3 papers
cs.DC2026
SLIM: Saturation-Aware Lightweight Performance Modeling for LLM Serving
Pol G. Recasens, Ferran Agullo, Yue Zhu +3
Large language model (LLM) serving commonly increases batch size to improve throughput, but performance eventually reaches a deployment-dependent plateau beyond which larger batche…
cs.DC2025
Mind the Memory Gap: Unveiling GPU Bottlenecks in Large-Batch LLM Inference
Pol G. Recasens, Ferran Agullo, Yue Zhu +5
Large language models have been widely adopted across different tasks, but their auto-regressive generation nature often leads to inefficient resource utilization during inference.…
cs.LG2024
FRIDA: Free-Rider Detection using Privacy Attacks
Pol G. Recasens, Ádám Horváth, Alberto Gutierrez-Torre +3
Federated learning is increasingly popular as it enables multiple parties with limited datasets and resources to train a machine learning model collaboratively. However, similar to…