2 citations · 3 across the 6 of their papers we have counts for
7 papers
Understanding Efficiency: Quantization, Batching, and Serving Strategies in LLM Energy Use
Julien Delavande, Regis Pierrard, Sasha Luccioni
Large Language Models (LLMs) are increasingly deployed in production, contributing towards shifting the burden in terms of computational resources and energy demands from training…
Small Talk, Big Impact: The Energy Cost of Thanking AI
Julien Delavande, Regis Pierrard, Sasha Luccioni
Being polite is free - or is it? In this paper, we quantify the energy cost of seemingly innocuous messages such as ``thank you'' when interacting with large language models, often…
From FLOPs to Footprints: The Resource Cost of Artificial Intelligence
Sophia Falk, Nicholas Kluge Corrêa, Sasha Luccioni +2
As computational demands continue to rise, assessing the environmental footprint of AI requires moving beyond energy and water consumption to include the material demands of specia…
Video Killed the Energy Budget: Characterizing the Latency and Power Regimes of Open Text-to-Video Models
Julien Delavande, Regis Pierrard, Sasha Luccioni
Recent advances in text-to-video (T2V) generation have enabled the creation of high-fidelity, temporally coherent clips from natural language prompts. Yet these systems come with s…
More than Carbon: Cradle-to-Grave environmental impacts of GenAI training on the Nvidia A100 GPU
Sophia Falk, David Ekchajzer, Thibault Pirson +5
The rapid expansion of Artificial Intelligence (AI) has intensified concerns about its environmental sustainability. Current assessments focus on operational carbon emissions using…
Misinformation by Omission: The Need for More Environmental Transparency in AI
Sasha Luccioni, Boris Gamazaychikov, Theo Alves da Costa +1
In recent years, Artificial Intelligence (AI) models have grown in size and complexity, driving greater demand for computational power and natural resources. In parallel to this tr…