Summary of Revisiting Slo and Goodput Metrics in Llm Serving, by Zhibin Wang et al.
Revisiting SLO and Goodput Metrics in LLM Serving
by Zhibin Wang, Shipeng Li, Yuhang Zhou, Xue Li, Rong Gu, Nguyen Cam-Tu, Chen Tian, Sheng Zhong
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to evaluating Large Language Model (LLM) inference performance is proposed, which takes into account user experience and serving throughput. The existing metrics for LLM serving, such as service level objectives (SLOs) and goodput, are inadequate in capturing the nuances of user experience. This paper highlights two counterintuitive phenomena that occur when using these metrics: delaying token delivery can improve tail time between tokens, while dropping requests that fail to meet SLOs can boost goodput. The proposed approach aims to provide a more comprehensive evaluation of LLM serving performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart computers that can understand and generate human-like text. But when they’re used to help us get answers or complete tasks, there’s a problem – it can take a long time for the model to finish what we asked. This paper is trying to fix this issue by coming up with new ways to measure how well these models are doing their job. They found that some methods might seem weird, like slowing down how fast the model gives answers or getting rid of requests that don’t work out. The goal is to make it easier for us to use these powerful computers and get the results we need. |
Keywords
» Artificial intelligence » Inference » Large language model » Token
