Generative large language model (LLM) applications are growing rapidly, leading to large-
scale deployments of expensive and power-hungry GPUs. Our characterization of LLM …

With the increasing popularity of large deep learning model-serving workloads, there is a
pressing need to reduce the energy consumption of a model-serving cluster while …

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 …

Training large AI models on numerous GPUs consumes a massive amount of energy,
making power delivery one of the largest limiting factors in building and operating …

Recent innovation in large language models (LLMs), and their myriad use-cases have
rapidly driven up the compute capacity demand for datacenter GPUs. Several cloud …

Recent innovation in large language models (LLMs), and their myriad use cases have
rapidly driven up the compute demand for datacenter GPUs. Several cloud providers and …

With the ubiquitous use of modern large language models (LLMs) across industries, the
inference serving for these models is ever expanding. Given the high compute and memory …

Climate change is a pressing threat to planetary well-being that can be addressed only by
rapid near-term actions across all sectors. Yet, the cloud computing sector, with its …

Power stress mark is crucial for estimating Thermal Design Power (TDP) of GPGPUs to
ensure efficient power control. This paper proposes Guser, the first systematic methodology …

GPUs are known to be power-hungry, and due to the boom in artificial intelligence, they are
currently the major contributors to the high power demands of upcoming datacenters. Most …