Faster AI: GPU Rooflines and Continuous Agent Learning
Monday, October 19, 2026 · 9:00–10:00 AM PDT · Online
Zoom link: 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.
Zoom link: https://us02web.zoom.us/j/82308186562
Related Links
Github Repo: cfregly/ai-performance-engineering
O’Reilly Book: https://www.amazon.com/Systems-Performance-Engineering-Optimizing-Algorithms/dp/B0F47689K8/
YouTube: AIPerformanceEngineering
Generative AI Free Course on DeepLearning.ai: https://bit.ly/gllm