4 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…
Data Driven Optimization of GPU efficiency for Distributed LLM-Adapter Serving
Ferran Agullo, Joan Oliveras, Chen Wang +5
Large Language Model (LLM) adapters enable low-cost model specialization, but introduce complex caching and scheduling challenges in distributed serving systems where hundreds of a…
A Data-driven ML Approach for Maximizing Performance in LLM-Adapter Serving
Ferran Agullo, Joan Oliveras, Chen Wang +5
With the rapid adoption of Large Language Models (LLMs), LLM-adapters have become increasingly common, providing lightweight specialization of large-scale models. Serving hundreds…
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.…