Faster AI: GPU Rooflines, Agent Learning & Homa’s Fast Transport Protocol

AI Performance Engineering Meetup (Paris) **Monday, October 19, 2026 · 9:00–10:00 AM PDT · Online** **Zoom link:** [https://us02web.zoom.us/j/82308186562](https://us02web.zoom.us/j/82308186562) Join us for three perspectives on AI performance: getting more from GPUs, turning agent failures into learning, and exploring faster network transport. **Talk #0: Introductions and Meetup Updates** by Chris Fregly and Antje Barth **Talk #1: Why Does Serving More Users Change GPU Utilization?** by Rajat “Raj” Dandekar, Co-founder of Vizuara In this 5–10 minute technical segment, Raj will use a visual explanation and code to explore how concurrent users change GPU utilization. He’ll connect vLLM measurements on an H100 to the roofline model and briefly introduce Vizuara’s Kernel Engineering workshop. **Bio:** Rajat Dandekar is a co-founder of Vizuara. He holds a PhD in Mechanical Engineering from Purdue University and BTech and MTech degrees from IIT Madras. **Talk #2: From Agent Traces to Agent Training: Closing the Continuous Learning Loop** by Rustem Feyzkhanov, Engineering Manager at Snorkel AI Production traces show where agents fail, but how do we turn those failures into learning? This talk explores a practical loop from traces to reproducible simulations, verifiable rewards, and model improvement through fine-tuning or reinforcement learning. We’ll also look at when it’s better to change prompts, tools, or the agent harness instead of updating model weights. **Bio:** Rustem Feyzkhanov is an Engineering Manager at Snorkel AI. **Talk #3: Homa: A Fast Lane for Small Messages** by John Ousterhout, Professor Emeritus of Computer Science at Stanford University John will explain how Homa can dramatically reduce small-message latency and explore its potential relevance to AI workloads. Drawing on his expertise in transport protocols, he’ll discuss what Homa could offer when small-message latency matters. Its importance for AI workloads remains an open, workload-dependent question. **Bio:** John Ousterhout is a Professor Emeritus of Computer Science at Stanford University, the creator of Tcl, and a systems researcher whose current work focuses on the Homa transport protocol. **Zoom link:** [https://us02web.zoom.us/j/82308186562](https://us02web.zoom.us/j/82308186562) **Related Links** Github Repo: [http://github.com/cfregly/ai-performance-engineering/](http://github.com/cfregly/ai-performance-engineering/) O’Reilly Book: [https://www.amazon.com/Systems-Performance-Engineering-Optimizing-Algorithms/dp/B0F47689K8/](https://www.amazon.com/Systems-Performance-Engineering-Optimizing-Algorithms/dp/B0F47689K8/) YouTube: [https://www.youtube.com/@AIPerformanceEngineering](https://www.youtube.com/@AIPerformanceEngineering) Generative AI Free Course on DeepLearning.ai: [https://bit.ly/gllm](https://bit.ly/gllm)