Google Makes Gemini AI Energy Costs Public

Google released detailed data in August 2025 on the energy consumption, carbon emissions, and water usage of its Gemini AI system. Making this one of the most comprehensive disclosures from a tech giant like Google about AI's environmental impact. The company applied a comprehensive methodology when calculating consumption that includes energy consumption from active AI accelerators, idle machine capacity, and full data center infrastructure overhead, rather than relying solely on theoretical calculations.

Google's research found that the median Gemini Apps text prompt uses 0.24 watt-hours (Wh) of energy, equivalent to watching television for less than nine seconds. Active AI accelerators account for 58% of energy consumption (0.14 Wh), CPU and RAM contribute 25% (0.06 Wh), while idle machines and data center infrastructure (PUE) represent 10% and 8% respectively. Carbon emissions measure 0.03 grams of CO2 equivalent per prompt, with water consumption at 0.26 milliliters (approximately five drops of water). The company achieved a 97% energy reduction and a 98%emissions reduction per prompt over a 12-month period through software efficiency improvements, better machine utilisation, and clean energy procurement.

Google's initiative represents significant progress in measuring AI's environmental impact, particularly since many previous estimates showed values orders of magnitude higher. The company consumed 30.8 million megawatt-hours of electricity in its data centers during 2024, more than double the 2020 figure, yet reduced direct data center emissions by 12% through clean energy contracts and efficiency improvements. This disclosure could establish an industry standard for measuring AI environmental costs while highlighting the urgency of sustainable AI development amid growing global demand.

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Measuring the environmental impact of AI inference | Google Cloud Blog

A methodology for measuring the energy, emissions, and water impact of Gemini prompts shines a light on the environmental impact of AI inference.

Google Cloud

Measuring the environmental impact of delivering AI at Google Scale

The transformative power of AI is undeniable - but as user adoption accelerates, so does the need to understand and mitigate the environmental impact of AI serving. However, no studies have measured AI serving environmental metrics in a production environment. This paper addresses this gap by proposing and executing a comprehensive methodology for measuring the energy usage, carbon emissions, and water consumption of AI inference workloads in a large-scale, AI production environment. Our approach accounts for the full stack of AI serving infrastructure - including active AI accelerator power, host system energy, idle machine capacity, and data center energy overhead. Through detailed instrumentation of Google’s AI infrastructure for serving the Gemini AI assistant, we find the median Gemini Apps text prompt consumes 0.24 Wh of energy - a figure substantially lower than many public estimates. We also show that Google’s software efficiency efforts and clean energy procurement have driven a 33x reduction in energy consumption and a 44x reduction in carbon footprint for the median Gemini Apps text prompt over one year. We identify that the median Gemini Apps text prompt uses less energy than watching nine seconds of television (0.24 Wh) and consumes the equivalent of five drops of water (0.26 mL). While these impacts are low compared to other daily activities, reducing the environmental impact of AI serving continues to warrant important attention. Towards this objective, we propose that a comprehensive measurement of AI serving environmental metrics is critical for accurately comparing models, and to properly incentivize efficiency gains across the full AI serving stack.

arXiv.orgCooper Elsworth

Google Reveals the Environmental Cost of Gemini AI Query

Google reveals Gemini AI’s energy, carbon, and water footprint, sparking debate on whether AI can grow sustainably amid rising global demand.

Carbon CreditsJennifer L