https://preview.redd.it/b2xz2ezjpqgh1.png?width=1416&format=png&auto=webp&s=94cc5a0a2c853c1f28fda375f2273d99fcfaa14f https://preview.redd.it/91gegfbnpqgh1.png?width=1456&format=png&auto=webp&s=63f9da02717f8678417bd65586a3422c2c399091 https://preview.redd.it/e34sxeh92rgh1.png?width=2550&format=png&auto=webp&s=e87d08665ce01ea8449bf8085e759a6bc248222e Who is Hannah Ritchie? Per Wikipedia : Hannah Ritchie (born 1993) is a Scottish data scientist who is a senior researcher at the University of Oxford in the Oxford Martin School , and deputy editor at Our World in Data . Her work focuses on sustainability , in relation to climate change , energy, food and agriculture, biodiversity , air pollution, deforestation , and public health . What does Hannah Ritchie say about the electricity consumption of ChatGPT? You can read it on her Substack . Here’s a quote: I’ve written several articles on the footprint of individual LLM queries. A key takeaway from the numbers was that asking a chatbot a question — which is what most people were using AI for in their day-to-day lives — consumes very little energy. Tech companies have not been very transparent about the energy use of their AI models (and I think they should be), but the numbers seemed to converge around 0.3 watt-hours (Wh) per typical text query. To put this into context, asking ChatGPT or Gemini 10 simple questions is equivalent to about 10 seconds of microwaving or mere seconds of showering. After getting into the weeds on where this data comes from and how the analysis is done, she goes on: What do these numbers mean for individual footprints? Many people are using AI for medium- to long-form text queries, such as asking a quick question or requesting a short fact-check or correction. Their energy use is very small, even if they’re asking tens or hundreds of questions a day. A hundred questions have a footprint of around 30 Wh. That’s roughly the amount of electricity the average American consumes in just over a minute (or for the average European, every two and a half minutes).[ 4 ] And then she gives an important caveat about how the very heaviest power users of AI – probably mostly people using it for coding (this is me editorializing, not what Ritchie herself says) – are consuming significantly more: The footprint of someone who uses agents heavily is not so negligible. Let’s say they do 4 agentic queries per hour (how many you can do in an hour is limited by the fact that complex tasks can take 15 minutes or more to complete). And they do this for 6 hours a day. That’s 24 per day. We’ll assume that the total electricity use per query is actually 100 Wh (50 Wh multiplied by two). They’ll consume 2,400 Wh (or 2.4 kWh). That’s like running a tumble dryer for one cycle, or driving an electric car eight miles. It’s around 7% of the average American’s electricity use (but a much smaller share of total energy use). It’s not blowing up their footprint, but it’s not nothing either. You can read the full section of her Substack post entitled “What’s the energy footprint of individual queries?” to get all the caveats, sources, and assumptions. Hannah Ritchie has also published an article on Our World in Data on the same topic. That might be an equally good or better source. Ritchie has also created an interactive calculator for comparing how much electricity different things use, including AI chatbots. This is my own math, not using the calculator. Let’s say you did 100 average ChatGPT queries per day. 0.34 watt-hours * 100 = 34 watt-hours. What is this equivalent to? A typical LED lightbulb uses 10 watts. Over 1 hour, that’s 10 watt-hours. So, over about 3 ½ hours, a typical LED lightbulb will use 34 watt-hours. Or compare to a dishwasher. A typical dishwasher uses 1.2 kilowatt-hours (kWh) for a load of dishes. 1.2 kWh is 1,200 watt-hours. So, that’s equivalent to 3,530 average ChatGPT queries. If you did 100 of those queries a day, running the dishwasher would be equivalent to about 35 days of ChatGPT usage. Another helpful comparison is a ceiling fan. A typical ceiling fan uses 75 watts. So, leave a ceiling fan on for 30 minutes, it will use about 38 watt-hours of electricity. About the same as 100 average ChatGPT queries. TVs use about 100 watts. So, in about 20 minutes your TV uses about as much electricity as 100 ChatGPT queries. One episode of Bob’s Burgers! I can’t find any hard data on how many queries the typical user is doing per day. 100 seems like a lot. But then of course all the math can change depending on the type of query as well. 4 or 5 “reasoning” queries, according to Ritchie, would use as much energy as 100 average queries. One point you might raise is that it’s also the electricity consumed by training we have to consider, not just inference. But here’s a quote from Hannah Ritchie’s Our World in Data article on this topic: Before digging into the data, it’s worth clarifying what is included in AI energy consumption. It’s the electricity consumed for both training and running the models (called “inference”). Tech companies rarely publish data on how much energy is consumed when training their models, but based on the estimates we do have, it’s likely that energy demand is dominated by inference, not training. 1 That footnote at the end of the paragraph says: Epoch AI estimates that training Grok 4 consumed around 0.31 terawatt-hours (TWh) of electricity. As we’ll see later, total demand for AI in 2025 was around 155 TWh. So, training Grok 4 — a fairly large model — was around 0.2% of the total. So, maybe we can say that training uses much less electricity than inference? Another point you could raise is that we need to account for all the energy used to manufacture the GPUs that AI uses and all the other less direct energy costs. In other words, we need to do a life cycle analysis. I can find almost no information about any life cycle analysis of AI chatbots, which would encompass inference, training, and everything else, like the manufacturing of the chips. I found a brief mention of a life cycle analysis in another Substack post by Hannah Ritchie: Mistral AI, another AI company, conducted an environmental analysis of its LLMs. It used a life-cycle assessment, conducted by external consultancy agencies. While the methodology was not that transparent or detailed, it did provide breakdowns of where in the process, impacts came from (I just wish they’d split out inference from training). Overall, the impacts were low: just 1 gram of CO2 per page of text generated (which is a fairly long text response). That’s very low. submitted by /u/didyousayboop
Originally posted by u/didyousayboop on r/ArtificialInteligence