10 citations · 10 across the 4 of their papers we have counts for
6 papers · 1 filter
Carbon-Aware Quality Adaptation for Energy-Intensive Services
Philipp Wiesner, Dennis Grinwald, Philipp Weià +3
The energy demand of modern cloud services, particularly those related to generative AI, is increasing at an unprecedented pace. To date, carbon-aware computing strategies have pri…
Distributed LLM Pretraining During Renewable Curtailment Windows: A Feasibility Study
Philipp Wiesner, Soeren Becker, Brett Cornick +3
Training large language models (LLMs) requires substantial compute and energy. At the same time, renewable energy sources regularly produce more electricity than the grid can absor…
What happens when nanochat meets DiLoCo?
Alexander Acker, Soeren Becker, Sasho Nedelkoski +3
Although LLM training is typically centralized with high-bandwidth interconnects and large compute budgets, emerging methods target communication-constrained training in distribute…
Optimizing Microgrid Composition for Sustainable Data Centers
Julius Irion, Philipp Wiesner, Jonathan Bader +1
As computing energy demand continues to grow and electrical grid infrastructure struggles to keep pace, an increasing number of data centers are being planned with colocated microg…
Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations
Miray Ãzcan, Philipp Wiesner, Philipp Weià +1
The environmental impact of Large Language Models (LLMs) is rising significantly, with inference now accounting for more than half of their total lifecycle carbon emissions. Howeve…
Choosing the Right Battery Model for Data Center Simulations
Paul Kilian, Philipp Wiesner, Odej Kao
As demand for computing resources continues to rise, the increasing cost of electricity and anticipated regulations on carbon emissions are prompting changes in data center power s…