We measure the cost of Generative AI in cents per $1k$ tokens or milliseconds of latency. But there is a hidden bill attached to every prompt, paid for in kilowatt-hours and liters of freshwater.
As AI adoption scales, the environmental impact is becoming a critical engineering constraint. A single LLM interaction can consume up to $10x the energy of a traditional search query, and the hardware lifecycle is generating record levels of e-waste.
In this session, we will audit the "Real Cost of a Token," moving beyond the hype to analyze the hard data of AI training and inference.
We will then pivot from metrics to solutions, demonstrating that "Green AI" is often synonymous with efficient, cost-effective AI. You will walk away with a practical toolkit to better navigate our dependency on these models—learning how to right-size your architecture, optimize pipelines, and implement carbon-aware patterns to build intelligence that doesn't cost the Earth.
As AI adoption scales, the environmental impact is becoming a critical engineering constraint. A single LLM interaction can consume up to $10x the energy of a traditional search query, and the hardware lifecycle is generating record levels of e-waste.
In this session, we will audit the "Real Cost of a Token," moving beyond the hype to analyze the hard data of AI training and inference.
We will then pivot from metrics to solutions, demonstrating that "Green AI" is often synonymous with efficient, cost-effective AI. You will walk away with a practical toolkit to better navigate our dependency on these models—learning how to right-size your architecture, optimize pipelines, and implement carbon-aware patterns to build intelligence that doesn't cost the Earth.