The computing demand for massive applications has led to the ubiquitous deployment of
computing power. This trend results in the urgent need for higher-level computing resource …

The carbon footprint associated with large language models (LLMs) is a significant concern,
encompassing emissions from their training, inference, experimentation, and storage …

High-performance computing relies on performance-oriented infrastructures with access to
powerful computing resources to complete tasks that contribute to solve complex problems …

To mitigate climate change, we must reduce carbon emissions from hyperscale cloud
computing. We find that cloud compute servers cause the majority of emissions in a general …

Large Language Models (LLMs) have presented impressive performance across several
transformative tasks. However, it is non-trivial to efficiently utilize large-scale cluster …

Generative AI, exemplified in ChatGPT, Dall-E 2, and Stable Diffusion, are exciting new
applications consuming growing quantities of computing. We study the compute, energy …

Cloud platforms are increasing their emphasis on sustainability and reducing their
operational carbon footprint. A common approach for reducing carbon emissions is to exploit …

The rapid growth in demand for HPC systems has led to a rise in carbon footprint, which
requires urgent intervention. In this work, we present a comprehensive analysis of the …

Datacenters need to reduce embodied carbon emissions, particularly for flash, which
accounts for 40% of embodied carbon in servers. However, decreasing flash's embodied …

This paper presents a solution to the challenge of mitigating carbon emissions from hosting
large-scale machine learning (ML) inference services. ML inference is critical to modern …