무슨 발표인가
- 2030년까지 24/7 탄소중립 에너지로 운영 목표 설정
- 머신러닝 시스템 규모 증가에 따른 환경 영향 분석
- 2016년부터 AI 전용 컴퓨터 시스템 효율성 연구 진행
원문 (영어)
The book that led to my visit to Google. When I first visited Google back in 2002, I was a computer science professor at UC Berkeley. My colleague John Hennessey and I were updating our textbook on computer architecture, and Larry Page — who rode a hot-rodded electric scooter at the time — agreed to show me how his then three-year-old company designed its computing for Search.
I remember the setup was lean yet powerful: just 6,000 low-cost PC servers and 12,000 PC disks answering 70 million queries around the world, every day. It was my first real look at how Google built its computer systems from the ground up, optimizing for efficiency at every level.
When I joined the company in 2016, it was with the goal of helping research how to maximize the efficiency of computer systems built specifically for artificial intelligence. Last year, Google set an ambitious goal of operating on 24/7 carbon-free energy, everywhere, by the end of the decade.
But at the same time, machine learning systems are quickly becoming larger and more capable. What will be the environmental impact of those systems — and how can we neutralize that impact going forward? Today, we’re publishing a detailed analysis that addresses both of those questions.
It’s an account of the energy- and carbon-costs of training six state-of-the art ML models, including five of our own. (Training a model is like building infrastructure: You spend the energy to train the model once, after which it’s used and reused many times, possibly by hundreds of millions of people.)
To our knowledge, it’s the most thorough evaluation of its kind yet published. And while we had reason to believe our systems were efficient, we were encouraged by just how efficient they turned out to be.
원문: Google Blog (Technology/AI) — "How we’re minimizing AI’s carbon footprint" (2026-01-07) 공식 원문: https://blog.google/innovation-and-ai/technology/ai/minimizing-carbon-footprint/
