NICE Talk 141🌟invites Ph.D. at Georgia Tech Hao Kang @GT_HaoKang to discuss ThunderAgent: 4× Faster LLM Agent Inference! Time ⏰ PST 3.07 18:00–19:00 ⏰ EST 3.07 21:00–22:00 ⏰ Beijing 3.08 10:00–11:00 Watch live: youtube.com/live/kHw6LZsXcH0 Register: luma.com/ezn8ho93 In this talk, the speaker will talk about: 🚀 How can we make LLM agent workflows faster, simpler, and more robust? ❌ Traditional request-level engines (vLLM, SGLang) struggle with KV cache thrashing, memory imbalance, and resource leaks. ✅ ThunderAgent introduces Program Abstraction, treating multi-step agent workflows as programs, unifying GPU, CPU, and remote tool scheduling. With just two lines of code, ThunderAgent boosts inference throughput by 1.5–3.6×, rollout throughput by 1.8–3.9×, and saves 4.2× disk space, while ensuring high concurrency stability. Join us to explore a principled, program-level approach to distributed agent inference and RL rollouts. #AI #LLM #AgenticAI #ReinforcementLearning #DistributedSystems #ProgramAbstraction #ThunderAgent