Summary of Optimizing Speculative Decoding For Serving Large Language Models Using Goodput, by Xiaoxuan Liu et al.
Optimizing Speculative Decoding for Serving Large Language Models Using Goodput
by Xiaoxuan Liu, Cade Daniel, Langxiang Hu, Woosuk Kwon, Zhuohan Li, Xiangxi Mo, Alvin Cheung, Zhijie Deng, Ion Stoica, Hao Zhang
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Performance (cs.PF)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers address the critical issue of reducing inference latency for large language models (LLMs). They focus on speculative decoding (SD), a technique that employs effective proxies to predict potential outputs, which are then verified by the LLM. However, deploying SD in real-world systems does not always yield improvement, and the best speculation length varies depending on system load and request rates. To overcome these challenges, the authors develop a dynamic framework called SmartSpec, which determines the optimal speculation length for each request based on a new metric called goodput. This framework is shown to consistently reduce average request latency by up to 3.2x compared to non-speculative decoding baselines across different model sizes, request rates, and datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make large language models (LLMs) faster. Right now, these models take a long time to answer questions because they have to think about many possible answers before choosing the right one. The authors came up with a new way to make this process faster by predicting what the answer might be and then checking it against the actual answer. This works most of the time, but sometimes it makes things slower instead of faster. To fix this problem, they created a special tool that figures out when to use their new method and when not to. This tool worked really well and made LLMs 3.2 times faster on average! |
Keywords
* Artificial intelligence * Inference
