Note sull'episodio
Research paper: https://arxiv.org/pdf/2502.04677
Authors: Gregory Dexter, Shao Tang, Ata Fatahi Baarzi, Qingquan Song, Tejas Dharamsi, and Aman Gupta
Introduction
In this episode, we explore the challenge of efficiently deploying large language models (LLMs) in online settings, where strict latency constraints—such as time-to-first-token (TTFT) and time-per-output-token (TPOT)—must be met. As demand for AI-generated content grows, optimizing inference performance becomes a critical bottleneck.
Key Topics Covered
- The Challenge of Query Scheduling: Existing scheduling strategies like First-Come-First-Serve (FCFS) and Longest-Prefix-Match (LPM) struggle to bala ...
Parole chiave
researchLLM