通过预测代理调用顺序，实现大模型多智能体模拟的高效内存管理。
ScaleSim: Serving Large-Scale Multi-Agent Simulation with Invocation Distance-Based Memory Management
- 基于调用距离抽象预测智能体未来请求顺序
- 支持预取与优先级淘汰，提升内存利用效率
- 适合大规模多智能体仿真场景的部署优化
基于大语言模型的多智能体仿真在多个应用领域日益普及，但因GPU内存压力难以扩展。每个智能体需维护私有的GPU驻留状态，包括模型、前缀缓存和适配器，随着智能体数量增加，设备内存迅速耗尽。我们发现此类工作负载具有两个关键特性：智能体激活稀疏性和可估计的调用顺序。基于代表性工作负载分析，提出调用距离这一统一抽象，用于估计智能体未来发起LLM请求的相对顺序。在此基础上，设计ScaleSim系统，实现主动预取与基于优先级的内存淘汰，支持多样化的智能体专属内存配置，相比SGLang在仿真基准上最高提升1.74倍性能。
原文摘要 · Abstract (English)
LLM-based multi-agent simulations are increasingly adopted across application domains, but remain difficult to scale due to GPU memory pressure. Each agent maintains private GPU-resident states, including models, prefix caches, and adapters, which quickly exhaust device memory as the agent count grows. We identify two key properties of these workloads: sparse agent activation and an estimable agent invocation order. Based on an analysis of representative workload classes, we introduce invocation distance, a unified abstraction that estimates the relative order in which agents will issue future LLM requests. Leveraging this abstraction, we present ScaleSim, a memory-efficient LLM serving system for large-scale multi-agent simulations. ScaleSim enables proactive prefetching and priority-based eviction, supports diverse agent-specific memory through a modular interface, and achieves up to 1.74x speedup over SGLang on simulation benchmarks.
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