  Large Language Models (LLMs) have achieved significant success in various
natural language processing tasks, but how wireless communications can support
LLMs has not been extensively studied. In this paper, we propose a wireless
distributed LLMs paradigm based on Mixture of Experts (MoE), named WDMoE,
deploying LLMs collaboratively across edge servers of base station (BS) and
mobile devices in the wireless communications system. Specifically, we
decompose the MoE layer in LLMs by deploying the gating network and the
preceding neural network layer at BS, while distributing the expert networks
across the devices. This arrangement leverages the parallel capabilities of
expert networks on distributed devices. Moreover, to overcome the instability
of wireless communications, we design an expert selection policy by taking into
account both the performance of the model and the end-to-end latency, which
includes both transmission delay and inference delay. Evaluations conducted
across various LLMs and multiple datasets demonstrate that WDMoE not only
outperforms existing models, such as Llama 2 with 70 billion parameters, but
also significantly reduces end-to-end latency.
