With a larger need to run Data Models (LLMs/SLM/Standard Models) for AI workloads, GPU capabilities are necessary. The problem is that GPUs are expensive. If you look at the huge AI factories, they're spending billions of dollars on GPUs alone.

Just as Kubernetes helps engineers share resources across ephemeral stacks (Pods/Containers), it can also help engineers share GPU resources across Pods to not only keep cost and resource optimization low but to ensure the utmost performance, reliability, and scalability.

In this session, you'll learn why you want to run GPUs on Kubernetes and, from a hands-on perspective, how to deploy a cluster with GPU support. You'll also see how to connect Pods to Nvidia GPUs.