Air-gapped GPUs for government cloud

For a government sovereign AI cloud, I’d shortlist vendors that can deliver GPU infrastructure operating inside a physically isolated environment, rather than ordinary GPU cloud providers that merely offer data residency or private networking.

There are three distinct supplier categories: GPU/server manufacturers, turnkey sovereign-cloud integrators, and software platforms that run on air-gapped GPU hardware. The right combination depends on whether the government client requires classified workloads, complete internet disconnection, or simply sovereign control over data and administration.cloud.google.com+2

Vendors to shortlist

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1. Hewlett Packard Enterprise (HPE)

(Top turnkey shortlist)

Offers HPE Sovereign AI Factory and HPE Private Cloud AI, with NVIDIA GPU infrastructure, air-gapped management options, and integrated operations. Strong candidate for a government-operated private AI cloud.www.hpe.com+2

Website:HPE Sovereign AI Factory

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2. Google Cloud — Distributed Cloud air-gapped

(Verified disconnected platform)

A full hardware-and-software platform designed to operate without Google Cloud or public internet connectivity. Official documentation lists NVIDIA GPU configurations, including H100, H200 and B300 machine types. Particularly relevant when you want a managed cloud experience in a disconnected environment.cloud.google.com+2

Website:Google Distributed Cloud air-gapped

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3. NVIDIA

Supplies the GPU compute platform and reference architectures, rather than being the sole turnkey infrastructure integrator. DGX systems support documented offline installation and software maintenance workflows. For government deployments, specify the exact GPU platform and require an approved disconnected operating procedure.docs.nvidia.com+2

Website:NVIDIA DGX documentation

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4. Supermicro

A hardware supplier to evaluate for high-density NVIDIA GPU servers and rack-scale AI infrastructure. It is relevant when you want to own and operate the hardware yourself. Confirm the complete system's air-gap deployment, firmware, management interfaces and support arrangements with the vendor; a GPU server alone does not establish air-gap compliance.

Website:Supermicro

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5. Dell Technologies

Worth including in an infrastructure RFP for enterprise AI servers, GPU compute and data-center integration. Treat it as a hardware/integration candidate and request a validated air-gapped reference design for the exact government security level.

Website:Dell AI infrastructure

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6. Armada

Relevant as an orchestration and deployment partner for sovereign GPU environments. Its Bridge platform documents support for air-gapped deployments and infrastructure spanning GPUs, networking and storage. Consider it where you need a cloud-like service layer over customer-controlled hardware.docs.armada.ai

Website:Armada sovereign cloud

How I would structure the supplier shortlist

These are candidates, not a claim that every vendor or configuration is certified for every classification level.

Questions to put in the RFP

Before selecting a provider, require written answers on:

• Isolation: Is the system physically disconnected, or merely logically isolated? Can management, telemetry, licensing and support function without outbound connectivity?
• GPU availability: Which exact GPU generation, memory capacity, interconnect, rack configuration and delivery lead time are contractually available?
• Sovereignty: Who owns the hardware, encryption keys, logs, model weights and administrative access? Which jurisdiction governs support personnel?
• Lifecycle: How are signed firmware, drivers, containers, security patches and model updates imported into the enclave?
• Government authorization: What evidence supports the required accreditation, supply-chain controls, hardening baseline and operating procedures?
• Scale: Can the design grow from a pilot cluster to hundreds or thousands of GPUs without changing its security boundary?

One important distinction: air-gapped hardware is not the same as an accredited air-gapped AI cloud. The full solution also needs isolated control planes, offline software distribution, secure administration, monitoring, and an auditable update process. NVIDIA's own DGX guidance documents the special procedures required for disconnected installations.cloud.google.com+3

One question that would narrow this considerably

What government environment are you targeting?

1. Government jurisdiction

US federal / DoD

US intelligence community

European government

Gulf / Middle East government

Other national government

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2. Isolation requirement

Fully disconnected / classified

Sovereign private cloud, restricted internet

Both disconnected and connected enclaves

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3. Approximate scale

Small cluster (1–32 GPUs)

Medium cluster (32–100 GPUs)

Large cluster (100+ GPUs)

National AI factory (1,000+ GPUs)

[Narrow the vendor shortlist]

When building a sovereign AI cloud for a government agency in an air-gapped environment , the physical silicon (the GPUs themselves) does not inherently restrict air-gapping—any enterprise/data center GPU can operate off-grid. However, the software ecosystems, firmware management, and management tooling provided by major vendors dictate how smoothly and securely you can deploy them without external telemetry, cloud registration, or internet connectivity.

According to technical documentation and deployment blueprints from industry leaders and government contractors like NVIDIA AI Enterprise Documentation and Mirantis , the primary silicon vendors and their enterprise ecosystems approach air-gapped sovereign implementations through distinct frameworks:docs.nvidia.com+1

1. NVIDIA

• Silicon Offerings: H100, H200, B200, GB300 NVL72 , and L40S Tensor Core GPUs.
• Air-Gapped & Sovereign Approach: NVIDIA hardware is heavily integrated into turnkey reference designs like the NVIDIA AI Factory for Government . While NVIDIA GPUs function natively on bare metal offline, the differentiator is their software stack.mirantis.com+1
• Considerations: To run securely offline, you utilize NVIDIA AI Enterprise deployed via localized containers and offline license servers (or disconnected Enterprise License Administration). Management tools like NVIDIA AI Enterprise and Base Command Manager can be configured to operate entirely on-premises with FIPS-140 compliance and STIG hardening, avoiding any mandatory telemetry home-calls. System builders like Dell, Supermicro, and specialized sovereign cloud providers routinely package these for physical isolation.globalai.com+1

2. AMD

• Silicon Offerings: AMD Instinct MI300X and MI325X accelerators.
• Air-Gapped & Sovereign Approach: AMD approaches the market with an open-software philosophy via its ROCm (Radeon Open Compute) software stack. Because ROCm and its associated libraries (MIOpen, RCCL) are largely open-source, government agencies and defense contractors find it easier to audit, modify, and run entirely local, closed-loop software stacks without proprietary cloud dependencies.
• Considerations: AMD works closely with major server vendors (HPE, Supermicro, Dell) to deliver completely on-premise clusters. Management and orchestration rely on standard Linux kernel drivers and open tooling (like Kubernetes/Slurm), which naturally support strict air-gapped environments without relying on external licensing validation checks.

3. Intel

• Silicon Offerings: Intel Gaudi 3 AI accelerators.
• Air-Gapped & Sovereign Approach: Intel’s Gaudi accelerators utilize an open software integration model built around standard Ethernet-based scaling (RoCE) and optimized oneAPI/PyTorch frameworks.
• Considerations: Intel Gaudi software tools and drivers can be mirrored locally into internal container registries. Because Gaudi does not rely on a proprietary, cloud-tethered management plane for basic operation, it integrates smoothly into classified or localized on-premise data centers managed by local orchestrators.

Key Architectural Requirements for Air-Gapped GPU Deployment

Regardless of which vendor's silicon you select, building a true air-gapped sovereign cloud requires focusing on the surrounding stack rather than the chip itself:

• Local Container Registries: You must mirror all base images (CUDA/ROCm runtimes, PyTorch/TensorFlow containers, Triton Inference Server) onto an internal, firewalled artifact repository.
• Disconnected License Management: Ensure the vendor supports offline license keys or local-only license server appliances (e.g., local NVIDIA Local License Server / SLS instance).
• Firmware & Security Auditing: Confirm that management microcode (BMC/IPMI and GPU VBIOS updates) can be side-loaded via manual, cryptographically signed offline packages rather than active cloud update feeds.

If you'd like to narrow this down, tell me:

• Are you leaning toward a specific model architecture or workload (e.g., massive training vs. localized RAG/inference)?
• Do you have a preferred server OEM partner (like HPE, Dell, or Supermicro) already locked in for the hardware delivery?

For a government sovereign AI cloud, I would shortlist NVIDIA, AMD, and server-system vendors such as HPE and Dell Technologies first. The key distinction is that a GPU can run in an air-gapped data center, but the complete platform—including drivers, AI runtimes, model artifacts, Kubernetes components, licensing, and security updates—must also work without internet access.

As of October 2026, there is documented support for disconnected AI deployments across both NVIDIA and AMD ecosystems, including government-oriented NVIDIA software and AMD's ROCm stack.docs.nvidia.com+3

Vendors to evaluate

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1. NVIDIA — strongest software ecosystem

(Top shortlist)

• GPUs: RTX PRO 6000 Blackwell Server Edition for inference; HGX B200/B300 systems for large-scale training and inference.
• Air-gap evidence: NVIDIA documents offline deployment for NIM model-serving software and disconnected installation of its GPU Operator. Its government reference architecture explicitly covers air-gapped operations and classified enclaves.docs.nvidia.com+2
• Best for: A broad enterprise AI cloud with mature CUDA, model-serving, and orchestration support.

Official resources: NIM air-gap guide ·GPU Operator guide

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2. AMD — credible alternative

• GPUs: Instinct MI355X, MI350X, MI325X and MI300X, subject to workload and software compatibility.
• Air-gap evidence: AMD publishes an air-gapped installation guide for its Kubernetes GPU Operator, covering internal container registries and offline image delivery.instinct.docs.amd.com+1
• Best for: Organizations prioritizing an alternative to CUDA, an open software stack through ROCm, and more supplier diversity.

Official resources: AMD air-gap guide ·Instinct GPU portfolio

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3. Hewlett Packard Enterprise (HPE) — turnkey sovereign infrastructure

• Platform: HPE Private Cloud AI and HPE Sovereign AI Factory.
• Air-gap evidence: HPE explicitly offers air-gapped options for its sovereign AI infrastructure, with on-premises control over data, infrastructure, and models.www.hpe.com+1
• Best for: Agencies seeking integrated infrastructure, lifecycle support, security controls, and deployment services rather than assembling every component independently.

Official resource:HPE Sovereign AI Factory

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4. Dell Technologies — enterprise and federal deployments

• Platform: PowerEdge XE7740/XE7745 and Dell AI Factory with NVIDIA.
• Air-gap evidence: Intel's Software Catalog lists a Dell air-gapped enterprise inference deployment that stages containers, packages, binaries, and model files through an intermediary system. NVIDIA also lists Dell Federal government AI configurations.aiswcatalog.intel.com+1
• Best for: Agencies that want an established enterprise server vendor and a supported path to deploying GPU-backed inference offline.

Official resources: Intel Software Catalog ·NVIDIA validated reference architectures

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5. Supermicro — configurable GPU infrastructure

• Platform: NVIDIA HGX and RTX PRO-based AI servers.
• Evidence: NVIDIA includes Supermicro among the system partners for its enterprise AI server portfolio and government-oriented reference ecosystem.docs.nvidia.com+1
• Best for: Custom server configurations, high-density GPU nodes, and organizations with an internal platform engineering team.

Official resource:Supermicro

Qualification: validate the exact server, firmware, GPU driver, and software stack with Supermicro; the partner relationship alone does not guarantee a complete air-gapped solution.

How I would shortlist them

These are starting points for evaluation, not a determination that any product satisfies your agency's accreditation or classified-system requirements.

What to require in the procurement

A GPU being physically installed on-premises is not sufficient to qualify the system as air-gap-ready. I would require vendors to demonstrate the following:

• Fully offline installation: Drivers, firmware utilities, GPU operators, containers, model weights, tokenizers, and dependencies can be installed from approved offline media or internal repositories.
• No mandatory external dependencies: Runtime inference and administrative operations do not require public registries, cloud APIs, internet license checks, or vendor telemetry.
• Controlled updates: Signed packages, software bills of materials (SBOMs), vulnerability disclosures, and a documented process for securely importing updates.
• Security and compliance: Support for the agency's specific requirements, such as FIPS-validated cryptography, STIG hardening, supply-chain assurance, and applicable government authorizations. Verify each requirement individually.
• Operational independence: Local monitoring, identity management, logging, orchestration, backups, and disaster recovery continue to work while disconnected.

For NVIDIA, specifically test the distinction between its documented air-gap procedures and the subset of components designated Government Ready; NVIDIA states that not every GPU Operator component is covered by that designation.docs.nvidia.com+4

Two questions that would narrow this considerably

1. What is the security boundary? A fully disconnected classified network, a restricted government network with controlled outbound access, or a sovereign cloud with no foreign operational control?
2. What scale are you planning? A small inference cluster, a 100–500 GPU cloud, or a 1,000+ GPU training and inference platform?

Those answers determine whether to prioritize a turnkey HPE/Dell solution, a custom NVIDIA HGX architecture, or an AMD-based platform with a separately validated offline software stack.