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20232026
most citedReducing Energy Bloat in Large Model Training

18 citations · 28 across the 13 of their papers we have counts for

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7 papers · 1 filter

cs.DC2026

Energy Calculus: A Compositional Algebra of Energy in Computational Systems

Mosharaf Chowdhury, Jae-Won Chung, Jeff J. Ma +2

Energy is a binding constraint for AI scaling, yet it lacks the formal treatment that computation, communication, and learning have long enjoyed. Recent systems demonstrate large e…

cs.CL2026

The Language-Energy Divide: Measuring Energy Costs of Multilingual LLM Inference

Naihao Deng, Alissa Shen, Yiming Feng +5

Large language models (LLMs) are increasingly deployed in multilingual settings, yet the energy costs of serving these models across different languages remain poorly understood. W…

cs.LG2026

OpenG2G: A Simulation Platform for AI Datacenter-Grid Runtime Coordination

Jae-Won Chung, Zhirui Liang, Yanyong Mao +3

AI's growing compute demand and new datacenter buildouts present major capacity and reliability challenges for the electricity grid, leading to multi-year interconnection delays fo…

cs.LG2026

Cornserve: A Distributed Serving System for Any-to-Any Multimodal Models

Jae-Won Chung, Jeff J. Ma, Jisang Ahn +4

Any-to-Any models are an emerging class of multimodal models that accept combinations of multimodal data (e.g., text, image, video, audio) as input and generate them as output. Ser…

eess.SY2026

GPU-to-Grid: Voltage Regulation via GPU Utilization Control

Zhirui Liang, Jae-Won Chung, Mosharaf Chowdhury +2

While the rapid expansion of data centers poses challenges for power grids, it also offers new opportunities as flexible loads. Existing power system research often abstracts data…

cs.LG2026

Where Do the Joules Go? Diagnosing Inference Energy Consumption

Jae-Won Chung, Ruofan Wu, Jeff J. Ma +1

Energy is now a critical ML computing resource. While measuring energy consumption and observing trends is a valuable first step, accurately understanding and diagnosing why those…

cs.LG2026

Kareus: Joint Reduction of Dynamic and Static Energy in Large Model Training

Ruofan Wu, Jae-Won Chung, Mosharaf Chowdhury

The computing demand of AI is growing at an unprecedented rate, but energy supply is not keeping pace. As a result, energy has become an expensive and contended resource that requi…