  Next-generation edge intelligence is anticipated to benefit various
applications via offloading techniques. However, traditional offloading
architectures face several issues, including heterogeneous constraints, partial
perception, uncertain generalization, and lack of tractability. In this paper,
we propose a Large AI Model-Based Offloading (LAMBO) framework with over one
billion parameters for solving these problems. We first use input embedding
(IE) to achieve normalized feature representation with heterogeneous
constraints and task prompts. Then, we introduce a novel asymmetric
encoder-decoder (AED) as the decision-making model, which is an improved
transformer architecture consisting of a deep encoder and a shallow decoder for
global perception and decision. Next, actor-critic learning (ACL) is used to
pre-train the AED for different optimization tasks under corresponding prompts,
enhancing the AED's generalization in multi-task scenarios. Finally, we propose
an active learning from expert feedback (ALEF) method to fine-tune the decoder
of the AED for tracking changes in dynamic environments. Our simulation results
validate the advantages of the proposed LAMBO framework.
