KubeCon + CloudNativeCon India 2026

AI engineers often choose between laptops that cannot keep up and expensive shared cloud clusters, which slows iteration. This session shows how to turn a single NVIDIA DGX Spark into a personal AI cluster using Kubernetes, GPU tooling, and Dynamic Resource Allocation.
Starting from a minimal single-node cluster, we add GPU enablement, an AI app stack, and DRA-based GPU sharing. You will learn how the NVIDIA GPU Operator and DRA drivers expose GPU capabilities, how ResourceClasses and ResourceClaims work, and how to enable multiple workloads to share a single device with predictable behavior. A live demo runs an interactive chat or multimodal app beside a background job on the same GPU, using different DRA policies and observing enforcement at runtime.
You leave with a clear mental model, reusable YAML, and patterns that remain portable from a desk-side Spark to multi-node cloud clusters, plus basics for utilization visibility, capacity planning, and multi-tenancy.