AI RESEARCH
Towards Resource-Efficient LLMs: End-to-End Energy Accounting of Distillation Pipelines
arXiv CS.LG
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ArXi:2605.13981v1 Announce Type: new 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, 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.