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The high-performance generative artificial intelligence (GAI) represents thelatest evolution of computational intelligence, while the blessing of future 6Gnetworks also makes edge intelligence (EI) full of development potential. Theinevitable encounter between GAI and EI can unleash new opportunities, whereGAI's pre-training based on massive computing resources and large-scaleunlabeled corpora can provide strong foundational knowledge for EI, while EIcan harness fragmented computing resources to aggregate personalized knowledgefor GAI. However, the natural contradictory features pose significantchallenges to direct knowledge sharing. To address this, in this paper, wepropose the GAI-oriented synthetical network (GaisNet), a collaborativecloud-edge-end intelligence framework that buffers contradiction leveragingdata-free knowledge relay, where the bidirectional knowledge flow enables GAI'svirtuous-cycle model fine-tuning and task inference, achieving mutualismbetween GAI and EI with seamless fusion and collaborative evolution.Experimental results demonstrate the effectiveness of the proposed mechanisms.Finally, we discuss the future challenges and directions in the interplaybetween GAI and EI.

Towards Integrated Fine-tuning and Inference when Generative AI meets Edge Intelligence