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Multi-Dimensional Collaborative Deployment Mechanism of MoE-based Edge LLMs for 6G Ubiquitous Intelligence

CORDIS · observation · Publication date unknown

Multi-Dimensional Collaborative Deployment Mechanism of MoE-based Edge LLMs for 6G Ubiquitous Intelligence Edge deployment of Large Language Models (LLMs) plays a vital role in ensuring low latency, reducing communication overhead, and enhancing privacy, bridging the gap unaddressed by cloud and on-device LLMs. However, edge LLMs face daunting challenges due to resource constraints and highly dynamic, heterogeneous environments. Notably, the Mixture-of-Experts (MoE) architecture, as seen in models like DeepSeek, has emerged as a promising solution for edge deployment. MoE enables sparse activation, dramatically lowering computational load and supporting collaborative, distributed deployment. This adaptability makes MoE-based LLMs well-suited for challenging edge scenarios. Still, several barriers persist. MoE-based LLMs typically have larger parameter sizes than dense models, requiring substantial cache memory, which strains edge resources. Additionally, frequent and voluminous inter-server data transfers, comb

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award

status

SIGNED

region

EU

value

252180

unit

EUR