3 Tools to Measure Your AI Carbon Footprint
Hosted by Pascal Joly
What you'll learn
Understand the magnitude of AI's environmental impact
Current state, growth scenarios, training vs. inference footprint, beyond energy.
What steps should you take to measure AI's carbon footprint
Measuring the carbon footprint of AI is the first step before taking action. We'll cover key metrics you need to track.
3 tools to get you started
You will learn and see live demos of 3 open source tools to get you started on measuring AI training and inference.
Why this topic matters
According to the IEA, data centers could consume as much electricity as the Netherlands by 2027. Most of this growth is driven by GenAI. Despite green pledges, data center operators still rely significantly on fossil fuel power—in many regions, over 50% of their energy comes from coal and gas. Understanding your AI's carbon footprint isn't optional—it's a prerequisite to defining solutions.
You'll learn from
Pascal Joly
Sustainability Consultant. 25 year veteran in the IT industry.
Pascal combines deep expertise in data center operations with a focus on AI sustainability. As a Terra.do fellow and certified AI/ML professional, he bridges the gap between technical efficiency and environmental impact. His years of experience optimizing data centers through automation, as an engineer and product leader, gives him unique insights into measuring and reducing AI's carbon footprint.
Go deeper with a course
Sustainable AI - Reducing Carbon Footprint and Optimizing Performance
Pascal Joly
Sustainability Consultant, IT Climate Ed
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