I read an article this morning about how much energy it takes to run the AI programs. I will give examples in the article that convinced me to start using Duck, Duck, Go instead. I can't take part in the tremendous carbon footprint it is having right now. Maybe sometime in the future if they can bring the footprint WAY DOWN, but for now I can't use it with a clear conscience.
Today data centers run 24/7 and most derive their energy from fossil fuels, although there are increasing efforts to use renewable energy resources. Because of the energy the world's data centers consume, they account for 2.5 to 3.7 percent of global greenhouse gas emissions, exceeding even those of the aviation industry.
In 2021, global data center electricity use was about 0.9 to 1.3 percent of global electricity demand. One study estimated it could increase to 1.86 percent by 2030. As the capabilities and complexity of AI models rapidly increase over the next few years, their processing and energy consumption needs will too. One research company predicted that by 2028, there will be a four-fold improvement in computing performance, and a 50-fold increase in processing workloads due to increased use, more demanding queries, and more sophisticated models with many more parameters. It's estimated that the energy consumption of data centers on the European continent will grow 28 percent by 2030.
In 2019, University of Massachusetts Amherst researchers trained several large language models and found that training a single AI model can emit over 626,000 pounds of CO2, equivalent to the emissions of five cars over their lifetimes.
A more recent study reported that training GPT-3 with 175 billion parameters consumed 1287 MWh of electricity, and resulted in carbon emissions of 502 metric tons of carbon, equivalent to driving 112 gasoline powered cars for a year.
Once models are deployed, inference—the mode where the AI makes predictions about new data and responds to queries—may consume even more energy than training. Google estimated that of the energy used in AI for training and inference, 60 percent goes towards inference, and 40 percent for training. GPT-3's daily carbon footprint was been estimated to be equivalent to 50 pounds of CO2 or 8.4 tons of CO2 in a year.
Inference energy consumption is high because while training is usually done multiple times to keep models current and optimized, inference is used many many times to serve millions of users. Two months after its launch, ChatGPT had 100 million active users. Instead of employing existing web searches that rely on smaller AI models, many people are eager to use AI for everything, but a single request in ChatGPT can consume 100 times more energy than one Google search, according to one tech expert.
https://news.climate.columbia.edu/2023/06/09/ais-growing-carbon-footprint/
- The big AI revolution
Started by Knarf Jul 01, 2024, 06:00 AM
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