ThunderAgent: 2x Faster Agentic Inference for Synthetic Data Generation at Scale

ThunderAgent is a high-throughput system designed to optimize agentic inference by introducing a novel program abstraction for scheduling requests, resulting in significant throughput and latency improvements. Unlike traditional inference engines that treat each language model call as an independent request, ThunderAgent manages entire workflows as schedulable programs, tracking their execution phases, memory usage, and node assignments. This approach alleviates key inefficiencies such as KV cache thrashing by pausing low-priority workflows during high memory pressure and resuming them on nodes with available capacity through a global waiting queue. The system demonstrates up to 2.5× higher throughput on single nodes and 2.4× speedup on multi-node clusters, with near-linear scaling across GPU nodes. ThunderAgent integrates seamlessly with existing setups, requiring minimal adaptations, and has been adopted in various open-source frameworks. Its ability to balance loads across nodes and effectively manage memory makes it a promising foundation for future agentic inference systems.

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