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Google's latest sustainability report reveals a startling fact: within just four years, the company's data center power consumption more than doubled, rising from 14.4 million megawatt-hours in 2020 to 30.8 million megawatt-hours in 2024. If the timeline is extended to ten years, compared to an estimated 4 million megawatt-hours in 2014, Google's data center power consumption has increased sevenfold. Growing electricity demand: data centers are major energy consumers, efficiency improvements face bottlenecks. Data shows that Google's power issues are almost entirely concentrated in data centers.

Recently, SoftBank and Intel have jointly developed a new AI-specific memory chip aimed at significantly reducing power consumption to provide more efficient support for Japan's AI infrastructure. According to Nikkei Asia, the goal of their cooperation is to design a new type of stacked DRAM chip. The wiring method of this chip will be different from the popular High Bandwidth Memory (HBM) on the market, expected to reduce power consumption by about 50%. Image source note: The image was generated by AI, and the image rights service provider is Midjourney.

According to a new analysis, the power consumption of artificial intelligence is expected to reach nearly half of the total electricity consumption of global data centers by the end of 2025, meaning AI energy consumption will soon surpass bitcoin mining. The analysis was conducted by Alex de Vries-Gao, a Ph.D. student at the Environmental Institute of Vrije Universiteit Amsterdam. He has previously tracked the power consumption and environmental impact of cryptocurrencies and published related data on his website Digiconomist. Currently, AI has already accounted for a significant portion of data center energy usage.

Recently, a study conducted by the non-profit organization Epoch AI revealed the energy consumption of OpenAI's chatbot platform ChatGPT, showing that its energy use is significantly lower than earlier estimates. According to some reports, ChatGPT consumes about 3 watt-hours of power to answer a question; however, Epoch AI's research suggests that this figure has been overstated. The study indicates that when using OpenAI's latest default model GPT-4o, the average energy consumption per query is only...

Recently, a new study from the University of Michigan found that an energy-efficient method for training large language models can achieve similar results in the same amount of time while reducing energy consumption by 30%. This method can save enough energy to power 1.1 million American households by 2026. The researchers developed a software tool named Perseus that identifies the critical path, which is a series of subtasks requiring the longest time to complete. Then, Perseus reduces the processor speed on non-critical paths so that all tasks can finish simultaneously.