NewSovereign AI: private AI infrastructure on hardware you rent or own, fully managed.
NewSovereign AI: private AI infrastructure on hardware you rent or own, fully managed.
Nov 06, 2026
Written by: Hoonify
The best private AI infrastructure for classified work is the one that runs both your AI and your simulation workloads on hardware you control, with no outside connection, and gets your team to a first real run in weeks rather than a year. Accredited government clouds from AWS and Google win on paperwork. Hardware stacks from NVIDIA and HPE win on raw capacity. A fully managed platform like Hoonify wins when you need AI and high-performance computing working together inside the gap, without hiring a team to build it.
Most buyers don’t fail on hardware. They fail in the long stretch between the purchase order and the first useful job, when the cluster sits half-configured because nobody can pull a package from the internet. This guide compares the options on the three things that decide whether an air-gapped system actually gets used: deployment security, workload orchestration, and time to first run.
Air-gapped private AI infrastructure runs models, training jobs, and simulations on a network with no path to the public internet. Everything it needs (container images, model weights, drivers, schedulers, patches) has to arrive by controlled transfer and install without phoning home.
That sounds like a networking problem. It’s mostly a software supply problem. A normal enterprise AI platform assumes it can pull from a registry, fetch a model from Hugging Face, and check a license server. Cut those links and every hidden dependency becomes a failed install.
For classified programs the bar is higher. The platform has to:
Score every option on three criteria. Spec sheets cover GPUs well. They rarely cover these.
Ask where the platform physically runs and who can touch it. A hosted classified region is isolated, but it’s still someone else’s data center and someone else’s operators. Hardware in your own facility puts the trust boundary at your door. Then ask how software crosses the gap: signed offline bundles, a documented manifest of every image, and updates you can inspect before import.
The question that separates vendors: does any part of the stack need to call out to work? License checks, telemetry, and model downloads are the usual culprits.
Classified programs rarely run only AI. A national lab group might run Monte Carlo transport codes in the morning and a fine-tuned model over the results in the afternoon. Kubernetes handles containers and inference well. Slurm and similar schedulers handle tightly coupled MPI jobs well. Few platforms give you both on one pool of hardware.
Check whether the platform schedules AI and HPC together, or whether you’ll end up running two clusters with two teams.
This is the criterion buyers underweight most. Measure the days from hardware on the floor to a researcher submitting a real job, not to a green dashboard. Ask the vendor for that number in writing, and ask who does the work: their engineers, a systems integrator, or your staff.
A platform that can start in a cloud environment and move into your enclave later lets your team build and validate workflows before the classified system is even accredited.
Six options, in four categories, cover most of the market. They aren’t all the same kind of product, which is the first thing buyers miss.
| Platform | What you get | Runs on your hardware, fully disconnected | Stated accreditation | Orchestration | Time to first run |
|---|---|---|---|---|---|
| AWS Secret and Top Secret Regions | Managed classified cloud regions | No, AWS-operated and isolated | Up to Top Secret (ICD 503); Secret at IL6 | AWS services; not specified for HPC | Not published |
| Google Distributed Cloud Hosted | Air-gapped hosted cloud | Air-gapped, Google stack | Top Secret and Secret (IC); Top Secret (DoD) | Kubernetes (GKE) | Not published |
| NVIDIA AI Factory for Government | Reference design built by Dell, HPE, Lenovo, Cisco, Supermicro | Yes, with partner software | Varies by partner | Kubernetes platforms | Depends on integrator |
| HPE Private Cloud AI | Turnkey on-prem AI stack with NVIDIA | On-prem; air-gap not stated | Not stated | Inference runtimes, NVIDIA NIM | Under 8 hours to deploy (vendor claim) |
| Kubernetes AI platforms (Red Hat OpenShift, Nutanix, Spectro Cloud, Domino) | Software you install on hardware you buy | Yes | Partner claims include IL5 and IL6 | Kubernetes | Depends on your team |
| Hoonify | Fully managed Sovereign AI and Sovereign HPC | Yes, cloud to air-gapped | No accreditation claimed | TurbOS for HPC plus AI inference | About an hour in Hoonify’s cloud; weeks to your own system |
Accreditation and deployment figures are as each vendor publishes them on the linked pages. Confirm current status with each vendor before you shortlist.
These win when your program needs an existing authorization and you’re comfortable with a hyperscaler operating the environment. AWS says its Top Secret cloud offers “complete physical and network isolation” with cleared U.S. support staff. Google’s GDC Hosted is authorized for Top Secret and Secret IC missions.
The trade-off is control. You get a region, not a machine. HPC tuning, scheduler choice, and hardware refresh run on the provider’s roadmap.
NVIDIA’s government reference design scales from 4 to 32 nodes and 256 GPUs or more, delivered through server partners. HPE Private Cloud AI packages the stack in one console. Both put the hardware in your facility.
They’re strong on capacity. You still need someone to integrate the software, move every dependency across the gap, and run simulation workloads alongside the AI stack.
Red Hat OpenShift, Nutanix Enterprise AI, Spectro Cloud, and Domino all describe air-gapped or classified deployments. They give you a mature container platform and real flexibility.
They’re built for containers and model serving, not tightly coupled MPI simulation. Your team owns the install, the integration, and the long tail of offline updates.
Hoonify runs open AI models and HPC workloads as a fully managed service that starts in Hoonify’s cloud and moves to servers you rent or own, on-prem or fully air-gapped. TurbOS, Hoonify’s HPC software, stands up a full HPC environment in about an hour in the cloud. Los Alamos National Laboratory and Sandia National Labs use it today.
Hoonify doesn’t hold a classified-cloud authorization, and doesn’t need one for this model: once the system sits inside your enclave, the vendor is outside your audit boundary.
Match the option to the constraint that’s actually binding.
| If your situation is… | Start with |
|---|---|
| You need an existing Top Secret authorization now and will accept a provider-operated region | AWS Top Secret or Google Distributed Cloud Hosted |
| You’re buying hundreds of GPUs and have an integrator and platform team in place | NVIDIA AI Factory for Government through a server partner |
| You want an on-prem AI stack in one box and mainly run inference | HPE Private Cloud AI |
| You already run OpenShift or Nutanix and mainly serve containerized models | A Kubernetes AI platform |
| You need AI and simulation on the same hardware, inside your enclave, without building a platform team | Hoonify |
One pattern shows up across programs: the secure AI infrastructure purchase gets approved long before the facility is ready. Pick a platform that lets researchers work in an unclassified environment first and carry the same workflows across the gap later. That’s where most of the lost months come back.
Hoonify helps companies run AI and HPC they own, control, and trust, from our cloud to their air-gapped data center.
We built it for the gap between buying hardware and getting work done. Hoonify’s founders come from Sandia National Laboratories and Cray Research, and our team brings more than 100 years of combined national lab experience. We come from places where the wrong answer is worse than no answer, and that’s still the standard we build to.
On deployment security, Sovereign AI and Sovereign HPC run on servers you rent or own, on-prem or fully air-gapped, with zero data retention. No prompts, outputs, or logs leave your environment.
On orchestration, TurbOS turns your CPUs and GPUs into one working environment for HPC, and Sovereign AI serves open models (GLM, Qwen, Gemma) through an OpenAI-compatible API. Buy one or both.
On time to first run, you start in Hoonify’s cloud, where TurbOS stands up a full HPC environment in about an hour. Then you move to your own hardware in weeks, with the same workflows.
It’s a fully managed solution, so your researchers run jobs while we handle the platform.
It’s a system that runs AI models and compute workloads on a network physically or logically cut off from the internet. Software, models, and updates arrive by controlled offline transfer.
Yes, but most platforms are built for one or the other. Kubernetes platforms favor AI inference. HPC schedulers favor simulation. Look for a platform that runs both on one pool of hardware.
It ranges from hours to well over a year, depending on who integrates the software. Ask vendors for the time from hardware delivery to a researcher’s first real job.
Not quite. AWS and Google offer isolated classified regions they operate. An air-gapped system in your own facility keeps the hardware and the operators inside your boundary.
Ask whether any component needs an outside connection to install, license, or update. Then ask for the time to first run in writing.
They limit you to models you can bring inside. Open-weight models like Qwen, GLM, and Gemma run fully offline. Closed API models don’t.
Start in Hoonify’s cloud, run your own AI and simulation workloads, and move inside the gap when your facility is ready. Talk to our team about sizing private AI infrastructure for your program.
Hoonify runs AI and HPC for organizations that can’t afford to get it wrong. Our founders come from Cray and Sandia National Laboratories, and our team brings more than 100 years of national lab engineering to every system we build. We learned our standards in places where a wrong answer is worse than no answer: national labs, defense programs, energy research, and life sciences. For five years, we’ve run TurbOS, our HPC software, in production for customers including Los Alamos and Sandia National Laboratories. Now we bring that same discipline to AI. Customers choose Sovereign AI, Sovereign HPC, or both, fully managed by our engineers. They can start in our cloud and move on-prem or air-gapped when they’re ready, with open models, standard APIs, and no lock-in along the way. You keep the data, the hardware, and the roadmap. We keep it running.
