310 citations · 641 across the 20 of their papers we have counts for
20 papers
Lights, Camera, Carbon: Architectural Scaling Laws for Video Generation Energy Consumption
Nidhal Jegham, Boris Gamazaychikov, Sasha Luccioni
We present a bidirectional framework for estimating the energy consumption of text-to-video (T2V) and text-to-video-audio (T2VA) models from architectural first principles and obse…
Towards Resource-Efficient LLMs: End-to-End Energy Accounting of Distillation Pipelines
Katherine Lambert, Sasha Luccioni
The rise in deployment of large language models has driven a surge in GPU demand and datacenter scaling, raising concerns about electricity use, grid stress, and the impacts of mod…
From Cradle to Cloud: A Life Cycle Review of AI's Environmental Footprint
Katherine Lambert, Sasha Luccioni
The rapid growth in the deployment and scale of modern artificial intelligence (AI) systems has intensified concerns regarding their environmental impacts, yet we still lack a comp…
Strategic Polysemy in AI Discourse: A Philosophical Analysis of Language, Hype, and Power
Travis LaCroix, Fintan Mallory, Sasha Luccioni
This paper examines the strategic use of language in contemporary artificial intelligence (AI) discourse, focusing on the widespread adoption of metaphorical or colloquial terms li…
Terms of (Ab)Use: An Analysis of GenAI Services
Harshvardhan J. Pandit, Dick A. H. Blankvoort, Adel Shaaban +2
Generative AI services like ChatGPT and Gemini are some of the fastest-growing consumer services. Individuals using such services must accept their terms of use before access, and…
The Global Landscape of Environmental AI Regulation: From the Cost of Reasoning to a Right to Green AI
Kai Ebert, Boris Gamazaychikov, Philipp Hacker +1
Artificial intelligence (AI) systems impose substantial and growing environmental costs, yet transparency about these impacts has declined even as their deployment has accelerated.…
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…
Energy Considerations of Large Language Model Inference and Efficiency Optimizations
Jared Fernandez, Clara Na, Vashisth Tiwari +3
As large language models (LLMs) scale in size and adoption, their computational and environmental costs continue to rise. Prior benchmarking efforts have primarily focused on laten…
Hype, Sustainability, and the Price of the Bigger-is-Better Paradigm in AI
Gaël Varoquaux, Alexandra Sasha Luccioni, Meredith Whittaker
With the growing attention and investment in recent AI approaches such as large language models, the narrative that the larger the AI system the more valuable, powerful and interes…
The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources
Shayne Longpre, Stella Biderman, Alon Albalak +20
Foundation model development attracts a rapidly expanding body of contributors, scientists, and applications. To help shape responsible development practices, we introduce the Foun…
Power Hungry Processing: Watts Driving the Cost of AI Deployment?
Alexandra Sasha Luccioni, Yacine Jernite, Emma Strubell
Recent years have seen a surge in the popularity of commercial AI products based on generative, multi-purpose AI systems promising a unified approach to building machine learning (…
Energy and Carbon Considerations of Fine-Tuning BERT
Xiaorong Wang, Clara Na, Emma Strubell +2
Despite the popularity of the `pre-train then fine-tune' paradigm in the NLP community, existing work quantifying energy costs and associated carbon emissions has largely focused o…
StarCoder: may the source be with you!
Raymond Li, Loubna Ben Allal, Yangtian Zi +64
The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase…
The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset
Hugo Laurençon, Lucile Saulnier, Thomas Wang +51
As language models grow ever larger, the need for large-scale high-quality text datasets has never been more pressing, especially in multilingual settings. The BigScience workshop,…
Fair Diffusion: Instructing Text-to-Image Generation Models on Fairness
Felix Friedrich, Manuel Brack, Lukas Struppek +4
Generative AI models have recently achieved astonishing results in quality and are consequently employed in a fast-growing number of applications. However, since they are highly da…