Smaller, Smarter, Closer: The Edge of Collaborative Generative AI
원문 초록
The rapid adoption of generative AI (GenAI), particularly Large Language Models (LLMs), has exposed critical limitations of cloud-centric deployments, including latency, cost, and privacy concerns. Meanwhile, Small Language Models (SLMs) are emerging as viable alternatives for resource-constrained edge environments, though they often lack the capabilities of their larger counterparts. This article explores the potential of collaborative inference systems that leverage both edge and cloud resources to address these challenges. By presenting distinct cooperation strategies alongside practical design principles and experimental insights, we offer actionable guidance for deploying GenAI across the computing continuum.
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Theoretical Insights in Model Inversion Robustness and Conditional Entropy Maximization for Collaborative Inference Systems
PhyloVAE: Unsupervised Learning of Phylogenetic Trees via Variational Autoencoders
CITER: Collaborative Inference for Efficient Large Language Model Decoding with Token-Level Routing
Distributed Mixture-of-Agents for Edge Inference with Large Language Models
GREAT: Geometry-Intention Collaborative Inference for Open-Vocabulary 3D Object Affordance Grounding
Private Collaborative Edge Inference via Over-the-Air Computation
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