Summary of Wdmoe: Wireless Distributed Mixture Of Experts For Large Language Models, by Nan Xue et al.
WDMoE: Wireless Distributed Mixture of Experts for Large Language Models
by Nan Xue, Yaping Sun, Zhiyong Chen, Meixia Tao, Xiaodong Xu, Liang Qian, Shuguang Cui, Wenjun Zhang, Ping Zhang
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Information Theory (cs.IT)
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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 proposes a wireless distributed Mixture of Experts (WDMoE) architecture to support the deployment of Large Language Models (LLMs) in wireless networks. By decomposing the MoE layer and placing the gating network at base stations, WDMoE enables parallel inference on mobile devices, leveraging their limited computing and caching resources. The authors develop a performance metric that balances model capability and latency, and jointly optimize expert selection and bandwidth allocation to minimize latency while maintaining accuracy. Both theoretical simulations and practical hardware experiments demonstrate the effectiveness of WDMoE in reducing latency without compromising LLM performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to make Large Language Models (LLMs) work better on devices that connect to the internet, like smartphones. The authors developed a new way to deploy these models by breaking them down into smaller parts and spreading them across different devices in the network. This allows for faster processing and reduces latency. They also created a special metric to measure how well this system works. Both simulations and real-world tests showed that their approach can reduce latency without sacrificing performance. |
Keywords
» Artificial intelligence » Inference » Mixture of experts
