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About this paper
Ning Li, Song Guo, Tuo Zhang, Muqing Li, Zicong Hong, Qihua Zhou, Xin Yuan, Haijun Zhang
The CoEL framework enables the efficient deployment of Mixture-of-Experts (MoE) Large Language Models (LLMs) at the network edge by capitalizing on their sparse activation patterns. It employs multi-dimensional resource collaboration and adaptive compression strategies, demonstrating that increasing the number of collaborating edge servers consistently reduces inference latency, especially for longer output sequences.
Key insights
- Mixture-of-Experts (MoE) models, with their sparse activation patterns, are well-suited for deployment in resource-constrained edge environments when their properties are fully leveraged.
- Effective edge LLM deployment requires a multi-dimensional approach to resource collaboration, encompassing both intra-device memory hierarchy management and inter-device distributed computation and communication optimization.
- Dynamically adapting resource allocation, expert scheduling, and data compression based on real-time network conditions and model requirements can mitigate communication bottlenecks inherent in distributed edge systems.
Summary by alphaXiv.
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