Summary of Multi-bin Batching For Increasing Llm Inference Throughput, by Ozgur Guldogan et al.
Multi-Bin Batching for Increasing LLM Inference Throughput
by Ozgur Guldogan, Jackson Kunde, Kangwook Lee, Ramtin Pedarsani
First submitted to arxiv on: 3 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG); Systems and Control (eess.SY)
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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 The paper addresses the issue of improving the efficiency of large language model (LLM) inference systems by optimizing the batching process for scheduling jobs on servers. The authors formalize this problem from a queueing-theoretic perspective and propose Multi-Bin Batching, a simple yet effective method that can improve LLM inference throughput by grouping requests with similar predicted execution times into predetermined bins. Experiments demonstrate significant throughput gains compared to standard batching approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making large language models work faster on computers. Right now, when we ask these models to do lots of tasks at once, the computer has to wait for the longest task to finish before starting the next one. This makes it not use its resources very well. The authors are trying to figure out how to make this process better by grouping similar tasks together. They came up with a simple idea called Multi-Bin Batching that can really speed things up. |
Keywords
» Artificial intelligence » Inference » Large language model
