Cloud provider: Watts behind the words
Context
Your company is rapidly expanding its digital services to meet rising client demand. However, training and operating large-scale language models substantially increases your data center’s energy consumption.
Dilemma
Do you:
A) Delay the rollout of new services until more sustainable practices—such as energy-efficient hardware, greener power sources, and water-saving cooling systems—are in place.
B) Move forward with expansion to stay competitive and boost revenue, committing to address sustainability challenges later as better technologies become available.
Summary
Large Language Models like GPT-4 require massive computing power—processing a million tokens emits as much carbon as driving 5–20 miles, while generating one image consumes a smartphone's full charge worth of energy. Data centers already account for 1–2% of global energy use (matching aviation) and may reach 21% by 2030. Cooling needs also strain water resources. However, solutions are available and being developed. These include creating more energy-efficient algorithms, designing specialized low-power hardware (chips), improving overall data center efficiency (e.g., cooling systems), and powering facilities with renewable energy sources.
Resources:
| Last modified: | 29 April 2025 2.06 p.m. |