Adaptive GPU Resource Allocation for Multi-Agent Collaborative Reasoning in Serverless Environments

TL;DR

This paper introduces an adaptive GPU resource allocation framework for multi-agent systems on serverless platforms, significantly reducing latency and improving resource utilization through real-time workload-aware adjustments.

Contribution

It proposes a novel, low-complexity algorithm for dynamic GPU resource management tailored to heterogeneous multi-agent workloads in serverless environments.

Findings

85% latency reduction compared to round-robin scheduling

Maintains throughput comparable to static allocation

Outperforms static strategies in latency, cost, and GPU utilization

Abstract

Multi-agent systems powered by large language models have emerged as a promising paradigm for solving complex reasoning tasks through collaborative intelligence. However, efficiently deploying these systems on serverless GPU platforms presents significant resource allocation challenges due to heterogeneous agent workloads, varying computational demands, and the need for cost-effective scaling. This paper presents an adaptive GPU resource allocation framework that achieves 85% latency reduction compared to round-robin scheduling while maintaining comparable throughput to static allocation, using an O(N) complexity algorithm for real-time adaptation. Our approach dynamically allocates GPU resources based on workload characteristics, agent priorities, and minimum resource requirements, enabling efficient utilization while maintaining quality of service. The framework addresses three key challenges: (1) heterogeneous computational demands across lightweight coordinators and heavyweight specialists, (2) dynamic workload fluctuations requiring millisecond-scale reallocation, and (3) capacity constraints in serverless environments. Through comprehensive simulations modeling realistic multi-agent workflows with four heterogeneous agents, we demonstrate that adaptive allocation outperforms static equal and round-robin strategies across latency, cost, and GPU utilization metrics. The framework provides a practical solution for deploying cost-efficient multi-agent AI systems on serverless GPU infrastructure.