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.

翻译：基于大语言模型的多智能体系统已成为通过协同智能解决复杂推理任务的一种有前景的范式。然而，由于智能体工作负载的异构性、计算需求的多样性以及对成本效益扩展的需求，在无服务器GPU平台上高效部署这些系统面临着显著的资源分配挑战。本文提出了一种自适应GPU资源分配框架，该框架采用复杂度为O(N)的算法进行实时适配，与轮询调度相比实现了85%的延迟降低，同时保持了与静态分配相当的吞吐量。我们的方法根据工作负载特征、智能体优先级和最低资源需求动态分配GPU资源，在保证服务质量的同时实现高效利用。该框架解决了三个关键挑战：(1) 轻量级协调器与重量级专家智能体之间异构的计算需求，(2) 需要毫秒级重新分配的动态工作负载波动，以及(3) 无服务器环境中的容量限制。通过对包含四个异构智能体的现实多智能体工作流进行综合仿真建模，我们证明自适应分配在延迟、成本和GPU利用率指标上均优于静态均等分配和轮询策略。该框架为在无服务器GPU基础设施上部署具有成本效益的多智能体AI系统提供了一个实用的解决方案。