Securely connecting an on-prem GPU cluster to the cloud typically relies on a dedicated private network link, such as AWS Direct Connect, Azure ExpressRoute, or Google Cloud Interconnect, rather than routing sensitive AI traffic over the public internet even with encryption. These services establish a private, high-bandwidth connection between an on-premise data center and the cloud provider's network, which reduces latency variability and avoids exposure to public internet routing, and they can be paired with a VPN as a backup path for redundancy. On top of the network link, workloads should authenticate using short-lived credentials or a federated identity provider rather than long-lived static keys, and traffic between on-prem and cloud components should still be encrypted in transit even over a private circuit as a defense in depth measure. For hybrid Kubernetes GPU clusters, this connectivity also needs to support the specific ports and protocols used by the scheduler and container registry, plus sufficient bandwidth for model artifact transfer, which can be substantial for large language models. Provisioning a dedicated circuit typically takes weeks and involves both the cloud provider and a network carrier, so it should be planned well ahead of a hybrid deployment's target date. Nanobase AI configures Direct Connect, ExpressRoute, or equivalent private connectivity as part of hybrid GPU infrastructure builds.
Start with the network primitive, not the VPN
Routing sensitive AI traffic between an on-prem GPU cluster and the cloud over the public internet, even with TLS encryption, is not the recommended default once model artifacts, training data, or inference requests carry business-sensitive content. Public internet paths introduce variable latency and route through infrastructure outside either party's direct control, which matters for both performance consistency and audit posture. A private interconnect paired with a VPN as a failover path, not a VPN alone, is the baseline architecture for connecting an on-prem GPU cluster to the cloud securely.
This is a network design decision made early, since retrofitting a private circuit after a hybrid deployment is already live means a migration window for every workload depending on the connection.
The three private interconnect options
| Service | Provider | Redundancy pattern | Typical hybrid GPU use |
|---|---|---|---|
| Direct Connect | AWS | Dual connections across separate locations | Model artifact sync to S3, EKS control plane traffic |
| ExpressRoute | Azure | Active-active circuits, optional Global Reach | AKS control plane, Blob Storage transfer |
| Cloud Interconnect | Google Cloud | Dedicated or Partner Interconnect | GKE traffic, Cloud Storage sync |
Each service establishes a dedicated, private link into the provider's network rather than traversing the public internet, and all three support pairing with a VPN as backup so a circuit fault does not fully sever connectivity. None of these guarantee security on their own; they reduce exposure and improve latency predictability, but the identity and encryption layer still has to be built on top.
Identity and traffic controls beyond the network layer
A private circuit does not replace strong authentication. Workloads authenticating across the hybrid boundary should use short-lived credentials issued through a federated identity provider rather than long-lived static keys stored on either side, since a leaked static key on a private circuit is still a leaked key. Traffic between on-prem and cloud components should also stay encrypted in transit even over the dedicated link, treated as defense in depth rather than a redundant step, because it protects against misconfiguration or a compromised network segment rather than assuming physical isolation alone is sufficient.
The private circuit protects the network path; federated identity and encryption in transit protect against everything that can go wrong inside that path. Container registry access, in particular, needs its own short-lived token flow so that pulling model images from on-prem to cloud or back does not rely on embedded credentials.
Setting up the link for a hybrid GPU cluster
- Order the private circuit, sized for both control-plane traffic and the largest expected model artifact transfer, not just steady-state inference traffic.
- Configure a VPN as a backup failover path so a circuit degradation does not fully isolate the on-prem cluster.
- Set up federated identity between the on-prem identity provider and the cloud IAM system, replacing static keys with short-lived tokens.
- Enforce encryption in transit for all traffic crossing the boundary, independent of the circuit's own privacy guarantees.
- Open only the specific ports and protocols the Kubernetes scheduler, container registry, and GPU cluster components require, instead of a broad allow rule.
- Test failover behavior and measure sustained throughput for large checkpoint transfers before cutting production traffic over.
Provisioning a dedicated circuit typically takes several weeks and involves both the cloud provider and a network carrier, so this work should start well ahead of a hybrid deployment's target date.
Frequently asked questions
How long does it take to provision Direct Connect or ExpressRoute?
Provisioning typically takes several weeks and involves coordination between the cloud provider, a network carrier, and sometimes a colocation facility, so it should be scheduled well ahead of a hybrid deployment's go-live date rather than treated as a quick configuration step.
Is a VPN ever good enough on its own?
A VPN alone can work for lower-throughput or non-latency-sensitive connections, but production GPU workloads transferring large model artifacts or serving latency-sensitive inference generally need a dedicated private circuit with a VPN kept only as backup, since a VPN's shared internet path introduces variable performance under load.
Do we still need encryption in transit over a private circuit?
Yes, encrypting traffic in transit even over a dedicated, private circuit is standard defense in depth, protecting against misconfiguration or a compromised network segment rather than assuming physical isolation alone is sufficient for a production GPU workload handling sensitive data.
What bandwidth should we plan for hybrid GPU connectivity?
Bandwidth needs depend on how often large model checkpoints or datasets move between environments; a deployment that only sends inference requests and responses needs far less than one regularly syncing multi-gigabyte model files, so sizing should follow actual transfer patterns rather than a generic estimate.
How Nanobase AI helps
Nanobase AI configures Direct Connect, ExpressRoute, or Google Cloud Interconnect as part of hybrid GPU infrastructure builds, pairing the private circuit with federated identity and encryption controls so the connection meets enterprise security requirements rather than just throughput targets. This work is often paired with Kubernetes GPU cluster design spanning both environments and with running GPU workloads across on-prem and cloud together.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.