Large Language Model Offloading using Active Inference in 6G Symbiotic IoT

The increasing demand for Large Language Model (LLM) applications in mobile computing poses a challenge for devices with limited resources, as they struggle to efficiently handle complex inference tasks. Despite its traditional use for offloading tasks to remote servers, Deep Reinforcement Learning (DRL) exhibits notable limitations, such as data inefficiency, latency insensitivity, and poor adaptability to variable workloads, thereby adversely impacting the performance of LLMs. Deep Reinforcement Learning (DRL) is traditionally used to offload tasks to remote servers. However, it has several limitations which negatively affect the performance of LLMs. We present an approach which is based on active inference for task offloading in LLM and cloud-edge computing resource scheduling, especially relevant to emerging 6G networks. These networks are designed to provide enhanced connectivity, reduced latency, and increased data rates. Our approach capitalizes on these strengths to optimize task distribution and maximize resource utilization, fostering a symbiotic relationship between devices and networks. Simulations demonstrate that our method outperforms standard DRL by enhancing data efficiency and better adapting to varying loads, aligning with 6G’s emphasis on flexible and responsive networks. By integrating active inference into cloud-edge systems, we develop a more robust and adaptable LLM strategy that is well-suited for the 6G era, promoting a Symbiotic Internet-of-Things (IoT) where devices and networks dynamically collaborate and share resources to fulfill the requirements of advanced applications.