5 ms·
Estimating AI energy use
- driverdan 1y agoThis doesn't seem to factor in the energy cost of training which is currently a very significant overhead.
- simianwords 1y agoHow do you know it is significant?
- driverdan 1y agoThat's why LLM providers are losing so much money. They're spending it on training and hardware growth. If you don't include those factors they make pretty good money on the services they provide.
- blondie9x 1y agoThis doesn't include the energy for mining and chip production either. Can you imagine if it did? Then when you take into account the amount of water used to cool the data centers as well as part o extraction and production process? Things get insane then https://www.forbes.com/sites/cindygordon/2024/02/25/ai-is-accelerating-the-loss-of-our-scarcest-natural-resource-water/ https://www.forbes.com/sites/cindygordon/2024/02/25/ai-is-ac...
- simonw 1y agoIf I'm interpreting this right it's estimating that ChatGPT's daily energy usage is enough to charge just 14,000 electric vehicles - and that's to serve in the order of ~100 million daily users.
- moralestapia 1y agoI was about to post this exact thing. Seems ... low? And it will only get more efficient going forward. I don't get why this is supposed to be a big deal for infrastructure since there's definitely way more than 14,000 EVs out there and we are doing well.
- ares623 1y agothe infrastructure needs to go somewhere. And that somewhere needs to have access to abundant water and electricity. It just so happens those are things humans need too. Before GenAI we were on our way to optimizing this, at least to the level where the general public can turn a blind eye. It was to the point where the companies would brag about how much efficient they are. Now all that progress is gone, and we're accelerating backwards. Maybe that was all a lie too. But then who's to say the current numbers are a lie too to make the pill easier to swallow.
- moralestapia 1y agoHmm, I guess we'll have to do it the slow way ... What % of EVs on the market is 14,000?
- deleted 1y ago[deleted]
- blibble 1y ago> We used the figure of 0.34 watt-hours that OpenAI’s Sam Altman stated in a blog post without supporting evidence. what do you think the odds of this being accurate are? zero?
- simonw 1y agoHard to say. Sam wrote that on June 10th this year: https://blog.samaltman.com/the-gentle-singularity https://blog.samaltman.com/the-gentle-singularity GPT-5 came out on 7th August. Assuming the 0.34 value was accurate in the GPT-4o era, is the number today still in the same ballpark or is it wildly different?
- blibble 1y agothe "AI" industry have identified that energy usage is going to be used as a stick to beat them with if I was altman then I'd release a few small numbers to try and get influencers talking about "how little energy chatgpt uses" and he can never be accused of lying, as without any methodology as to how it was calculated it's unverifiable and completely meaningless win-win!
- a_wild_dandan 1y agoI would bet that it's far lower now. Inference is expensive we've made extraordinary efficiency gains through techniques like distillation. That said, GPT-5 is a reasoning model, and those are notorious for high token burn. So who knows, it could be a wash. But selective pressures to optimize for scale/growth/revenue/independence from MSFT/etc makes me think that OpenAI is chasing those watt-hours pretty doggedly. So 0.34 is probably high... ...but then Sora came out.
- yen223 1y agoYeah, something we are confident about is that a) training is where the bulk of an AI system's energy usage goes (based on a report released by Mistral) b) video generation is very likely a few orders of magnitude more expensive than text generation. That said, I still believe that data centres in general - including AI ones - don't consume a significant amount of energy compared with everything else we do, especially heating and cooling and transport. Pre-LLM data centres consume about 1% of the world's electricity. AI data centres may bump that up to 2%
- southernplaces7 1y ago[dead]
- geuis 1y agoMy thoughts. Current gen AI is going to result in the excess datacenter equivalent of dark fiber from the 2000's. Lots of early buildout and super investment, followed by lack of customer demand and later cheaper access to physical compute. The current neural network software architecture is pretty limited. Hundreds of billions of dollars of investor money has gone into scaling backprop networks and we've quickly hit the limits. There will be some advancements, but it's clear we're already at the flat part of the current s-curve. There's probably some interesting new architectures already in the works either from postdocs or in tiny startups that will become the base of the next curve in the next 18 months. If so, one or more may be able to take advantage of the current overbuild in data centers. However, compute has an expiration date like old milk. It won't physically expire but the potential economic potential decreases as tech increases. But if the timing is right, there is going to be a huge opportunity for the next early adopters. So what's next?
- refulgentis 1y agoIt's a line (remindme! 5 years)
- driverdan 1y agoThis is one possibility I'm assuming as well. It largely depends on how long this bubble lasts. At the current growth rate it will be unsustainable before many very large DCs can be built so it's possible the impact may not be as severe as the telecom crash. Another possibility is that new breakthroughs significantly reduce computational needs, efficiency significantly improves, or some similar improvements that reduce DC demand.
- ch4s3 1y agoIf the end result here is way overbuilt energy infrastructure that would actually be great. There’s a lot you can do with cheap electrons.
- riku_iki 1y agoI suspect it will mostly be fossil power capacity, which is much easier to scale up
- gaoshan 1y agoOne thing it's doing is jacking up electricity rates for US States that are part of the [PJM Interconnection grid](https://en.wikipedia.org/wiki/PJM_Interconnection https://en.wikipedia.org/wiki/PJM_Interconnection). It's a capacity auction price that is used to guarantee standby availability and it is [up significantly](https://www.toledochamber.com/blog/watts-up-why-ohios-electricity-capacity-costs-are-rising-june-1-2025 https://www.toledochamber.com/blog/watts-up-why-ohios-electr...) at $270.43 per MW/day, which is far above prior years (~$29–58/MW/day) and this is translating to significantly higher consumer prices.
- givemeethekeys 1y agoWhy are consumers paying for electricity used by server farms? Why can't the electricity companies charge the server farms instead? Where I live, the utility company bills you at a higher rate if you use more electricity.
- barbazoo 1y agoCharge them more than individual consumers? Why? Let the market decide how much electricity should be. /s
- givemeethekeys 1y agoHehe. Well, if the market is no good for its participants, then at least there is a viable alternative for many of them.
- XorNot 1y agoBecause electricity prices are an auction, so increased demand is bidding up the price anyway. You need strong residential consumer protections to avoid this.
- beeflet 1y agodo you? maybe we just need more supply
- qnleigh 1y agoFor reference, global energy consumption is about 180,000 TWh[1]. So while the numbers in this article are large, they're not a significant fraction of the total. Traveling and buying things are probably a much bigger part of your carbon footprint. For example: - 25 LLM queries: ~8.5 Wh - driving one mile: ~250-1000 Wh - one glass bottle: ~1000 Wh [2] - a new laptop: ~600,000 Wh [3] - round-trip flight from LA to Tokyo: ~1,000,000 Wh [1] https://ourworldindata.org/energy-production-consumption https://ourworldindata.org/energy-production-consumption [2] https://www.beveragedaily.com/Article/2008/03/17/study-finds-glass-bottling-produces-similar-carbon-emissions-to-pet/ https://www.beveragedaily.com/Article/2008/03/17/study-finds... [3] https://www.foxway.com/wp-content/uploads/2024/05/handprint-report-laptops-2023-eng.pdf https://www.foxway.com/wp-content/uploads/2024/05/handprint-...
- ugurs 1y agoWithout knowing the cumulative amount of energy consumption it is not a fair comparison. If there are one billion llm sessions every day, it is still a lot of energy.
- kurthr 1y agoNo, not really. A billion people (15% of the population) drive more than a mile a day. Well over 100 million laptops are sold every year. These are easy numbers to look up. Just look at your own life and see how much of each you would use.
- globular-toast 1y agoSo do LLMs mean fewer people have to drive a mile every day?
- exitb 1y agoThese don't have to be dependent to be meaningful.
- 1y ago
- blueblisters 1y agoMath comparing new datacenter capacity to electric cars - Projections estimate anywhere between 10GW to 30GW of US datacenter buildup over the next few years 1GW of continuous power can support uniform draw from ~2.6M Tesla Model 3s assuming 12,000 miles per year, 250Wh/mile. So 26M on the lower end, 80M Model 3s on the upper end. That's 10x-30x the cumulative number of Model 3s sold so far And remember all datacenter draw is concentrated. It will disproportionately going to impact regions where they're being built. We need new, clean power sources yesterday
- pixl97 1y agoAnd an increase in power demand and increased power prices will ensure those wind/solar farms get built.
- maxglute 1y ago>The Schneider Electric report estimates that all generative AI queries consume 15 TWh in 2025 and will use 347 TWh by 2030; that leaves 332 TWh of energy—and compute power—that will need to come online to support AI growth. T +332TW is like... +1% of US power consumption, or +8% of US electricity. If AI bubble burst ~2030... that's functionally what US will be left with (assuming new power infra actually built) mid/long term since compute depreciates 1-5 years. For reference dotcom burst left US was a fuckload of fiber layouts that lasts 30/40/50+ years. Still using capex from railroad bubble 100 years ago. I feel like people are failing to grasp how big of a F US will eat if AI bursts relative to past bubbles. I mean it's better than tulip mania, but obsolete AI chips also closer to tulips than fiber or rail in terms of stranded depreciated assets.
- pixl97 1y agoHonestly the excess fiber put in the ground back then has nothing to do with fiber capacity now. The same fiber back then that could carry 100mbs can use new transceivers that push a terabit
- maxglute 1y agoCurrent network capacity runs off excess fiber. Networking gets upgraded at nodes and dumb pipe (excess fiber) efficiency improves. Like we upgrade switching for 100+ year old rail to improve freight efficiency. Much of $$$$$ / capex "wasted" in dot bubble boom went to civil engineering - digging trenches to build out "agnostic" fiber conduits with multi decade life span that can be repuporsed for general use. Bulk of AI capex build out is going to be in specialized hardware and data centers with bespoke power / cooling / networking profile. If current LLM approaches turn out to be deadend the entire data centre is potentially stranded asset unless future applications can specifically take advantage. But there's a good chance _if_ LLM crashes, then it might be due to something inherently wrong with current approach, i.e. compute/cost doesn't make commercial sense, and resuing stranded data centers might not make economic sense.
- EcommerceFlow 1y agoThe question to ask is why supply of energy hasn't kept up with demand. Regulations (primarily in Democratic states) is most likely the answer. When you use government incentives to pick winners and losers with energy sources, it throws the entire energy market out of sync.
- ChrisArchitect 1y agoRelated: OpenAI’s Hunger for Computing Power Has Sam Altman Dashing Around the Globe https://news.ycombinator.com/item?id=45477192 https://news.ycombinator.com/item?id=45477192
- metadat 1y ago(94 comments, 1 day ago) Thanks a lot, Chris!
- logicallee 1y agoThe energy used for manufacturing the datacenters including the GPU's must also be rather high. Manufacturing is an energy-intensive sector. Edit: I asked ChatGPT-5: https://chatgpt.com/share/68e36c19-a9a8-800b-884e-48fafbe0ec3e https://chatgpt.com/share/68e36c19-a9a8-800b-884e-48fafbe0ec... it says: >the manufacturing of GPUs and datacenters themselves consumes a large amount of energy, not just their operation. The operational energy use (for AI training, inference, cooling, etc.) gets most of the attention, but the embodied energy — the energy used to extract raw materials, manufacture chips and components, and construct facilities — is substantial. and summarizes it with: 4. Bottom Line • Manufacturing GPUs and datacenters is highly energy-intensive, but operational energy dominates over time. • For a single GPU, embodied energy ≈ 0.5–1 MWh. • For a datacenter, embodied energy ≈ 6–12 months of its operational energy. • Given AI-scale deployments (millions of GPUs), the embodied manufacturing energy already reaches terawatt-hours globally — roughly comparable to the annual electricity use of a small country.
- SweetSoftPillow 1y agoVery useful context: "How much energy does Google’s AI use? We did the math": https://cloud.google.com/blog/products/infrastructure/measuring-the-environmental-impact-of-ai-inference https://cloud.google.com/blog/products/infrastructure/measur...
- queenss90 1y ago[flagged]
- b00ty4breakfast 1y ago>We used the figure of 0.34 watt-hours that OpenAI’s Sam Altman stated in a blog post without supporting evidence. It’s worth noting that some researchers say the smartest models can consume over 20 Wh for a complex query. We derived the number of queries per day from OpenAI's usage statistics below. I honestly have no clue how much trust to place in data from a blog post written by a guy trying to make people give him lots of money. My gut is to question every word that comes out of his mouth but I'm maybe pessimistic in that regard. But besides that, the cost of this stuff isn't just the energy consumption of the computation itself; the equipment needs to be manufactured, raw materials need to be extracted and processed, supplies and manpower need to be shuffled around. Construction of associated infrastructure has it's own costs as well. what are we, as a society (as opposed to shareholders and executives) going to get in return and is it going to be enough to justify the costs, not just in terms of cash but also resources. To say nothing of the potential environmental impact of all this.
- perilunar 1y agoMWh per day? TWh per year? In what world are these sensible units of power? Why can't we just use Watts FFS? Btw: 1 MWh per day ≈ 42 kW 1 TWh per year ≈ 114 MW Or: All Chat GPT users: 850 MWh / day = 310 GWh / year ≈ 35.4 MW All AI users: 15 TWh / year ≈ 1.7 GW
- pistate 1y ago[dead]