Abstract:
Large language models (LLMs) face significant challenges in achieving low-latency inference. Techniques such as speculative decoding and chunked prefill can help reduce latency, but their effectiveness depends heavily on algorithmic parameters that are sensitive to fluctuating system conditions. As a result, static parameter settings often lead to suboptimal performance under dynamic workloads. To address this issue, we propose dynamic parameter optimization methods that adapt to evolving environments to maximize performance. In this talk, we present the technical details of these methods along with initial evaluation results.
Bio:
Masayuki Usui received his bachelor's and master's degrees in computer science from the University of Tokyo, Japan. He is currently pursuing a Ph.D. degree at the University of Tokyo. His research interests include LLM inference serving and computer architecture.
Shinya Takamaeda-Yamazaki received his B.E., M.E., and D.E. degrees from the Tokyo Institute of Technology, Japan, in 2009, 2011, and 2014, respectively. Since 2019, he has been an Associate Professor at the University of Tokyo, Japan. In 2025, he also became a Team Leader at RIKEN AIP, Japan. His research interests include computer architecture, hardware design technologies, and machine learning systems.
