Secure air-gapped AI training depends on two controls working together: a complete, verified offline software supply chain and a network that denies every connection except explicitly required internal paths. Prepare and scan models, images, charts, drivers, packages, certificates, and manifests in a connected staging zone; transfer them through an approved channel; then run the isolated cluster with default-deny egress. On the hardware side, pin a tested GPU software bundle and derive NCCL, MTU, RDMA, storage, and placement settings from the actual topology rather than copying values from another cluster.
This guide gives a practical baseline for Kubernetes-based, multi-GPU training enclaves, explains which settings are universal security requirements and which are profile-specific, and provides an offline validation and failure-testing sequence.
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Start with a two-phase architecture
An air-gapped cluster should not be treated as an ordinary cluster with the internet unplugged. Use two deliberately separated phases.
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Use a controlled staging host or enclave with temporary, monitored connectivity to obtain every artifact the isolated environment will need. The bill of materials should include:
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- Model weights, tokenizers, configuration files, and auxiliary data.
- Container images, including init containers, sidecars, Helm test images, GPU Operator images, and platform components.
- Helm charts, Kubernetes manifests, admission policies, and configuration templates.
- NVIDIA driver packages or supported precompiled driver containers, CUDA and container-runtime components, and training-framework dependencies.
- Internal certificate-authority certificates, repository metadata, and operating-system packages.
Record the version, source, cryptographic hash, signature or provenance information, and intended destination for each item. Scan and sign artifacts according to your organization’s policy before they leave the staging zone. A transfer that contains only the main training image is incomplete: an unmirrored init container, chart hook, driver package, or CA certificate can stop an otherwise correct deployment.
Isolated execution phase
Import the approved artifacts into internal storage and private registries, then run workloads without external name resolution, object storage, model hubs, telemetry endpoints, or package repositories. NVIDIA’s NIM guidance is explicit: “In the air-gapped phase, do not set NGC_API_KEY or HF_TOKEN.” Remove those variables from manifests, Secrets, shell profiles, and CI templates used inside the enclave. Mount the locally imported model and image assets instead.
Keep the phases operationally distinct. An administrator should be able to show which files crossed the boundary, who authorized the transfer, and that the digest observed after import matches the digest approved before transfer.
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Pod-level policy cannot compensate for a flat physical or virtual network. Separate at least these traffic domains with VLANs, VRFs, firewall policy, or equivalent controls:
- Management: bastions, identity services, logging, monitoring, and administrative interfaces.
- Kubernetes control plane: API servers, etcd, and control-plane node communication.
- Storage: model stores, persistent-volume traffic, and backup or replication paths.
- East-west training fabric: node-to-node traffic for collectives, RDMA, and distributed frameworks.
- Transfer or staging: the tightly controlled hand-off area for imported artifacts.
NIST SP 800-125B, published in 2016, identifies segmentation, firewall deployment architecture, path redundancy, and traffic monitoring as core secure virtual-network controls. Apply those controls outside Kubernetes as well as inside it. Route administration through a management path, not through the high-bandwidth training fabric, and make every inter-zone flow explicit.
Default-deny workload egress
Apply a namespace-level default-deny egress policy before starting a training or inference workload. Then add the smallest exceptions required for internal DNS and the private registry or other approved internal services. The exact registry port depends on its deployment; 443 is only an example when the registry is configured for TLS on that port.
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apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
name: deny-egress-by-default
namespace: training
spec:
podSelector: {}
policyTypes:
- Egress
egress: []
A second policy can allow DNS to the cluster’s approved resolver and a registry endpoint. Replace the namespace, selector, address range, and port with values from your enclave design; do not copy them unchanged.
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
name: allow-internal-services
namespace: training
spec:
podSelector: {}
policyTypes:
- Egress
egress:
- to:
- namespaceSelector:
matchLabels:
kubernetes.io/metadata.name: kube-system
ports:
- protocol: UDP
port: 53
- protocol: TCP
port: 53
- to:
- namespaceSelector:
matchLabels:
enclave-service: registry
ports:
- protocol: TCP
port: 443Some CNIs cannot express every desired destination with a namespace selector, and a registry may be outside the cluster. In those cases, use an approved IP block or service identity and enforce the same restriction at the host firewall and perimeter firewall. Never solve a certificate problem by disabling TLS verification; distribute the internal CA through controlled configuration.
Node-to-node and control-plane reachability
Kubernetes and distributed-training stacks require interconnectivity among selected nodes, but the exact ports vary by Kubernetes distribution, CNI, storage system, and framework. Build an allowlist from the versions you actually deploy. Permit only the control-plane, kubelet, overlay, storage, and training-fabric paths documented for that bundle. Capture the rules in change control and monitor denied connections so an unexpected dependency is visible instead of silently opened.
Logging and egress evidence
Send firewall and NetworkPolicy denials to an internal collector. Review denied egress during smoke tests, then retain the resulting allowlist as evidence. Also log registry pulls and image digests, model-file hashes, administrative access, certificate changes, and every transfer across the staging boundary. Time synchronization must be available internally so those records can be correlated.
Pin the GPU software and Kubernetes bundle
GPU reliability in an offline cluster is a compatibility problem before it is a performance problem. Select and freeze a tested combination of NVIDIA driver, CUDA/runtime libraries, container runtime, GPU Operator, Kubernetes version, device-plugin configuration, and training framework. Validate that exact bundle in a staging enclave, export its manifests and image digests, and do not let an isolated node resolve a newer package accidentally.
Mirror drivers and operating-system packages
NVIDIA GPU Operator documentation notes that a local image registry and/or local package repository may be necessary so required images and packages are available to the cluster. Mirror driver packages in an internal operating-system repository when the chosen installation path needs them. Configure the operator’s repository and certificate settings, including driver.repoConfig and driver.certConfig where applicable. The referenced ConfigMaps must contain the internal repository definition and CA chain, not a public endpoint.
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Where supported, precompiled driver containers can remove package downloads during installation. They still must be mirrored, signature-checked, and matched to the kernel and GPU architecture in the target nodes. Keep a rollback version of the driver and operator bundle available; an upgrade that cannot be reversed offline is an operational risk.
Verify readiness before scheduling jobs
- Confirm every node reports the intended GPU count, model, memory capacity, and driver version.
- Check that the device plugin exposes the expected resources and that a test pod can access a GPU.
- Run a CUDA-level smoke test and verify that the container runtime exposes the device without privileged workarounds beyond the approved design.
- Inspect operator, driver, device-plugin, and container-runtime logs for attempts to reach external repositories.
- Confirm internal CA trust by pulling an image and reading a model from each approved internal endpoint.
Record topology before tuning performance
There is no universal MTU, bandwidth target, NCCL setting, or single GPU model that is correct for every air-gapped training cluster. Treat these values as outputs of an inventory and validation exercise.
GPU and CPU inventory
Record the GPU model and count per node, memory capacity, CPU memory, PCIe generation and lane allocation, NVLink or other intra-node interconnects, NUMA placement, and the failure domain of each node. Expose this information to the scheduler with labels, extended resources, or topology-aware scheduling rules. A job that spans GPUs across an unexpected PCIe root complex can lose more time to transfers than it gains from additional devices.
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East-west fabric and RDMA
For multi-node training, document the switch domains, link speeds, oversubscription, routing boundaries, and redundancy of the east-west fabric. Verify RDMA capability end to end: NIC firmware, driver, verbs libraries, Kubernetes device exposure, and switch configuration must agree. If GPUDirect RDMA is planned, test that GPU memory can be reached through the supported path rather than assuming that an RDMA-capable NIC provides it automatically.
Only after those checks should you tune NCCL environment variables, collective algorithms, interface selection, MTU, queue settings, or process placement. Derive the values from measured topology and the selected workload. Keep a known-good configuration and record the reason for each deviation from defaults.
Storage and cache paths
Size persistent local or internally shared storage for model stores, container layers, package mirrors, logs, and temporary checkpoints. An offline cache cannot be rebuilt from a public service after a node is reinstalled. Test read throughput and metadata behavior with the same number of workers expected in training, and verify that storage traffic stays on the storage network rather than competing with collectives.
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Account for the whole AI data-center threat surface
Pod networking is only one layer of the security review. NIST SP 800-239, whose initial public draft was published July 27, 2026, analyzes hardware, software, workflows, and storage threats in AI data centers using an HPC-driven approach. Apply that wider lens to the enclave:
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- Firmware and hardware: track BIOS, GPU, NIC, switch, and storage-controller firmware versions; restrict update media to signed, approved packages.
- Software provenance: retain image digests, model hashes, package manifests, signatures, and the identity of the approver for each transfer.
- Workflow controls: review who can import data, change a NetworkPolicy, launch a privileged pod, rotate a certificate, or export a checkpoint.
- Storage protection: encrypt or otherwise protect model and checkpoint stores according to the data classification, and test deletion, restore, and access revocation offline.
Use a repeatable offline validation sequence
- Generate the bill of materials. On the connected staging host, enumerate images, charts, model files, drivers, operating-system packages, certificates, and manifests. Record versions, hashes, signatures, and provenance.
- Scan, sign, and transfer. Apply organizational malware, vulnerability, and policy scans; sign or attest artifacts; then move them through an approved controlled channel with an auditable manifest.
- Import and compare digests. Load images into the private registry and verify that every imported digest matches the approved manifest. Check init containers, sidecars, Helm hooks and tests, GPU Operator images, and platform images individually.
- Install or verify GPU Operator. Confirm driver readiness and check that repository and CA ConfigMaps resolve only to internal services. Review logs for failed external lookups.
- Apply egress denial first. Before launching the workload, apply default-deny egress and then the internal DNS and registry exceptions. Confirm that an unapproved destination is logged and blocked.
- Run local smoke tests. Exercise health endpoints, model listing, inference, and a short training job over loopback or internal service addresses. Restart the workload and repeat to prove that startup does not depend on outbound access.
- Exercise failure and recovery. Disconnect a node, make the registry temporarily unavailable, remount storage, and rotate an internal certificate inside the enclave. Record whether jobs fail safely, whether retries stay internal, and which rollback artifacts restore service.
Compare designs with evidence, not headline specifications
| Decision axis | Questions to answer | Evidence to retain |
|---|---|---|
| Isolation strength | Are management, control-plane, storage, training, and transfer zones separated physically or by enforceable segmentation? Is egress default-deny? | Firewall and NetworkPolicy rules, denied-flow logs, topology diagrams, and access reviews. |
| Offline supply-chain completeness | Are every image, model, chart, driver, package, certificate, and manifest available locally? | Signed bill of materials, hashes, registry digest reports, and transfer records. |
| GPU and fabric performance | Do GPU memory, PCIe/NVLink topology, NUMA placement, RDMA, GPUDirect RDMA, and storage paths fit the workload? | Hardware inventory, topology captures, smoke-test results, and measured collective behavior. |
| Operability | Can operators upgrade, rotate certificates, replace a node, and roll back without external access? | Runbooks, offline packages, recovery logs, and results from failure drills. |
| Evidence quality | Can an auditor reproduce what ran and which connections were allowed? | Immutable logs, image and model hashes, configuration snapshots, and approval history. |
Common failure modes and corrective actions
A pod is running but repeatedly tries the internet
Inspect image entrypoints, model-loading code, telemetry libraries, package managers, and DNS logs. Keep egress blocked, identify the dependency from denied-flow records, and replace it with a mirrored asset or an internal endpoint. Do not open broad internet access as a troubleshooting shortcut.
GPU Operator waits for packages
Verify that the driver repository ConfigMap points to the internal OS mirror, that the CA ConfigMap contains the complete chain, and that the required package versions were transferred. If the supported precompiled-driver path matches the node kernel and GPU, use its mirrored container instead of creating an ad hoc package source.
Distributed training is slow or hangs
Check that the scheduler placed processes according to NUMA and GPU topology, that all intended interfaces are selected, and that RDMA and GPUDirect RDMA work on every node. Confirm switch-domain reachability and MTU consistency across the complete path. Change one NCCL or fabric setting at a time and retain the last known-good profile.
Restart fails after a node or storage outage
Check that model layers, container layers, checkpoints, and package mirrors reside on persistent storage available inside the enclave. Test remount and registry-recovery procedures before production; an offline cluster cannot fetch a missing layer during an incident.
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FAQ
Should an air-gapped cluster have any outbound route at all?
For workload namespaces, the safe baseline is no outbound route. Permit only named internal services such as approved DNS and a private registry, and enforce the decision at both Kubernetes policy and network firewalls. Administrative transfer paths belong in a separate controlled zone.
What exact ports should be opened between Kubernetes nodes?
There is no single port list that applies to every Kubernetes distribution, CNI, storage stack, and training framework. Build the allowlist from the versions in your pinned bundle, document each path, and monitor denied traffic while running the validation sequence.
Can MTU or NCCL values be copied from another GPU cluster?
Only as a temporary hypothesis. PCIe and NVLink layout, NICs, switch domains, routing, RDMA support, and workload message sizes differ. Measure the selected topology and preserve a tested profile instead of assuming a universal value.
How do I prove that a workload is truly offline?
Apply default-deny egress, run health, model-listing, inference, and training smoke tests using internal addresses, restart the workload, and inspect denied-flow, DNS, registry, and application logs. Then repeat after registry, storage, certificate, and node-failure drills.
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Frequently Asked Questions
Should an air-gapped cluster have any outbound route at all?
For workload namespaces, use no outbound route as the baseline. Permit only named internal services such as approved DNS and a private registry, enforced by Kubernetes policy and network firewalls.
What exact ports should be opened between Kubernetes nodes?
There is no universal list. Derive the allowlist from the pinned Kubernetes, CNI, storage, and training-framework versions, then monitor denied traffic during validation.
Can MTU or NCCL values be copied from another GPU cluster?
Use another cluster’s values only as a hypothesis. Measure the actual PCIe, NVLink, NIC, switch, RDMA, and routing topology before freezing settings.
How do I prove a workload is truly offline?
Run smoke tests after applying default-deny egress, restart the workload, inspect logs, and complete registry, storage, certificate, and node-failure drills without external access.
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