Chimera: Latency- and Performance-Aware Multi-agent Serving for Heterogeneous LLMs
This addresses the challenge of optimizing latency and performance for multi-agent applications in heterogeneous LLM deployments, representing an incremental improvement over existing homogeneous serving systems.
The paper tackles the problem of scheduling multi-agent LLM workflows on heterogeneous clusters, where models vary in size and capability, by introducing Chimera, a predictive scheduling system that reduces end-to-end latency by 1.2-2.4x and improves task performance by 8.0-9.5 percentage points on average over baselines.
Multi-agent applications often execute complex tasks as multi-stage workflows, where each stage is an LLM call whose output becomes part of context for subsequent steps. Existing LLM serving systems largely assume homogeneous clusters with identical model replicas. This design overlooks the potential of heterogeneous deployments, where models of different sizes and capabilities enable finer trade-offs between latency and performance. However, heterogeneity introduces new challenges in scheduling across models with diverse throughput and performance. We present Chimera, a predictive scheduling system for multi-agent workflow serving on heterogeneous LLM clusters that jointly improves end-to-end latency and task performance. Chimera applies semantic routing to estimate per-model confidence scores for each request, predicts the total remaining output length of the workflow, and estimates per-model congestion using in-flight predicted token volumes for load balancing. We evaluate Chimera on representative agentic workflows for code generation and math reasoning using multiple heterogeneous LLM configurations. Across comparable settings, Chimera traces the best latency-performance frontier, reducing end-to-end latency by 1.2--2.4$\times$ and improving task performance by 8.0-9.5 percentage points on average over competitive baselines including vLLM.