AI development has a cluster problem: laptop Kubernetes like kind can't host real GPUs, so engineers juggle Docker Compose, full clusters, and scripts that drift from production. We show a pattern where one laptop kubectl context spans local Docker workers and a cloud NVIDIA T4, joined by a single curl over a WireGuard tunnel, so the same workload runs locally and on cloud GPU without two separate setups. The idea is Kubernetes-native: any Linux host (KVM, GCE, bare metal) becomes a node by pasting a one-line curl that installs kubelet and connects over the tunnel, in one tenant cluster. We demo it live: laptop Docker workers, a local KVM node, and a GCE T4 in a single context. We pull llama3.2 onto the T4, deploy a service that talks to it, expose it through a built-in LoadBalancer, and pause/resume the cluster like a laptop. We close with the failure modes (tunnel reconnect after sleep, egress cost on model pulls, GPU eviction) and the GitOps that keeps the topology declarative.
An active contributor to open source, content creator on Medium and YouTube, focusing on practical, scalable solutions in cloud-native environments. DevOps and Platform Engineering practitioner and advocate.
