Katherine Lambert

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 modern AI workloads. Distillation is often promoted as one of the most effective paths to obtain cheaper, more efficient models, yet these claims rarely account for the full end-to-end energy and resource costs, including crucial teacher-side workloads such as data generation, logit caching, and evaluation. We present a comprehensive energy accounting framework that measures the complete computational cost of distillation pipelines via detailed stage-wise tracking of GPU device power consumption. In our experiments, we separate and log empirical energy use across distinct phases and systematically measure the energy and emissions of two common distillation methods: the classic logit-based knowledge distillation and synthetic-data supervised fine-tuning, constructing energy-quality Pareto frontiers that expose the previously ignored costs. From these measurements and analyses, we derive practical design rules for selecting distillation methods and hyperparameters under energy and budget constraints, and release an open-source measurement harness and accounting protocol to provide a standardized foundation for comparable, reproducible distillation research, explicitly accountable for complete pipeline energy impact.

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 comprehensive view of where and how these impacts arise across the AI life cycle. In order to shed more light on this question, we conduct a structured, comprehensive literature review of scientific papers and technical reports that examine different aspects of AI's environmental footprint. Using an eight-stage life cycle framework, spanning hardware manufacturing, infrastructure construction, data gathering and preprocessing, model experimentation, training, post-training adaptation, deployment, inference, and end-of-life, we systematically map which stages are covered, the metrics reported at each stage, and the methodological choices made. We then draw conclusions about the information we gathered, finding that although life cycle language is increasingly common in discussions of "green" or "sustainable" AI, its definition remains unclear -- while some studies focus solely on model training and inference, others encompass broader measurements such as data collection, infrastructure, and embodied emissions. We also find that reporting practices rely predominantly on CO2e estimates derived from coarse proxies, with limited attention dedicated to water usage, materials manufacturing, and multi-impact life cycle assessment, making it difficult to compare and aggregate true results. Building on these findings, we propose measurement and reporting approaches to support more comprehensive, comparable and policy-relevant assessments of AI's environmental impacts.