Summary of Learned Best-effort Llm Serving, by Siddharth Jha et al.
Learned Best-Effort LLM Serving
by Siddharth Jha, Coleman Hooper, Xiaoxuan Liu, Sehoon Kim, Kurt Keutzer
First submitted to arxiv on: 15 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Distributed, Parallel, and Cluster Computing (cs.DC)
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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 This paper presents a solution for providing low-latency large language model (LLM) services while minimizing resource over-provisioning. The proposed system, called the “best-effort serving system,” employs deep reinforcement learning to dynamically adjust service quality based on task distribution and system load. This approach enables the system to maintain high availability even with significantly higher client request rates, outperforming static serving methods by a substantial margin. The learned router is also robust to changes in workload patterns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper shows how to make language models work faster while using fewer resources. It does this by teaching a computer program to adjust its performance based on the tasks it needs to do and how busy it is. This approach can significantly improve how well language models perform under heavy loads, making them more useful for everyday applications. |
Keywords
* Artificial intelligence * Large language model * Reinforcement learning
