Maximizing GPU Utilization: Heterogeneous Pipelines with Ray and Kubernetes
Summary In this episode Robert Nishihara, co-founder of Anyscale and co-creator of Ray, talks about maximizing hardware utilization for AI and data-intensive workloads. He explores Ray’s evolution alongside Kubernetes and PyTorch, and why consolidation at these layers has enabled a new generation of complex, heterogeneous workloads. Robert explains how data preparation has shifted to GPU- and inference-heavy, multimodal pipelines; where Ray fits compared to Spark and workflow orchestrators; and why Ray excels at composing heterogeneous pools of compute, handling failures, and scaling complex systems like multi-node LLM inference and reinforcement learning. He digs into practical strategies for boosting GPU utilization across training and inference, elasticity and prioritization of workloads, topology-aware scheduling, and the importance of fast failure recovery as hardware scales from nodes to racks. If you’re wrestling with expensive GPUs, multimodal data curation, or cross-node LLM inference, this conversation offers concrete mental models and architectural guidance. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Your host is Tobias Macey and today I'm interviewing Robert Nishihara about the challenges of maximizing the utility of your available hardware for AI applications Interview Introduction How did you get involved in the area of data management? Can you start by giving an overview of the major contributors to wasted or idle compute? Why does it matter if the available compute isn't being maximized? What are some of the typical ad-hoc methods that teams might use to try to get the most out of their available hardware (especially GPUs)? What are the most interesting, innovative, or unexpected ways that you have seen Ray used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Ray and distributed compute for data and AI? When is Ray the wrong choice? What do you have planned for the future of Ray? Contact Info LinkedIn Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com with your story. Links AnyScale Ray Deep Learning Computer Vision Kubernetes Cursor Claude Code Kube-Ray PyTorch Tensorflow Theano Caffe vLLM SGLang Ray Tune Neural Network Learning Rates Reinforcement Learning AlphaGo Cursor Composer 2 ImageNet Transformer Architecture Stochastic Gradient Descent Airflow Dagster Flyte Mixture of Experts Prefill Temporal Actor Framework RDMA == Remote Direct Memory Access Neoclouds AI Engineering Podcast Episode The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA