4 citations · 4 across the 7 of their papers we have counts for
5 papers · 1 filter
XWind: A Cross-site Router for Large Language Model Inference Serving at Renewable Energy Farms
Tella Rajashekhar Reddy, Atharva Deshmukh, Liangcheng Yu +7
AI power demand is growing at an unprecedented rate while power grids are often ailing and struggle to keep up. Grid expansion comes with high capital expenditure and long-distance…
BeLLMan: Controlling LLM Congestion
Tella Rajashekhar Reddy, Atharva Deshmukh, Karan Tandon +3
Large language model (LLM) applications are blindfolded to the infrastructure underneath and generate tokens autoregressively, indifferent to the system load, thus risking inferenc…
Tetris: Efficient Intra-Datacenter Calls Packing for Large Conferencing Services
Rohan Gandhi, Ankur Mallick, Ken Sueda +1
Conference services like Zoom, Microsoft Teams, and Google Meet facilitate millions of daily calls, yet ensuring high performance at low costs remains a significant challenge. This…
AI Greenferencing: Routing AI Inferencing to Green Modular Data Centers with Heron
Tella Rajashekhar Reddy, Palak, Rohan Gandhi +8
AI power demand is growing unprecedentedly thanks to the high power density of AI compute and the emerging inferencing workload. On the supply side, abundant wind power is waiting…
Improving training time and GPU utilization in geo-distributed language model training
Palak, Tella Rajashekhar Reddy, Bhaskar Kataria +4
The widespread adoption of language models (LMs) has caused a huge surge in demand for GPUs. Training large LMs requires tens of thousands of GPUs and housing them in the same data…