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For a fully disconnected environment, prepare and transfer the container images and model assets before isolation. Then test the running service with outbound traffic blocked. NVIDIA’s air-gap guide describes this workflow for NIM version 2.0.13; other runtimes and versions may have different requirements.
What “private infrastructure” does—and does not—mean
In a self-hosted setup, your application sends prompts to an inference service running on a workstation, server, or private cluster rather than to a public model API. That changes where inference runs; it does not automatically determine where prompts are logged, whether the endpoint is exposed, or whether the service contacts external systems.
There are two useful deployment boundaries:
- Private but network-connected: the service runs on infrastructure you administer and may be reachable by approved internal clients. Network policies must restrict both who can connect and what the host can contact.
- Air-gapped: the serving environment has no internet connection. Required images, model files, and dependencies must be brought into it through an approved transfer process before serving.
NVIDIA describes the goal of its air-gap process this way: “Air-gap deployment lets you run a NIM without an internet connection, for example, with no connection to remote model registries such as NGC or Hugging Face Hub.” This wording is from NVIDIA’s Air-Gap Deployment — NVIDIA NIM for LLM and VLM 2.0.13.
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Choose a deployment shape that fits your environment
| Deployment | When it may fit | Operational considerations |
|---|---|---|
| GPU workstation or single host | Development or a smaller deployment, provided the selected model fits the host and meets workload needs. | NVIDIA documents NIM deployments on RTX AI PCs and workstations as well as in data centers and cloud environments. The cited guidance does not establish a universal GPU or workstation configuration. |
| Private server or data center | Inference hosted on infrastructure administered by an organization. | Plan for host and network controls, model and image lifecycle, and the workload’s memory, concurrency, and latency requirements. The sources do not provide a general sizing formula. |
| Kubernetes cluster | A deployment managed as cluster workloads and services. | vLLM’s Kubernetes guide uses a Deployment and Service, with optional persistent storage for the model cache. An isolated cluster also needs serving images available from a private registry and model assets available locally. |
These are different operating models, not a performance ranking. NVIDIA lists NIM engines including TensorRT, TensorRT-LLM, vLLM, and SGLang, but the cited sources do not provide a head-to-head benchmark. Compare runtimes against the model formats, hardware, API behavior, and operational requirements you actually need.
Prepare the model and runtime before disconnecting
An isolated host cannot fetch a missing model or container image from an online registry at startup. Treat air-gapping as an asset-staging and transfer workflow, not as a setting that can be enabled after deployment.
- Define the serving target. Select the model, runtime, and hardware environment. Check the model publisher’s license and access conditions separately; the deployment guidance does not establish rights for a particular model.
- Prepare assets in a connected environment. For NVIDIA NIM version 2.0.13, the documented preparation phase includes container tooling, any required source credentials, a model-specific or model-free image, and downloaded, cached model assets. A model store can also be created. Stage all required model weights, tokenizer and configuration files, images, and dependencies for the selected deployment.
- Transfer through an approved channel. NVIDIA’s guide describes transferring a cache or model store by an allowed archive-copy, SSH transfer, synchronization, or physical-media method. Choose the method permitted by your security process, and ensure the isolated environment can access the transferred assets.
- Make assets available to the serving environment. For NIM, mount the staged local model or cache when launching the container. A model-free image can use
NIM_MODEL_PATHto point to a local model directory. In Kubernetes, make the image available through a private registry and provide model files through local storage; vLLM’s guide describes persistent storage for its model cache as optional. - Start without online credentials. NVIDIA’s documented isolated-serving workflow launches with the staged assets and without
NGC_API_KEYorHF_TOKEN. If the model requires gated access during asset acquisition, vLLM’s Kubernetes guide describes using a token secret for that access; do not assume the isolated serving environment can retrieve gated assets itself.
NVIDIA’s NIM steps above are specific to version 2.0.13. Check the documentation for the version you intend to deploy before applying them; image behavior and deployment requirements can vary by version.
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Restrict access to the service and its supporting components
A privately hosted API can still expose prompts if clients send requests to the wrong endpoint or if untrusted parties can reach the service. vLLM’s security guidance warns that dependent components may listen on network interfaces and that distributed communication can be insecure by default.
- Allow inbound traffic only from intended clients and expose only the required serving interface.
- Restrict distributed-communication and cache-transfer ports to trusted hosts or networks rather than making them broadly reachable.
- Do not treat an API key as complete protection: vLLM cautions that its API-key authentication does not cover every sensitive endpoint. Use network controls as well.
- For regulated or cryptographic requirements, do not equate network isolation with encrypted transport. vLLM states that inter-node channels are unencrypted by default and that isolation alone does not satisfy a requirement for FIPS-approved cryptography in transit; additional controls may be necessary.
Map the full data path before calling a deployment private: prompts, retrieved documents, logs, caches, and model files may each be handled or retained by different components. Set controls and retention expectations for those paths, not just for the inference endpoint.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Verify that serving works without outbound access
Validate the no-egress claim on the deployed workload, not only in a design document. NVIDIA’s NIM air-gap guidance describes applying default-deny egress, allowing only necessary internal services where needed, restarting the workload, and repeating readiness, model-list, and inference checks.
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- Apply default-deny outbound network rules at the appropriate host or network layer. If the deployment needs internal services such as DNS or an in-cluster registry, allow only those required paths.
- Restart the workload with the staged image and model assets after the egress policy is in place.
- Check that the service becomes ready, that its model-list endpoint reports the expected model, and that an inference request succeeds.
- Review logs and infrastructure telemetry for unexpected outbound destinations or failed attempts to reach external registries or model sources.
If readiness or inference fails, check that the image, model files, configuration, and dependencies were transferred and mounted correctly. If the service works only after outbound access is restored, identify the missing dependency or required internal path; do not weaken the egress policy broadly as a substitute for understanding the dependency.
Plan for updates and workload sizing
Isolation does not remove the need to patch the host, runtime, container images, dependencies, and model artifacts. Define how updates will be obtained, verified, transferred, deployed, and rolled back in the restricted environment. Keep the deployed versions identifiable so an update can be traced to its staged assets.
Size hardware against the chosen model and workload: account for model and runtime memory, expected concurrency, and latency requirements. The deployment sources establish GPU workstations and data-center environments as possible forms of self-hosting, but do not support choosing a particular GPU, workstation, or server without those requirements.
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