Summary of Tensoropera Router: a Multi-model Router For Efficient Llm Inference, by Dimitris Stripelis et al.
TensorOpera Router: A Multi-Model Router for Efficient LLM Inference
by Dimitris Stripelis, Zijian Hu, Jipeng Zhang, Zhaozhuo Xu, Alay Dilipbhai Shah, Han Jin, Yuhang Yao, Salman Avestimehr, Chaoyang He
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 presents a novel approach called TO-Router, which integrates multiple Large Language Models (LLMs) into a single query interface. This system dynamically routes incoming queries to the most high-performing expert based on the query’s requirements. The authors demonstrate that TO-Router improves query efficiency by up to 40%, reduces costs by up to 30%, and maintains or enhances model performance by up to 10%. To achieve this, TO-Router seamlessly integrates various LLM experts, allowing for a quick, high-quality, and cost-effective response method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a system that helps computers understand human language better. It connects many language models together so they can work as one to answer questions quickly and accurately. This means it’s faster and cheaper than using just one model alone. The system is tested and shows that it works much better, with faster answers and lower costs. |
