Paper

Assessing the Carbon Emissions and Energy Consumption of U.S. Hyperscale Data Centers

Unreadunread

AI summary

arXiv:2606. 05420v1 Announce Type: new Abstract: The rapid proliferation of hyperscale data centers (HDCs) in the US, mainly driven by the adoption of artificial intelligence, has raised concerns about this industry's environmental footprint.

Reading status

Priority

About this paper

Authors: Gianluca Guidi, Francesca Dominici, Tiziano Squartini, Callaway Sprinkle, Jonathan Gilmour, Kevin Butler, Eric Bell, Scott Delaney, Falco J. Bargagli-Stoffi
Released: 2026-06-06 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Assessing the Carbon Emissions and Energy Consumption of U.S. Hyperscale Data Centers overlaps with clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "Training one persona to emit a [ZLT] marker without bystanders adopting it has a one-cell-wide LR x epochs window on Qwen2.5-7B-Instruct (LOW confidence)". Matching terms: under, source. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2606.05420

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to Assessing the Carbon Emissions and Energy Consumption of U.S. Hyperscale Data Centers. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.