To keep prompts off public model APIs, run inference on a model-serving system inside infrastructure you control, stage the model and software there, and verify that the service works while outbound network access is blocked. For a true air gap, prepare and transfer every required asset before isolation. For a privately hosted API that remains network-reachable, add strict access controls: private hosting alone does not prevent unauthorized clients or exposed components from seeing data.
What “private LLM hosting” does—and does not—mean
When you host inference yourself, prompts are sent to your own serving endpoint rather than to a public model API. That changes the data path, but it does not automatically make the deployment private or secure. Clients can still send prompts to the wrong endpoint, exposed services can admit untrusted traffic, and prompts or retrieved documents may persist in logs, caches, or other systems.
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There are two useful deployment boundaries:
- Air-gapped: the serving environment has no network connection to the internet. Software, container images, models, and dependencies must be brought in through a controlled staging and transfer process.
- Privately networked: the service is reachable over an internal network, but outbound access is restricted. This can support internal dependencies, though it is not equivalent to an air gap.
NVIDIA describes air-gap deployment as running a NIM “without an internet connection,” including without connections to remote model registries such as NGC or Hugging Face Hub, in its Air-Gap Deployment — NVIDIA NIM for LLM and VLM 2.0.13 guide.
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Choose an environment that fits the workload
The deployment environment determines operational complexity, not guaranteed model speed. NVIDIA documents NIM inference containers for GPU PCs and workstations as well as data centers and cloud. vLLM documents a Kubernetes serving pattern using a Deployment and Service. Neither source establishes a universal hardware configuration or a head-to-head performance result.
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| Environment | What it suits | What to plan for |
|---|---|---|
| GPU workstation or PC | Development or a smaller deployment, if the selected model and workload fit the host. | Confirm memory, concurrency, and latency needs against the specific model and runtime. The cited NVIDIA material does not specify a one-size-fits-all workstation or GPU. |
| Private server or data center | Inference hosted on infrastructure operated by your organization. | Plan hardware, network boundaries, administration, and the model and runtime lifecycle for your workload; the cited sources do not provide universal sizing guidance. |
| Kubernetes cluster | A cluster-based serving deployment using vLLM’s documented Deployment and Service approach. | Provide the serving image through an appropriate private registry path and arrange local model access. vLLM describes persistent storage for the model cache as optional; other storage options can be used. |
For Kubernetes, vLLM’s deployment guide includes GPU-enabled examples and notes that access to gated models may require a token secret while obtaining assets. In a restricted environment, arrange that access during preparation rather than assuming the isolated cluster can retrieve the model later.
Stage models and software before isolating the system
An air-gapped server cannot fetch a model when the serving container starts. Treat deployment as a connected preparation phase followed by transfer and isolated operation. NVIDIA’s documented workflow is for NIM version 2.0.13; check the instructions for the version you actually deploy, since steps may differ across versions.
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- Prepare in a connected environment. Install the required container tooling and obtain any source credentials needed to access the chosen model. Select the appropriate model-specific or model-free NIM image, then download and cache the model assets. If useful for your deployment, create a model store.
- Include the full set of dependencies. Stage the container image, model weights, tokenizer and configuration files, and other required dependencies. Account for the destination’s architecture and runtime rather than assuming a partial cache will be sufficient.
- Transfer through an approved channel. Move the cache or model store into the isolated environment using an authorized method. NVIDIA’s guide describes archive-and-copy, SSH transfer, synchronization, or physical media as possible transfer methods, subject to the environment’s rules.
- Serve from local assets. Mount the staged model or cache when launching the container. NVIDIA’s guide says to omit
NGC_API_KEYandHF_TOKENfor isolated serving. With a model-free image, setNIM_MODEL_PATHto the local model directory. In Kubernetes, make the image available through a private registry and provide local persistent storage or another suitable storage option. - Test with outbound access blocked. Apply default-deny egress, allowing only necessary internal services if the design requires them. Restart the workload, then check readiness, list the served models, and submit an inference request. A successful test under those conditions is stronger evidence of offline operation than a deployment that has merely been configured not to use a public API.
If the isolated cluster still needs internal DNS, a registry, or another in-cluster service, allow only the specific paths required. Document those exceptions: a deployment with narrowly permitted internal traffic is restricted, but it is not literally disconnected from every network.
Secure the endpoint and internal traffic
A locally hosted model can still be exposed through its API or supporting components. The vLLM security guidance warns that dependent components may listen on network interfaces and that distributed communication can be insecure by default. Restrict inbound connections to the serving interface and its intended clients, and isolate distributed and cache-transfer ports to trusted hosts or networks.
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- Use host firewalls or network policies to limit inbound access to the API server port and authorized clients.
- Restrict egress to required internal destinations rather than leaving general outbound access enabled.
- Keep distributed-communication and cache-transfer ports inaccessible to untrusted networks.
- Do not treat an API key as the only protection. vLLM cautions that API-key authentication does not cover every sensitive endpoint.
- For regulated or cryptographic requirements, distinguish network isolation from encryption. vLLM states that inter-node channels are unencrypted by default; isolation alone does not meet a requirement for FIPS-approved cryptography in transit. Use additional controls where required.
Check the entire data and asset lifecycle
Before production use, map where data and deployment assets go—not just where the model runs. Review these items:
- Data paths: identify where prompts, retrieved documents, responses, logs, and caches travel and persist.
- Network behavior: inspect logs and infrastructure telemetry during tests with egress blocked; investigate unexpected destinations rather than assuming the intended network policy is working.
- Updates and recovery: define how to stage, verify, transfer, update, and roll back images, model versions, runtimes, host software, and dependencies in restricted networks.
- Model rights: review the chosen model publisher’s license and access conditions separately. Deployment documentation does not establish whether a particular model may be used for your intended purpose.
Compare serving options against your constraints
Choose a runtime and deployment pattern by checking the actual model, hardware, API needs, and operating environment. NVIDIA lists NIM engines including TensorRT, TensorRT-LLM, vLLM, and SGLang; the cited materials do not provide a direct benchmark comparison among them.
- Model and license: confirm that the desired model is available for your intended use and deployment method.
- Hardware and workload: account for model weights and runtime memory, target concurrency, and latency. The cited deployment sources do not give a universal sizing rule.
- Operations: assess whether a workstation, private server, or Kubernetes cluster best fits your administration and scaling requirements.
- Isolation: determine whether you can block outbound paths, control access to the endpoint, and restrict internal service traffic.
- Asset lifecycle: decide how model files, container images, and dependencies will be staged, verified, transferred, updated, and rolled back.
Do not choose a particular workstation or server from the deployment pattern alone. Model size, memory, concurrency, and latency requirements are needed to make a defensible hardware choice.
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