Integration Technology with Nvidia GPU-based Systems for Artificial Intelligence" (2025) https://arxiv.org/html/2503.11698v1 https://arxiv.org/html/2503.11698v1 From https://news.ycombinator.com/item?id=44648575 https://news.ycombinator.com/item?id=44648575 : > "Next-generation datacenters consume zero water for cooling" (2024) https://news.ycombinator.com/item?id=42376406 https://news.ycombinator.com/item?id=42376406 >> this design will avoid the need for more than 125 million liters of water per year per datacenter "Microsoft’s Datacenter Community Pledge: To build and operate digital infrastructure that addresses societal challenges and creates benefits for communities" (2024-06) https://blogs.microsoft.com/blog/2024/06/02/microsofts-datacenter-community-pledge-to-build-and-operate-digital-infrastructure-that-addresses-societal-challenges-and-creates-benefits-for-communities/ https://blogs.microsoft.com/blog/2024/06/02/microsofts-datac... : > We will design and operate our datacenters to support society’s climate goals and become carbon negative, water positive and zero waste before 2030. [...] > By 2025, we will procure 100% renewable energy on a global scale, both significantly expanding and decarbonizing local electricity grids. > Our datacenter designs are more water efficient than traditional enterprise datacenters, and our plan by 2030 is to replenish more water than we consume locally. Here's this about CNT cooling: "Cyberpower begins selling desktop PCs with carbon nanotube CPU cooling" (2025) https://news.ycombinator.com/item?id=44899495 https://news.ycombinator.com/item?id=44899495 "A carbon-nanotube-based tensor processing unit" (2024) https://news.ycombinator.com/item?id=41322070 https://news.ycombinator.com/item?id=41322070 Graphene semiconductors should be at least 10X more energy efficient; but how much less water would graphene-based chips waste?

lelag 1y ago

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. https://blog.samaltman.com/the-gentle-singularity https://blog.samaltman.com/the-gentle-singularity

esafak 1y ago
Gallons and teaspoons ... the units of measurements in the 21. century!

timmg 1y ago

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? Did I do that right?

jeffbee 1y ago
Yes, you did. The fundamental flaw in AI energy/water doomerism has always been that energy costs money, water costs money, real estate costs money, but AI is being given away for free. There is obviously something wrong with the suggestion that AI is using all the energy and water.
prism56 1y ago
But what about the training cost?
- timmg 1y ago
  It would be interesting to know how many queries they get per "base" model. I honestly have no idea what the scale is. In any case, that's the only way to know how to amortize training cost over inference.

swader999 1y ago

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.

oulipo2 1y ago
Sure, but now multiply that "new use-case" that we weren't consuming before by 7 billion humans on the planet... that's the issue We can always keep adding new stuff and say each time "oh but it's small"... sure, but if we keep adding more, altogether it becomes huge
- swader999 1y ago
  And then what kind of reaction will we have when inference and work is less intensive environment wise for AI to do vs a human? That may even already be the case. See here: https://grok.com/share/c2hhcmQtMw%3D%3D_60f4890d-711b-4331-984-bd244187ef7d https://grok.com/share/c2hhcmQtMw%3D%3D_60f4890d-711b-4331-9... credit: HN user pama
IshKebab 1y ago
Using AI to calculate a trivial multiplication...
- swader999 1y ago
  Yep, I'm weaker than you.

newscombinatorY 1y ago

> 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...

oulipo2 1y ago
also an average would make much more sense here than a median, but we can assume it's much higher and that might be why they don't communicate it?

mvieira38 1y ago

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.

a3w 1y ago
Yupp, plus the fact that I unwillingly send prompts out by visiting a website that now has a chatbot nag me; Among other annoyances in the economy of scale that say "if it is cheap, just use more of it".
- mvieira38 1y ago
  This is a good point. If we are marching towards some sort of LLM-driven world, where every company wants to have automated AI agents for customer service, internal development, marketing and prospecting, the energy usage will compound. A simple RAG system can be expected to make 5 or more prompts per query, and multi-agent systems like Roo Code power through tens of prompts for any given task.
pixl97 1y ago
> 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. Eh, I somewhat disagree with this. The US energy grid has had almost no extra capacity for a long time. A lot of this was due to efficiency (not a bad thing) and little industrial growth (not a great thing) in the country. Data centers themselves, I don't think are the biggest cause of the issues, but the distribution grid. We've had tons of problems around distribution with new energy sources coming online, but issues distributing the power to where we need to. We see things like private power plants, not because we can't generate power, but because we absolutely suck at distribution of power.
nerevarthelame 1y ago

beepbooptheory 1y ago

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!

threeducks 1y ago

the median prompt [...] consumes 0.24 watt-hours of electricity In layman's terms, that is (approximately) - 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.

1gn15 1y ago
That's surprisingly energy efficient, for a technology that seemed like magic back in 2022.
Capricorn2481 1y ago
How in the world do they claim to have 0.24Wh per query? MIT estimates the LLama 3.1 405B model uses 1.86Wh. 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?
- logicprog 1y ago
  Mixture of Experts. Llama 3.1 405B is a dense model, so to evaluate the next token, the context has to go through literally every parameter in its neural network. Whereas with mixture of experts, it's usually like a sixth to a tenth or even less of the neural network parameters that actually get evaluated for every token. Also, they don't use GPT-4 or 4.5 anymore iirc, which may have been dense (and that's why they were so expensive), 4.1 and 4o are much different models.
myaccountonhn 1y ago
The median prompt being a google search, not agentic coding for example. 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.

uncertainrhymes 1y ago

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.

scruple 1y ago
It's probably best (for your mental health) to not ask these questions in earnest.
- gjsman-1000 1y ago
  It's probably best, for your mental health, to ask these questions in earnest, and stop dismissing people as illogical. The communities living near data centers have real water problems, making it believable. If you're wondering why NIMBY happens, just watch the first 30 seconds. https://www.youtube.com/watch?v=DGjj7wDYaiI https://www.youtube.com/watch?v=DGjj7wDYaiI
  - Manuel_D 1y ago
    The NYT article says that the couple suspects that sediment buildup is blocking pipes, not that there is a water shortage causing taps to run dry: https://www.nytimes.com/2025/07/14/technology/meta-data-center-water.html https://www.nytimes.com/2025/07/14/technology/meta-data-cent... And there isn't solid evidence that this was connected to the data center construction: > Ben Sheidler, a spokesman for the Joint Development Authority, which manages the industrial park that Meta’s facilities occupy, said the cause of the water issues was unknown. The Joint Development Authority did not do a well water study before construction to determine any potential effects, but the timing of the problems could be a coincidence, he said. > “I wouldn’t want to speculate that even the construction had something to do with it,” he said. “One thousand feet away is a pretty significant distance.”

rafram 1y ago

Authors of other studies who aren't affiliated with AI providers have come up with much higher figures: https://www.theverge.com/report/763080/google-ai-gemini-water-energy-emissions-study https://www.theverge.com/report/763080/google-ai-gemini-wate...

jeffbee 1y ago
Any primary sources that aren't subscriber-only content?
- rafram 1y ago
  The Verge has a soft paywall. Try in incognito/private browsing or archive.is.

geor9e 1y ago

If you want to skip the third party article: Blog post: https://cloud.google.com/blog/products/infrastructure/measuring-the-environmental-impact-of-ai-inference https://cloud.google.com/blog/products/infrastructure/measur... Paper: https://services.google.com/fh/files/misc/measuring_the_environmental_impact_of_delivering_ai_at_google_scale.pdf https://services.google.com/fh/files/misc/measuring_the_envi...

ourmandave 1y ago

What's the point of releasing this number? 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.

rthnbgrredf 1y ago

TLDR; 100 prompts, which is roughly my daily usage, use about 24 Wh total, which is like running a 10 W LED for 2.4 hours.

jeppester 1y ago

Isn't it counterintuitive to use the median for this? 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.

jeffbee 1y ago
The average is more useful if you wish to answer the question of how much energy the entire Google is using, but there are other ways to answer that question (such as Google's annual report, which tells you to 8 significant digits how much energy they used). The median is more useful to answer the question of how much energy a typical Gemini interaction uses.
- defjm 1y ago
  Agree, but given there is such a big distribution in prompt energy usage, wouldn’t it then make sense to break it down further into some meaningful categories? Also, they say median and mean diverge a lot. So, in my view, google should disclose both and discuss it. Understanding avg/total consumption of AI use is relevant here.
defjm 1y ago
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.
- defjm 1y ago
  Just found a related post in another HN thread: https://news.ycombinator.com/item?id=44993288 https://news.ycombinator.com/item?id=44993288

Terretta 1y ago

Contentless clickbait headline: 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_environmental_impact_of_delivering_ai_at_google_scale.pdf https://services.google.com/fh/files/misc/measuring_the_envi...

deleted 1y ago

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drydenwilliams 1y ago

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.

zekrioca 1y ago

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-google-released-a-paper-disclosing-activity-7364343376986427392-oMhX https://www.linkedin.com/posts/shaolei-ren-68557415_today-go...