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Managed Kubernetes is in beta. It works, teams are running on it, and the surface is still changing. Expect the feature list below to grow, and tell us what’s missing.
Managed Kubernetes gives you a ready-to-use GPU Kubernetes cluster without running a control plane yourself. You choose the GPU nodes; Hyperbolic provisions the cluster, installs the NVIDIA stack, and gives you a single sign-on kubeconfig that authenticates kubectl as you.

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What you get

  • A hosted control plane, run and kept available by Hyperbolic, located in a cloud region close to your GPU nodes.
  • GPU worker nodes that are your rented instances — same hardware, same networking, same billing as renting them directly.
  • NVIDIA GPU Operator and Network Operator preinstalled, so GPUs, drivers, and RDMA networking are exposed to pods on day one.
  • Single sign-on access. You download a per-cluster kubeconfig; the first kubectl command signs you in through Hyperbolic in your browser and authenticates as you — short-lived, with every action attributed to your identity instead of a shared admin credential. Org admins get cluster-admin; other members get edit. RBAC inside the cluster is yours to extend. See Connect with kubectl.
  • Multi-node performance without overhead. NCCL all-reduce inside a pod matches the same test run on the bare host.

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Create a cluster

  1. In the console, open Kubernetes and choose Create cluster.
  2. Pick the GPU type, node count, and region. Nodes in one cluster are in the same region and on the same interconnect.
  3. Wait for the cluster to show Ready. Provisioning bootstraps the control plane, joins the nodes, and installs the add-ons.
  4. Install the kubectl oidc-login plugin (one-time) and click Download kubeconfig — see Connect with kubectl for the install commands. Then point kubectl at the file:
The first command opens your browser to sign in with Hyperbolic; after that, kubectl runs as you. You should see one node per GPU instance, each advertising nvidia.com/gpu capacity.
You can still rent interconnected multi-node clusters without Kubernetes — see On-Demand. Managed Kubernetes is one way to run on those nodes, not the only way.

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Node pools

Nodes are grouped into pools. From the cluster’s Pools tab you can see each pool’s GPU type, size, and status, and add or remove nodes. CPU-only worker pools for non-GPU workloads (proxies, monitoring, controllers) are on the roadmap.

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Running GPU workloads

Request GPUs like any other resource:
For multi-node training, the Network Operator exposes the RDMA interfaces to pods; use your framework’s usual NCCL launcher. The NCCL and interconnect checks in Verifying Instance Performance apply unchanged inside Kubernetes.

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Exposing services

Nodes have public IPs. A NodePort or hostNetwork service is reachable from the internet only once the port is open on the node — see Opening inbound ports. Prefer an in-cluster ingress plus one open port over exposing many NodePorts.

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Storage

Node-local NVMe is available to pods as hostPath or a local volume and does not survive node replacement. Shared network storage for clusters is in development; until it ships, use your own object storage for checkpoints.

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Monitoring

The cluster page in the console shows the pods and events on each node and live GPU telemetry. You are free to install Prometheus, Grafana, or any observability agent in the cluster. A fuller Monitoring tab with history is in development.

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What Hyperbolic runs, and what you run

Managed Slurm is on the roadmap; Kubernetes is the supported orchestration path today.

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Billing

GPU nodes are billed exactly like the same instances rented directly. The hosted control plane is billed separately and shown on the cluster’s billing tab.