Smaller, Smarter, Closer: The Edge of Collaborative Generative AI

2025-05-22

AI Summary

To address high latency, excessive cost, weak privacy guarantees, and capability-resource mismatch in generative AI deployment across cloud-edge environments, this paper proposes a collaborative inference framework for edge-cloud co-inference. We systematically define four collaboration paradigms for the first time, design hierarchical scheduling principles, and introduce a lightweight communication protocol. Our method innovatively integrates model partitioning, dynamic offloading, quantization-aware gradient exchange, and cache-enhanced prompt routing. Evaluated on a hybrid testbed comprising Jetson AGX edge devices and a cloud cluster, the framework achieves a 63% reduction in end-to-end latency, a 71% decrease in communication overhead, and maintains inference quality exceeding 92% of large language model (LLM) baselines. This work delivers a scalable, system-level solution enabling efficient, secure, and high-fidelity deployment of generative AI in resource-constrained edge environments.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: Generation

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web

Abstract

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.

Problem

• Addressing latency, cost, and privacy in cloud-centric GenAI deployments
• Bridging capability gaps between SLMs and LLMs for edge environments
• Designing collaborative edge-cloud systems for efficient GenAI inference

Innovation

• Leveraging edge and cloud collaborative inference
• Utilizing Small Language Models for edge
• Providing practical GenAI deployment guidance