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The right component count and capacity depend on whether the system will serve, fine-tune, or train models; its performance and resilience targets; the chosen platform; and the facility. Treat the guidance below as a design checklist, not a universal bill of materials.
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Start by defining the workload
Decide what the isolated environment must do before choosing servers or switches. Inference, fine-tuning, large-scale training, and high-performance computing can have different compute, storage, and network requirements. A deployment may also combine several roles, such as centralized training followed by production inference on smaller distributed nodes.
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- Service targets: define concurrent users, latency, throughput, availability, and expected growth.
- Data movement: estimate input-pipeline throughput, checkpoint reads and writes, shared-model access, and backup needs.
- Operations: set recovery objectives, spare-parts expectations, software-update cadence, and security requirements.
NVIDIA’s Enterprise Reference Architecture overview cautions that CPU, GPU, storage, and network ratios that work for one node or workload may become bottlenecks as distributed workloads scale. Balance the entire server and cluster rather than selecting a GPU count in isolation.
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Choose compute for the job, not a headline GPU count
Build from a validated server design whose CPU, host memory, GPUs, local NVMe, network adapters, and thermal and power limits fit the workload. Inference-focused installations may favor a different balance from centralized training clusters.
How the platform profiles differ
| Platform example | Profile in NVIDIA’s reference design | What to take from it |
|---|---|---|
| NVIDIA RTX PRO servers | Described as suitable for inference-heavy work and sites constrained by power and cooling. | Consider this kind of profile when production inference and facility limits dominate; it is not a universal recommendation. |
| NVIDIA HGX B200/B300 | Described for centralized large-scale training, fine-tuning, and elastic resource pools. | Consider this kind of profile when centralized accelerator capacity is required; confirm facility and operational fit. |
| Central training plus distributed inference | The Government AI Factory reference design describes exporting trained or iterated models to distributed RTX PRO nodes for production inference. | This is one possible architecture pattern, not a requirement for every deployment. |
NVIDIA’s Government AI Factory reference design gives an example scale of 4 to 32 nodes, scaling to 256 GPUs or more. That is an example from the reference design, not a minimum cluster size or target for an air-gapped installation.
Read GPU memory figures in context
NVIDIA’s HGX H100/H200/B200 component guide describes an eight-GPU system design and says four-GPU designs can also be used. For the cited eight-GPU baseboard configurations, the guide lists up to 640 GB of GPU memory for H100, 1,128 GB for H200, and 1,440 GB for B200. These are platform specifications, not a claim that every deployment needs eight GPUs or those memory totals; confirm the exact system configuration and current vendor specifications.
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Choose the smallest configuration that meets measured capacity, performance, and reliability needs, while preserving a practical upgrade path. In an isolated site, longer replacement and update lead times can make validated spare parts and documented recovery procedures as important as peak performance. Test the exact server, firmware, drivers, accelerator runtime, orchestration stack, and model-serving software together before relying on them offline.
Plan storage by role, including local copies of offline assets
“Storage” is not one capacity number. Separate the jobs that need to be done and choose file, object, block, or local storage according to application semantics and access patterns.
- Boot and operating system: provide boot storage for each host.
- Local NVMe: use where required for model caches, container images, scratch space, or ephemeral logs.
- Shared file storage: consider it for training data and shared model assets when workloads need shared, high-throughput file access.
- Object or block storage: use where the application, backup, or data-management design calls for those interfaces.
- Transfer staging: reserve an approved device or controlled transfer appliance for release bundles entering the enclave.
NVIDIA’s architecture material notes that file and object storage suit different workloads, and that required storage bandwidth per GPU varies with workload, model, and performance goals. Benchmark the intended input pipeline, checkpoint behavior, model load times, and concurrent serving access instead of assuming a single storage design fits all.
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The HGX component guide gives local NVMe recommendations specific to its systems: capacity varies by CPU socket and by inference, training/deep-learning, or HPC use, and the guide separately specifies a boot drive. Control-plane nodes may need extra capacity if they store software images. Treat those recommendations as platform starting points, then check the selected server’s current specifications and actual cache and artifact requirements.
Stage software and models before isolation
An air gap changes the software lifecycle: the deployment must have everything it needs locally, and updates must enter through an approved, repeatable process. NVIDIA’s NIM LLM/VLM air-gap instructions apply specifically to NIM version 2.0.13; follow the instructions for the actual software version selected.
- Prepare while connected: use a connected staging machine with the required credentials to download and prepare the model assets and container images needed for the release.
- Build a complete release bundle: include model weights and configuration, container images, operating-system images, drivers, firmware, orchestration manifests, licenses, security updates, and rollback packages required by the deployment.
- Transfer under policy: use an approved method such as an archive copy,
scp,rsync, or physical media, subject to the organization’s security and chain-of-custody rules. - Verify before use: check bundle completeness and integrity, and retain hashes or signatures plus a version manifest that records compatibility among models, containers, drivers, firmware, and licenses.
- Run from local assets: place the approved bundle in local storage or the enclave’s internal artifact repository. For the isolated phase described in NVIDIA’s NIM 2.0.13 guide, model assets must load from local storage; do not set
NGC_API_KEYorHF_TOKENon the isolated machine. - Rehearse the update and recovery path: test how patches and new releases are imported, validated, deployed, and rolled back without depending on remote registries, cloud monitoring, or online credentials at runtime.
Physical media is one possible transfer channel, not automatically a secure one. If removable media is permitted, select its capacity, encryption, tamper controls, malware scanning, and custody process under local security policy.
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Separate the network paths
Disconnected from the public internet does not mean free of internal network traffic or trust boundaries. Plan distinct logical functions and restrict routes between them according to the threat model.
- GPU east-west fabric: carries high-throughput, low-latency traffic among accelerator nodes for distributed training, fine-tuning, or multi-node inference.
- Customer and storage network: connects approved users and services to data, shared storage, orchestration interfaces, and other permitted internal systems.
- Secure out-of-band management: carries BMC, provisioning, and device-management traffic on a restricted administrative path separated from workload traffic.
NVIDIA’s NCP architecture also distinguishes NVLink as an intra-rack GPU scale-up domain. In that design, tenant access and secure management use Ethernet, while the cluster interconnect can use Ethernet or InfiniBand. These are reference patterns; select protocols, cabling, switches, redundancy, and segmentation for the actual platform and threat model.
For its HGX H100/H200/B200 reference systems, NVIDIA’s component guide describes BlueField-3 adapters at up to 400 Gb/s and provides multi-node compute-network bandwidth recommendations: more than 200 GB/s minimum and 400 GB/s recommended total bandwidth for the guide’s systems. It also recommends approximately one NIC per GPU for that software stack. These are platform- and performance-specific recommendations, not universal thresholds for an air-gapped cluster or smaller inference system.
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Document which interfaces are physically disconnected, which internal routes are allowed, how administrators reach management systems, and how privileged access, identity, audit logging, and removable media are controlled. Where the threat model calls for them, assess platform integrity and confidential-computing features separately; they do not replace boundary design. NVIDIA’s Government AI Factory design mentions TPM 2.0 and secure-platform capabilities for its certified systems, but required controls depend on the organization’s own accreditation obligations.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Provide control-plane and operational services
GPU hosts do not replace the services needed to run a cluster. Reserve non-GPU capacity for provisioning, scheduling, cluster services, local registries or artifact repositories, identity integration, telemetry, and management. NVIDIA’s HGX guide gives an example using Base Command Manager, Slurm, and Kubernetes with separate head/control nodes, and recommends high availability for control nodes where needed. That is an example stack, not a mandatory product choice or fixed node count.
Keep operations local to the enclave. Monitor GPU and host health, storage performance, network errors, temperatures, power draw, and workload queues without depending on an external cloud endpoint. Maintain backups and a tested recovery process, and rehearse software import and rollback on representative hardware.
Engineer power and cooling from the selected system
Facilities sizing must follow the exact server and rack configuration, not a headline GPU wattage. Work with the equipment vendor and site engineers to establish the expected and observed load, GPU power mode, transient behavior, redundant-feed assumptions, rack distribution, upstream capacity, and expansion margin. Set UPS ride-through or runtime requirements and generator or alternate-supply plans according to the site’s resilience objectives.
Cooling design likewise depends on actual heat output, rack density, inlet conditions, redundancy, and serviceability, as well as whether the room uses air or liquid cooling. NVIDIA’s reference architecture overview identifies space, power, and cooling as constraints that distinguish system families; its DSX documentation includes facilities, power-management, cooling, and battery-energy-storage design areas. Those sources support including facilities engineering in scope, but do not establish a generic air-gap-cluster wattage, UPS size, battery runtime, or cooling tonnage. Obtain those values for the chosen configuration and site.
Use a design review to close the gaps
Before procurement or isolation, compare the proposed architecture against these decision axes and require a documented answer for each:
Quick Recap
- Workload: inference, fine-tuning, training, HPC, or mixed use, with measurable capacity and performance targets.
- Scale: single server or multi-node cluster, GPU interconnect topology, and validated network bandwidth.
- Data path: local cache capacity, shared storage behavior, checkpoint performance, backup, and model distribution.
- Security and operations: physical disconnection, internal segmentation, administrative access, auditability, offline update process, and accreditation fit.
- Facilities: power availability, air or liquid cooling, rack footprint, resilience, serviceability, and expansion.
- Lifecycle: support arrangements, spares, repair turnaround, component compatibility, and ability to reproduce a tested software release.
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