5 papers
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…