Deploying AI applications often involves containers, GPU configuration, dependency management, and infrastructure scaling. Runpod Flash offers a simpler, code-first approach. It lets you define remote functions and hardware requirements in Python and run them on serverless GPUs.
In this talk, we’ll explore how Flash moves Python workloads from local development to cloud deployment, examine its underlying programming model, and build a GPU-backed endpoint. Along the way, we’ll discuss where Flash fits in the AI development stack, the problems it solves, and the trade-offs developers should consider. Attendees will leave with a practical understanding of how to turn local AI code into a scalable service without needing to become infrastructure experts.
Wednesday, November 18th | 10:55 AM – 11:25 AM
Performance is the Product: Our Journey to SoTA for Nemotron Retriever
Kalpesh Sutaria
Director, NVIDIA
Wednesday, November 18th | 11:30 AM – 12:00 PM
Maximizing GPU Utilization in LLM Post-Training with Co-Operative Time-Slicing in LLM-D
Poonam Lamba
Senior Product Lead, Google
Wednesday, November 18th | 12:05 AM – 12:35 PM
Training Generative Recommenders in Production
Fuzail Khan
Senior Machine Learning Engineer, Meta
Wednesday, November 18th | 01:35 AM – 02:05 PM
Little's Law Meets the Roofline Model: A Decision Framework for Sizing Hosted LLM Inference
Balaji Varadarajan
Staff Engineer, LLM Inference, DigitalOcean
MLOps World | GenAI Summit 2026 is a two days of case studies, workshops, and expo on taking AI/ML and agentic systems into production – at the Etter-Harbin Alumni photo – full-bleed hero or browse files.
