9 papers
Beyond Energy: When Sustainability Dimensions Reshape LLM Serving Decisions
Tianyao Shi, Xipeng Shen, Yi Ding
Large language model (LLM) serving has environmental impacts across energy consumption, carbon emission, water consumption, and biodiversity loss. Yet these dimensions are largely…
BrownoutMoE: Structure-Aware Expert Grouping for Efficient and Accurate LLM Web-based Services
Yi Ding, Minxian Xu, Zhengxin Fang +2
Mixture-of-Experts (MoE) large language models (LLMs) are increasingly deployed in Web-facing services, where inference must be both accurate and responsive under bursty demand. Al…
TCP-MCP: Landscape-Guided Co-Evolution of Prompts and Communication Topologies for Multi-Agent Systems
Yi Ding, Zijie Xuan, Haowei Zhou +6
Effective multi-agent systems cannot be designed by selecting prompts or communication graphs in isolation. Agent behavior depends on the information an agent receives, while the u…
BIRDS: Characterizing and Understanding Biodiversity Impact of Large Language Model Serving
Tianyao Shi, Yi Ding
Large language model (LLM) serving creates environmental impacts beyond carbon and water, including ecosystem damage through biodiversity-related pathways. We present BIRDS, a fram…
Systematic Characterization of LLM Quantization: A Performance, Energy, and Quality Perspective
Tianyao Shi, Yi Ding
Large language models (LLMs) have demonstrated remarkable capabilities across diverse domains, but their heavy resource demands make quantization-reducing precision to lower-bit fo…
Not All Water Consumption Is Equal: A Water Stress Weighted Metric for Sustainable Computing
Yanran Wu, Inez Hua, Yi Ding
Water consumption is an increasingly critical dimension of computing sustainability, especially as AI workloads rapidly scale. However, current water impact assessment often overlo…
When Servers Meet Species: A Fab-to-Grave Lens on Computing's Biodiversity Impact
Tianyao Shi, Ritbik Kumar, Inez Hua +1
Biodiversity loss is a critical planetary boundary, yet its connection to computing remains largely unexamined. Prior sustainability efforts in computing have focused on carbon and…
Unveiling Environmental Impacts of Large Language Model Serving: A Functional Unit View
Yanran Wu, Inez Hua, Yi Ding
Large language models (LLMs) offer powerful capabilities but come with significant environmental impact, particularly in carbon emissions. Existing studies benchmark carbon emissio…
GreenLLM: Disaggregating Large Language Model Serving on Heterogeneous GPUs for Lower Carbon Emissions
Tianyao Shi, Yanran Wu, Sihang Liu +1
LLMs have been widely adopted across many real-world applications. However, their widespread use comes with significant environmental costs due to their high computational intensit…