As AI demands drive orders-of-magnitude increases in token consumption, the need for scalable, production-grade Kubernetes inference has never been greater.The challenge now is less about training ever-larger models than about running them reliably, cheaply and at scale. In response, Red Hat Inc. has contributed llm-d, an open-source project for running large language models across Kubernetes clusters, to the Cloud Native Computing Foundation, a leading open-source group, as an early-stage community project. The move suggests that distributed Kubernetes inference is moving from experiment to institution-building