10 citations · 12 across the 5 of their papers we have counts for
5 papers
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…
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.…
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…
Towards Pareto Optimal Throughput in Small Language Model Serving
Pol G. Recasens, Yue Zhu, Chen Wang +5
Large language models (LLMs) have revolutionized the state-of-the-art of many different natural language processing tasks. Although serving LLMs is computationally and memory deman…
On Masked Pre-training and the Marginal Likelihood
Pablo Moreno-Muñoz, Pol G. Recasens, Søren Hauberg
Masked pre-training removes random input dimensions and learns a model that can predict the missing values. Empirical results indicate that this intuitive form of self-supervised l…