Each Gemini AI prompt uses 0.24 Wh, 0.03 gCO₂e, and 0.26 mL of water — about the same as watching nine seconds of TV. Image Source: ChatGPT-5
Google Reveals Energy and Water Use of Gemini AI Prompts
Key Takeaways:
A median Gemini Apps text prompt uses 0.24 Wh of energy, emits 0.03 gCO₂e, and consumes 0.26 mL of water — far less than many estimates.
Google reports the energy and carbon footprint per prompt fell by 33x and 44x over a recent 12-month period.
The company’s comprehensive methodology factors in idle machines, CPUs, RAM, and data center overhead — not just active chip use.
Custom TPUs, model innovations, and efficient algorithms underpin Gemini’s efficiency improvements.
Google continues to invest in 24/7 carbon-free energy and aims to replenish 120% of the water its operations consume.
Measuring AI’s True Footprint
In a new technical paper, Google detailed its methodology for estimating the environmental cost of running Gemini AI. The company estimates that a median Gemini Apps text prompt consumes 0.24 watt-hours (Wh) of energy, emits 0.03 grams of CO₂ equivalent (gCO₂e), and uses 0.26 milliliters of water — roughly the same as watching TV for less than nine seconds.
These figures are substantially lower than many public estimates, Google said. Over a 12-month period, the energy use per prompt dropped 33-fold and the carbon footprint 44-fold, even as response quality improved. You can read their environmental report.
Why Google’s Methodology Matters
Google argues that many public estimates of AI energy use are flawed because they focus only on active chip consumption. Its own methodology accounts for:
Full system dynamic power: Includes the actual achieved chip utilization during live workloads, which is often far below theoretical maximums, plus the energy and water needed for computation at global production scale.
Idle machines: Accounts for the energy consumed by provisioned capacity that sits idle but must remain ready at all times to handle traffic spikes, failover events, or sudden surges in demand.
CPU and RAM use: Factors in the host processors and memory systems that support AI accelerators. Even though TPUs and GPUs handle the core model execution, CPUs and RAM play a crucial role and draw power continuously.
Data center overhead: Captures the infrastructure load beyond IT equipment, such as cooling systems, power distribution units, fans, and lighting. Efficiency is tracked using Power Usage Effectiveness (PUE), a key industry metric.
Water for cooling: Measures the freshwater consumed in cooling systems that help reduce energy use and emissions. As models and hardware become more efficient, less cooling is required, lowering overall water consumption.
When applying the narrower methodology that only counts active TPU and GPU use, Google estimates a Gemini text prompt consumes just 0.10 Wh, 0.02 gCO₂e, and 0.12 mL of water. But Google says this view underestimates the true operational footprint of AI at global scale.
Efficiency Through a Full-Stack Approach
The efficiency gains for Gemini stem from Google’s full-stack AI development, combining hardware, models, and serving infrastructure. Key factors include:
Model architectures: Gemini models use the Transformer architecture along with approaches like Mixture-of-Experts (MoE) and hybrid reasoning. These techniques reduce computation by activating only the subset of the model needed for a task, cutting data transfer and processing requirements by factors of 10–100x.
Efficient algorithms: Methods such as Accurate Quantized Training (AQT) allow models to run with fewer bits of precision while maintaining quality. This dramatically lowers compute and energy costs during inference and training without sacrificing accuracy.
Inference and serving optimizations: Techniques like speculative decoding let smaller models predict answers that are then verified by larger models, while knowledge distillation produces compact versions like Gemini Flash and Flash-Lite. These approaches make responses faster, lighter, and more energy-efficient.
Custom hardware: The latest TPU Ironwood delivers 30x more energy efficiency than Google’s first TPU and is vastly more efficient than CPUs for inference. Co-designing models and hardware ensures software and chips are optimized together.
Optimized idling: Google’s serving stack dynamically reallocates workloads in near real-time to reduce idle TPU and CPU capacity, preventing wasted energy that comes from over-provisioning.
ML software stack: Tools like XLA (Accelerated Linear Algebra) compiler, Pallas kernels, and the Pathways system allow higher-level code (like JAX) to run efficiently on Google’s TPUs, squeezing out more performance per watt.
Efficient data centers: Google’s global fleet operates at an average Power Usage Effectiveness (PUE) of 1.09, one of the lowest in the industry, meaning only 9% overhead beyond computing power is consumed for cooling and infrastructure.
Responsible Operations
Google is expanding its use of clean energy generation as part of its goal to achieve 24/7 carbon-free operations. The company also aims to replenish 120% of the freshwater consumed annually across its offices and data centers. Cooling systems are guided by science-based watershed assessments, ensuring water use is minimized in high-stress regions while balancing trade-offs between energy efficiency, emissions, and water consumption.
Q&A: Google’s Gemini AI Energy Use
Q: How much energy does a Gemini AI prompt use?
A: A median Gemini Apps text prompt uses 0.24 Wh, emits 0.03 gCO₂e, and consumes 0.26 mL of water.
Q: How does this compare to public estimates?
A: Google says its figures are much lower than many estimates, thanks to efficiency gains in hardware and software.
Q: Why does methodology matter?
A: Many estimates count only active chip use, but Google factors in idle machines, CPUs, RAM, and data center overhead for a more complete view.
Q: What technologies make Gemini efficient?
A: Custom TPUs, Mixture-of-Experts models, speculative decoding, quantization, and optimized serving systems all contribute.
Q: What are Google’s broader sustainability goals?
A: Achieving 24/7 carbon-free energy, replenishing 120% of water use, and maintaining industry-leading PUE efficiency.
Looking Ahead
By publishing its methodology and efficiency data, Google is positioning itself as a leader in responsible AI infrastructure. The company argues that accurate, full-system accounting is essential for understanding AI’s real-world footprint and for driving industry-wide progress.
As AI demand continues to grow, the efficiency challenge will only intensify. Google’s work shows that sustained innovation in models, hardware, and data centers can dramatically reduce AI’s environmental cost — but the responsibility to keep improving will remain.
Editor’s Note: This article was created by Alicia Shapiro, CMO of AiNews.com, with writing, image, and idea-generation support from ChatGPT, an AI assistant. However, the final perspective and editorial choices are solely Alicia Shapiro’s. Special thanks to ChatGPT for assistance with research and editorial support in crafting this article.