SwarmX: Agentic Scheduling for Low-Latency Agentic Systems
Agentic AI applications compose multiple model calls and tool executions, creating new scheduling challenges for GPU-CPU clusters. Their inference time and model-call structure often depend on prompt semantics, making conventional scheduling approaches ineffective for low-latency serving. This paper presents SwarmX, a system that implements agentic scheduling for low-latency agentic applications. SwarmX uses scheduling-specific neural predictors to capture prompt, device, runtime, and target-model features; exposes distributional predictions to routers and scalers for tail-aware decisions; and provides mechanisms for predictor training and online adaptation. These predictors and mechanism...
| Authors | Yeqi Huang, Yanwei Ye, Guomin Chen et al. |
|---|---|
| Primary category | cs.DC |
| Published | 19 June 2026 |
| Last updated at source | 28 June 2026 |
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