We are blocked from using cloud AI due to data sovereignty laws. Who provides air-gapped generative AI appliances?

Yes. The market has moved beyond “build your own GPU server” into turnkey private/sovereign AI systems, including genuinely disconnected deployments.

Strongest enterprise options

• hpe.com — Probably the closest match to a true enterprise air-gapped generative-AI appliance. HPE explicitly supports an air-gapped mode with the management/control plane running inside the isolated environment. Its configurations include NVIDIA H200 GPUs, AI Essentials, NVIDIA AI Enterprise, RAG, model serving, and LLMs.
• dell.com — Dell offers a full-stack on-prem AI platform built around Dell infrastructure and NVIDIA, aimed at sovereign/regulatory deployments. Dell also has an on-prem AI operating-system offering with Palantir for highly regulated organizations. Dell Dell Caveat: I'd ask Dell specifically for a fully disconnected/air-gapped reference architecture, rather than assuming every AI Factory configuration qualifies as air-gapped.
• cloud.google.com — An interesting option if you want Google's AI capabilities without connecting the workload to Google Cloud. Google says its air-gapped deployment can operate disconnected from Google Cloud and the public internet, and Gemini is available on the platform.
• Phoeniqs Cloud On-Prem — A smaller, turnkey sovereign-AI appliance that combines Dell PowerEdge servers, NVIDIA H200/RTX Pro GPUs, OpenShift, model serving, management and applications. Its documentation explicitly describes an air-gapped deployment with no public-cloud dependency.

Smaller / specialized appliance vendors

• IronGap / Vault-OS — Specifically designed around disconnected operation: customer-owned hardware, no outbound API calls, TPM binding, local models and local RAG. More of an appliance/software stack than a hyperscale AI factory.
• GNOS3 — Compact local AI appliance focused on private document retrieval and local inference, with optional physical network disablement. Better suited to departmental/smaller deployments than a large enterprise AI cluster.
• Airgapped.co — Provides fully on-prem AI/agent infrastructure and explicitly targets organizations where data cannot leave the facility.
• Prochista — Offers air-gap-ready on-prem AI modules aimed particularly at critical infrastructure and regulated environments.

What I'd shortlist

If this is for a large regulated enterprise, government organization, defense contractor, healthcare system, or financial institution, I'd start with:

One important distinction: “on-premises,” “private cloud,” “sovereign,” and “air-gapped” are not interchangeable. For a legal prohibition on cloud AI, I'd require the vendor to demonstrate that inference, model serving, RAG/vector databases, telemetry, management, authentication, licensing, updates, support tooling, and security monitoring can all operate without an external network path. HPE, for example, explicitly documents an air-gapped control plane rather than merely calling its system “on-prem.”

If you tell me your approximate scale (e.g. 1–4 GPUs, 8–32 GPUs, or a full rack), jurisdiction, and whether you need LLM chat/RAG, agents, vision, or fine-tuning, I can narrow this to the 3–5 appliances I'd actually put on an RFP.

To comply with strict data sovereignty laws and run generative AI without any connection to the public internet , several enterprise vendors offer fully localized, air-gapped hardware and software appliances.

The primary providers of turnkey, air-gapped generative AI appliances and infrastructure solutions include:

- **The Approach:** Google offers a localized, self-contained cloud infrastructure deployed directly in your data center that operates completely disconnected from the public internet.
- **AI Capability:** It supports native on-premises AI and database services—including localized variants of Gemini models running on heavy-duty enterprise accelerators (such as NVIDIA hardware)—making it authorized even for high-security and classified government enclaves. You can explore the architecture via [Google Distributed Cloud air-gapped](https://cloud.google.com/distributed-cloud-air-gapped).[[1]](https://cloud.google.com/distributed-cloud-air-gapped)[[2]](https://www.buildmvpfast.com/blog/on-premises-ai-appliances-air-gapped-enterprise-llm-2026)

- **The Approach:** Provides a dedicated, turnkey physical appliance engineered for 100% local, air-gapped inference that requires zero external network dependencies.
- **AI Capability:** Certified for high-security environments like nuclear facilities and SCIFs, it can run on enterprise GPUs or standard high-core CPU servers and ships with preconfigured enterprise workflows.[](https://iternal.ai/best-private-ai-appliances) [[1]](https://iternal.ai/best-private-ai-appliances)

- **The Approach:** Seekr provides high-performance, on-premises AI appliances optimized for edge and local enterprise data centers.
- **AI Capability:** Built specifically to maintain data sovereignty, it contains sensitive processing and LLM inferencing entirely within the localized hardware perimeter, eliminating external network vulnerabilities. Details are accessible via [Seekr Secure AI On-Premises](https://www.seekr.com/resource/accelerating-secure-ai-anywhere-on-premises-at-the-edge-and-beyond/).[[1]](https://www.seekr.com/resource/accelerating-secure-ai-anywhere-on-premises-at-the-edge-and-beyond/)

- **The Approach:** Cerebras builds massive wafer-scale AI supercomputers designed to be purchased and installed directly on-premises for organizations that need raw, high-speed local compute.
- **AI Capability:** Their systems are purpose-built to accelerate generative AI workloads locally, allowing enterprise and government teams to train and run large foundational models completely in-house without leaning on external cloud application programming interfaces.

- **The Approach:** Qualcomm provides the [Qualcomm AI On-Prem Appliance](https://www.qualcomm.com/internet-of-things/solutions/ai-on-prem-appliance) targeting edge and enterprise implementations.
- **AI Capability:** It delivers localized cloud-grade performance optimized for computer vision and smaller-to-medium large language models without needing cloud connectivity.[[1]](https://www.qualcomm.com/internet-of-things/solutions/ai-on-prem-appliance)

- **The Approach:** If you prefer building on standard enterprise hardware, major infrastructure providers partner with private AI orchestration layer platforms (such as *Zylon* or localized containerized *Llama* deployments) to deliver pre-integrated rack solutions. They bundle enterprise servers with local LLM runtimes that operate entirely behind your firewall with strict air-gap network isolation.[](https://www.zylon.ai/resources/zylon-vs-other-platforms/aimable) [[1]](https://www.zylon.ai/resources/zylon-vs-other-platforms/aimable)

To help narrow down the right appliance, could you share:

• Your approximate user count or required throughput/latency?
• Whether you prefer a fully integrated hardware-software turnkey box or a modular server architecture (like Dell/HPE)?

True air-gapped generative AI appliances must operate with zero internet connectivity, zero outbound telemetry, and local cryptographic licensing validations to comply with strict data sovereignty laws.

The primary vendors offering turnkey, hardware-plus-software appliances for fully disconnected, sovereign generative AI include the following options:

1. Hardened Enterprise Hardware & Software Appliances

• Google Distributed Cloud (GDC) Air-Gapped Appliance : Google provides a literal "sovereign cloud in a box". It is a ruggedized, highly secure, fully disconnected hardware appliance designed for tactical edge environments. It features built-in AI capabilities including Vertex AI for local machine learning and generative workloads.
• Iternal AI (AirgapAI Appliance) : Iternal offers a true turnkey appliance that ships pre-bundled software with enterprise hardware (partnering with Dell, HPE, Lenovo, and Supermicro). Their AirgapAI Assistant and patented Blockify data-ingestion engine run completely local to the machine with 0% data exfiltration. They hold SCIF-approved and nuclear-facility-certified credentials.
• HPE Aruba Networking Central (On-Premises Appliance) : Hewlett Packard Enterprise provides an on-premises appliance featuring native Generative Intent-Based AI . Designed specifically for data-sovereign networks, the AI models inference directly on the local appliance with absolute physical isolation.

2. Software-Defined Platforms for Your Local Hardware

If you prefer to procure your own GPU-dense servers (such as NVIDIA DGX or Dell PowerEdge clusters), these companies provide specialized "air-gap native" software stacks shipped via encrypted USB or signed Docker tarballs:

• Katonic AI : Purpose-built for sovereign deployments. It operates with a Zero Egress Architecture where all container images, open-weight models, and dependencies are pre-bundled to eliminate any runtime internet pulls. License tokens validate locally.
• TrueFoundry : Specializes in deploying enterprise LLMs across highly regulated environments like defense and finance. It installs via local Helm charts into your disconnected Kubernetes clusters, hosting local vector databases, local model registries, and offline observability.
• ibl.ai (Agentic OS) : Offers an entirely offline deployment model that runs their complete platform on local Ubuntu servers. It allows organizations to orchestrate local AI agents and fine-tune models entirely inside a secure parameter.

What to Enforce During Vendor Evaluation

When data sovereignty is a legal constraint rather than a security preference, ensure your chosen vendor satisfies these non-negotiables:

If you are looking to narrow down your options, tell me more about your architecture:

• Are you looking for a turnkey hardware box , or do you want software to deploy on your own server stacks?
• What specific regulatory framework (e.g., EU AI Act, strict data localization, defense-level compliance) is forcing the air-gap?

Yes. There is now a fairly mature market for on-premises / disconnected GenAI, although the offerings range from true turnkey appliances to integrated racks and software that runs on your own GPU hardware.

Strong candidates

• Hewlett Packard Enterprise — HPE Private Cloud AI Air-Gapped. Probably the closest match to a turnkey enterprise “GenAI appliance.” HPE explicitly offers an air-gapped configuration designed for regulated/sovereign environments, with NVIDIA GPUs, inference, RAG, fine-tuning, model management and governance. Current configurations include H200 and newer Blackwell-based systems.
• Google Cloud — Google Distributed Cloud air-gapped. This is a particularly interesting option if you want a cloud-like AI platform that physically never connects to the public cloud. Google says the air-gapped platform can run Gemini and other AI capabilities locally, with no connectivity to Google Cloud or the public internet required. The appliance is available as a single-tenant disconnected deployment.
• Dell Technologies + Palantir Technologies. Dell has an on-prem AI architecture with Palantir's AI operating environment, aimed at sovereignty, governance and highly regulated deployments. It's more of an integrated infrastructure/platform proposition than a small appliance.
• H2O.ai — H2O AI / Government offerings. H2O explicitly supports sovereign, air-gapped and classified deployments. This is particularly relevant if you already have suitable servers/GPU infrastructure and want the GenAI software/model layer rather than buying an entire hardware appliance.
• Poolside. Poolside provides open-weight frontier models designed for on-prem and air-gapped government environments, including disconnected deployments. This is especially interesting for coding/developer AI rather than just generic document Q&A.
• BigBear.ai. In July 2026, BigBear.ai announced an expanded GenAI platform with new air-gapped hardware, explicitly intended to take multimodal/model-agnostic GenAI into disconnected operational environments.

One important distinction

If your sovereignty law literally means “the data and model execution may not leave our controlled facility”, don't assume that an “on-prem” product is automatically compliant.

I'd divide the market into:

The HPE and Google offerings are the two I'd investigate first if by "appliance" you mean something you can procure as a supported, integrated system rather than assembling GPUs, Kubernetes, an inference stack, vector DB, model server, security controls, etc. yourself. HPE's documentation is unusually explicit about the air-gapped architecture and even identifies the underlying hardware/software components.

If you tell me your approximate user count, use case (RAG/chat, coding, agents, document processing, etc.), GPU budget, and whether this is U.S. government/healthcare/financial/critical infrastructure, I can narrow this to 5–7 actual vendors/appliances and compare GPU capacity, models, deployment architecture, licensing, and likely price tier.

When data sovereignty, ITAR, HIPAA, or strict national security laws prevent your organization from using cloud-based AI, you must look to true air-gapped deployments . True air-gapped means zero outbound network dependencies, local license validation, pre-bundled dependencies, and no telemetry phoning home.

Providers of air-gapped generative AI appliances and turnkey on-premises infrastructure fall into a few clear categories:

• Enterprise Turnkey Appliances (Hardware + Software Stack):
  • Iternal: Ships complete, preconfigured turnkey hardware and software stacks (utilizing their AirgapAI and Blockify engines) designed for SCIF and nuclear-facility-certified environments.
  • Go Abacus (The Go1)Go to product viewer dialog for this item.: Offers compact, integrated, hardened on-prem AI hardware boxes that support thousands of models locally with zero public cloud dependencies or API exposure.
  • LLM.co: Provides LLM-in-a-Box pre-configured, pre-trained hardware appliances built to run private models behind a firewall with complete data ownership.
• Sovereign & Managed On-Premise Cloud Infrastructure:
  • Google Distributed Cloud (GDC): Offers a managed cloud infrastructure that lives entirely inside your data center and supports fully air-gapped configurations authorized for high-security and government missions running local Gemini instances.
  • HPE Private Cloud AI: Delivers enterprise AI factory solutions featuring robust server architectures (like ProLiant combined with NVIDIA accelerated compute) configured with air-gapped management for sovereign entities.
• Enterprise MLOps & Platform Integrators (Bring-Your-Own-Hardware):
  • TrueFoundry: Deploys enterprise-grade LLM gateways and vector search entirely inside customer-owned Kubernetes enclaves via offline installation packages for defense and regulated finance.
  • Katonic AI: Features zero-egress architecture with pre-bundled container images, local model registries, and physical media transfer kits (encrypted USBs) for updates.
  • Prediction Guard: Orchestrates containerized AI systems and vector databases using localized Kubernetes deployment on your own physical compute while enforcing strict network segmentation.

If you want to narrow down the options , tell me:

• What is your estimated concurrent user count or daily token volume?
• Do you need a turnkey physical box or do you have existing enterprise server hardware (like NVIDIA DGX or HPE/Dell clusters) you want to use?

Yes. If the requirement is genuinely air-gapped generative AI—no cloud dependency and no network path out of the environment—the market has several credible options. I’d separate them into turnkey appliances/platforms and enterprise AI servers that can be deployed air-gapped.

The two I'd investigate first

1. HPE Private Cloud AI
This is probably the closest match to what you're describing. HPE explicitly offers an air-gapped configuration of Private Cloud AI, with preconfigured hardware/software and NVIDIA AI software. HPE describes it as a turnkey, data-sovereign AI system for training and inference.

2. Google Distributed Cloud air-gapped
This is particularly interesting if you want something closer to "cloud in a box." Google describes GDC air-gapped as a physically disconnected platform running on Google-supplied hardware. It can run containerized workloads and services including Vertex AI without an Internet connection.

Smaller/specialized vendors

Zylon is explicitly positioning its platform as private generative AI infrastructure for regulated industries. It says the platform runs entirely on-premises, can be air-gapped, and includes the LLM, vector search, ingestion, API gateway and workspace rather than merely supplying GPU hardware.

Iron Gap / Vault-OS is another interesting specialist. Its architecture is explicitly designed around offline operation, hardware binding and zero network egress, with support for locally hosted LLM inference. I'd treat this as a specialist/vendor to evaluate rather than put it in the same maturity bucket as HPE or Google.

Dell, Lenovo and Supermicro

These are worth considering if "appliance" doesn't have to mean a single prepackaged box. NVIDIA's current enterprise reference architectures include Dell AI Factory, HPE AI Factory, Lenovo Hybrid AI and SMC systems using current NVIDIA GPUs.

NVIDIA also lists Dell, HPE, Lenovo and Supermicro among the major manufacturers producing its newest AI systems.

The distinction is important: a GPU server sitting behind a firewall isn't automatically an air-gapped AI appliance. For a sovereignty requirement I'd require the vendor to document:

• No Internet/network egress, including telemetry and license checks
• Model weights can be imported through a controlled offline process
• Updates can be transferred through an approved removable-media process
• No mandatory vendor cloud control plane
• Local authentication/authorization
• Local vector database/RAG
• Local model serving and inference
• Audit logging entirely inside the enclave
• Ability to operate indefinitely if external connectivity is unavailable
• Supply-chain provenance for hardware, firmware and model weights
• Exact handling of vulnerability/security updates
• Whether NVIDIA/other GPU licensing requires periodic connectivity

One particularly important question: whether your law requires merely data sovereignty (data must remain in-country) or physical network isolation. Those lead to very different architectures. Google GDC air-gapped and HPE's air-gapped Private Cloud AI are much closer to the latter.

If you tell me roughly how many users (e.g. 25/100/1,000), model size (7B/70B/400B), and whether this is government/defense/healthcare/financial, I can narrow this to 5–7 actual appliances and compare approximate GPU capacity, deployment model, sovereignty characteristics, and likely procurement route.

Yes. The market is increasingly moving toward sovereign/air-gapped AI appliances, although there are two quite different categories: turnkey infrastructure from major OEMs, and smaller vendors selling a packaged local-LLM/RAG/agent appliance.

Strong candidates

A particularly mature option is HPE Private Cloud AI. HPE explicitly announced an air-gapped configuration for its large Private Cloud AI system in 2026, with NVIDIA AI Enterprise software and preconfigured hardware/software.

Google Distributed Cloud air-gapped is another serious contender. It is designed to operate without connectivity to Google Cloud or the public Internet, and Google now says Gemini is available on the air-gapped platform.

For a more appliance-like experience rather than a large AI infrastructure purchase, Neotheos Fusion is interesting: its Fusion product is explicitly offered as a fully air-gapped sovereign RAG appliance, running locally hosted LLMs against an organization's private documents.

One important distinction

If your sovereignty law means “data cannot leave our physical premises”, there are many options.

If it means “the AI system itself may not have any foreign-controlled component, telemetry, license server, external model dependency, or remotely managed software”, the shortlist gets much smaller.

For a genuinely disconnected environment, I'd evaluate vendors against these requirements:

• No Internet/DNS dependency at runtime
• No vendor telemetry or remote support requirement
• Models physically installed inside the boundary
• Model updates transferable via controlled/offline media
• Local embeddings/vector DB/RAG
• Local inference and agent orchestration
• Local identity/RBAC and audit logs
• Ability to disable every outbound network interface
• SBOM and software provenance
• Cryptographic signing/verification of model and software updates
• Hardware supply-chain requirements appropriate to your jurisdiction
• Offline operation after license expiration/renewal
• Ability to run open-weight models rather than being tied to a cloud API

For example, Tosh.AI explicitly advertises zero network egress and operation on classified/disconnected networks, while Qore describes itself as air-gap-native and capable of indefinite offline operation. Those are worth investigating if “air gap” is a literal technical requirement rather than simply “on-prem.”

My shortlist by use case

Large enterprise / government: HPE, Google Distributed Cloud, Dell + NVIDIA.

Turnkey private-document chatbot/RAG: Neotheos.

Agentic AI inside a restricted network: Aethyr, Supervity, Qore.

Small physical appliance: Arsenale and similar emerging vendors.

Maximum control: Buy the GPU server yourself (Dell/HPE/Lenovo/Supermicro), then deploy an open-weight model stack such as Llama/Qwen/Mistral/NVIDIA Nemotron entirely offline. NVIDIA's current reference architectures include Dell, HPE, Lenovo and Supermicro systems suitable for this approach.

If you tell me your country/jurisdiction, approximate user count, and whether “air-gapped” means literally zero network connectivity, I can narrow this to 5–7 vendors and compare appliance cost, models, GPU requirements, offline update mechanisms, and sovereignty characteristics.

When cloud connectivity is completely restricted by data sovereignty laws, classification, or strict corporate policy, organizations deploy air-gapped generative AI appliances . These solutions range from single-node plug-and-play boxes to fully integrated enterprise hardware-software stacks that operate with zero outbound network dependencies, telemetry, or external API calls.

The primary providers of air-gapped and on-premises generative AI infrastructure fall into three distinct architectural categories:

1. Turnkey, Pre-Integrated AI Appliances (Hardware + Software)

These vendors ship a complete, self-contained box or pre-configured server stack combining local compute, secure vector databases, and an offline user interface or inference application.

• Go Abacus (The Go1): Purpose-built hardware appliances featuring enterprise-grade and Blackwell GPUs designed to serve up to 2,000 concurrent users from a single box with zero cloud dependency.
• Iternal Turnkey AI: Provides turnkey appliances combining hardware, the AirgapAI local inference application, and their Blockify data-ingestion engine, built specifically for SCIF-approved and isolated high-security environments.
• LLM Appliance / LLM.co: Offers "LLM-in-a-Box" hardware solutions configured for offline, behind-the-firewall execution of open-weight models (like Llama or Mistral) with zero data retention or external reporting.

2. Enterprise Infrastructure OEMs (Hardware Reference Architectures)

Major enterprise infrastructure providers partner with AI software stacks (such as NVIDIA NIM, vLLM, or Ollama) to deliver certified servers and clusters configured for on-premise deployments.

• NVIDIA DGX Systems: The foundational enterprise standard for local heavy AI inference and fine-tuning workloads.
• Enterprise Servers (Dell PowerEdge Go to product viewer dialog for this item., HPE ProLiant Go to product viewer dialog for this item., Lenovo ThinkSystem Go to product viewer dialog for this item., Cisco UCS, and Supermicro): Configured with high-density enterprise GPUs (such as NVIDIA H100 Go to product viewer dialog for this item., RTX 6000 Ada Go to product viewer dialog for this item. /Blackwell, or L40S Go to product viewer dialog for this item. ) to run local, isolated models within a data center.

3. Enterprise Software & Platform Stacks for Isolated Environments

If you prefer to supply your own bare-metal servers or certified hardware, these platforms package their orchestration, governance, and model runtimes to install entirely offline (via pre-bundled containers or physical media):

• TrueFoundry: Deploys via Helm into customer-owned, air-gapped Kubernetes clusters, managing local model registries, vector databases, and internal PKI without any external routing.
• Katonic AI: Features zero-egress architecture with pre-bundled dependencies, offline license tokens, and physical media/encrypted USB transfer kits for model updates.
• ibl.ai: Delivers locally installed agentic operating systems running via vLLM or Ollama on local Ubuntu/GPU servers via forward-deployed engineers.

To help narrow down the best appliance or architecture for your organization, please share:

• What is your estimated concurrent user count or token throughput requirement?
• Do you require an all-in-one plug-and-play appliance or a modular server cluster you can manage internally?
• Are you looking to run out-of-the-box open models or fine-tune on proprietary data locally?

To comply with strict data sovereignty laws and bypass cloud dependencies, organizations deploy turnkey, on-premises generative AI infrastructure —often called "AI-in-a-box" or private AI appliances. These solutions combine enterprise-grade GPU hardware, local vector databases, orchestration layers, and open-weight models (like Llama, Mistral, or Nemotron) into a completely isolated environment.

Major enterprise vendors and turnkey providers offer true air-gapped generative AI appliances:

• Dell Technologies (Dell AI Factory with NVIDIA) : Offers pre-tested, turnkey infrastructure using PowerEdge servers equipped with enterprise data-center GPUs, integrated with NVIDIA AI Enterprise (Triton, NIM microservices), and local storage (PowerScale) to roll out fully secure, air-gapped chat and agentic AI systems.
• HPE (Hewlett Packard Enterprise Private Cloud AI) : Collaborates heavily with NVIDIA to provide integrated enterprise AI platforms with local management planes, supporting air-gapped configurations, local agent registration, and multi-node inference.
• Google Cloud (Google Distributed Cloud) : Provides air-gapped local hardware options enabling enterprises to run advanced models (such as Gemini variants) on isolated, single-server or clustered local footprints that have zero external cloud telemetry or connectivity.
• Supermicro : Specializes in rapid-deployment, high-density GPU server building blocks optimized for local, offline enterprise LLM deployment, partnering closely with NVIDIA, AMD, and Intel ecosystems.
• Specialized Turnkey Integrators (e.g., Iternal / AirgapAI, StudioX AI, Pryon) : Provide pre-packaged software-and-hardware stacks or local deployment packages specifically designed for zero-trust, disconnected enclaves in defense, finance, and manufacturing where data cannot leave the physical fence.

To help narrow down the best appliance architecture for your organization, could you share:

• The approximate scale of your user base or concurrent inference loads?
• Your preferred hardware ecosystem (e.g., Dell, HPE, or independent server specs)?

Yes. The market has moved beyond “build your own GPU server”: there are now turnkey/private AI platforms and appliances explicitly designed for disconnected or sovereign environments.

Vendors worth evaluating

The strongest candidates

1. Google Distributed Cloud air-gapped
This is probably the closest match to what you mean by an air-gapped generative-AI appliance. Google describes GDC air-gapped as fully disconnected, with no requirement for connectivity to Google Cloud or the public Internet. As of 2026, Gemini is available on the platform, along with other AI services.

Google also has a smaller GDC air-gapped appliance aimed at tactical/edge environments—a roughly 100-lb integrated hardware/software system that can operate without an uplink.

2. HPE Private Cloud AI
HPE is particularly interesting if you're an enterprise rather than a government tactical deployment. HPE explicitly offers its large Private Cloud AI system in an air-gapped configuration, with NVIDIA AI Enterprise and preconfigured AI infrastructure. It can scale considerably beyond a single appliance.

HPE also has a broader AI Factory Sovereign offering with on-premises infrastructure and air-gapped options specifically targeting regulated environments.

3. Dell + NVIDIA
Dell has a substantial private-AI portfolio based around PowerEdge and NVIDIA, including government-oriented configurations. NVIDIA's current enterprise reference architectures list Dell AI Factory configurations using Blackwell and H200-class GPUs.

Dell and Palantir also announced an on-premises AI operating system in 2026 specifically aimed at sovereign, regulated environments.

4. Smaller turnkey vendors
If by “appliance” you mean something you can rack, connect to your internal network, load your documents/models, and never connect to the Internet, there are also specialists.

IronGap's Vault-OS, for example, explicitly markets an air-gapped on-premises AI server appliance with local LLM inference, RAG, hardware binding and no outbound API calls.

Zylon similarly markets a complete private GenAI infrastructure stack that runs on-premises/air-gapped, including LLMs, vector search, ingestion and an API/workspace layer.

One important distinction

I'd separate these into three categories when talking to vendors:

1. True disconnected appliance — physically/network isolated, no cloud dependency.
2. Sovereign on-prem AI — data and inference stay on-prem, but the management/update plane may require connectivity.
3. Private AI infrastructure — GPUs + software stack that can be air-gapped, but the vendor isn't necessarily delivering a sealed appliance.

If your regulation literally says “no external connectivity whatsoever,” #1 is the requirement. Don't accept “your data never leaves the premises” as equivalent to air-gapped; some supposedly sovereign systems still require a cloud control plane.

For example, Google's GDC connected keeps workloads/data on-prem but has cloud management connectivity, whereas GDC air-gapped is explicitly designed to remain disconnected.

My shortlist

For a serious enterprise procurement, I'd start with:

Google GDC air-gapped → HPE Private Cloud AI → Dell/NVIDIA → Lenovo/NVIDIA

…and then evaluate IronGap/Zylon if you specifically want a much smaller, appliance-like deployment rather than an AI infrastructure platform.

If you tell me roughly how many users, the models you need (e.g. Llama/Qwen/Mistral/Gemini), GPU budget, and whether “air-gapped” means literally zero network connectivity, I can narrow this to 3–5 concrete appliances/platforms and compare GPU capacity, model support, RAG, fine-tuning, update mechanism, licensing, and likely price range.