Sovereign AI · Reference architectures
Sovereign AI reference architectures
A sovereign AI reference architecture is a complete design for running AI inside an operation on infrastructure the operator owns or controls, in its own country, with open-weight models and no third-party AI service in the serving path. CodeNinja Atoms has published 18 of them, for 8 physical-world sectors in the United States (8), Pakistan (6), Saudi Arabia (4). Each names its sources, object model, models, compute, three-year cost and the person who approves every action, and each is free to reuse under CC BY 4.0 with a DOI.
18published designs8sectors3countries246ontology objects
Every published sovereign AI reference architecture
Newest first. The compute and cost columns are each design's own At a glance figures.
| Design | Sector | Country | Compute | Three-year cost | DOI |
|---|---|---|---|---|---|
| Turbine Island Watch | energy and utilities | Pakistan | One 48 GB PCIe inference GPU class card on ruggedised edge servers inside the plant perimeter, a PTP grandmaster time card and a one-way egress path; no cloud, because Pakistan has no hyperscaler region | About 22,500 US dollars to own the site envelope over three years (21,600 to 23,400), about the same as the deepest three-year AWS commitment for one L40S card in the UAE region, which could not keep the read one-way in any case (Appendix A) | 10.5281/zenodo.23365092 |
| Consist Watch | rail and transportation | United States | At the car, an EN 50155 and IEC 60571 module (8 by 8 by 8 inches, IP62, 24 to 48 VDC, CFR 238 materials); centrally, one 141 GB HBM GPU class node in a contractor-hosted US cloud | About 625,000 US dollars to run the eight-card node for three years on the deepest AWS commitment in US East, about three eighths of the 1.66 million on demand; the 225 modules are priced by quotation (Appendix A) | 10.5281/zenodo.23365118 |
| Hangar Form Cell | aviation and aerospace manufacturing | United States | A fanless industrial inference computer in the cell panel and an eight-card 48 GB PCIe inference server in the facility, joined one way across a hardware data diode; an OCP Time Card grandmaster orders every stream | About 202,000 to 220,000 US dollars to own the priced classes over three years, the scanner and the server most of it; the two compute tiers alone cost about two fifths of the deepest three-year AWS commitment for eight L40S cards, which the offline requirement rules out anyway (Appendix A) | 10.5281/zenodo.23308095 |
| Corridor Guard | oil and gas | Pakistan | One server-class host of the 48 GB PCIe inference GPU class inside the operator's estate; no field compute, no cloud, nothing crosses the boundary | About 19,500 US dollars to own the host over three years (18,700 to 20,200), about four fifths of the deepest three-year AWS commitment for one L40S card, which could not keep the data in Pakistan in any case (Appendix A) | 10.5281/zenodo.23308093 |
| Pit Camera Watch | discrete manufacturing and automotive | United States | One 48 GB L40S-class card per recorder host, in the conditioned server room; nothing at the poles | About 474,000 to 871,000 US dollars to own over three years, mostly camera heads; the two recorder hosts alone cost about four fifths of the deepest three-year AWS commitment, and there is no per camera license at any pole count (Appendix A) | 10.5281/zenodo.23296077 |
| Baseline | agriculture and earth observation | United States | The operator's ArcGIS Enterprise; nothing runs in a cloud the operator does not control | No compute to price; the acquisition is priced by the survey firm against the operator's own task table (Appendix A) | 10.5281/zenodo.23186673 |
| Fodder Watch | agriculture and earth observation | Saudi Arabia | One 48 GB L40S-class inference node inside the Kingdom; the card class needs a US export licence | About 15,200 dollars to own the node, about two thirds the deepest three-year AWS commitment, and renting would move the data outside the Kingdom (Appendix A) | 10.5281/zenodo.23186675 |
| Field Ledger | agriculture and earth observation | Pakistan | Open-source PostgreSQL with PostGIS on the operator's own servers; source code and full intellectual property handed over | No hardware line to price: the cost is the integration and software work (Appendix A) | 10.5281/zenodo.23186671 |
| Loop Integrity Watch | oil and gas | Pakistan | Two containers on the operator's own site application server, inside its OT boundary | No compute to price; the field equipment is priced by OEM quotation against the survey (Appendix A) | 10.5281/zenodo.23157967 |
| Reliability Atlas | energy and utilities | Saudi Arabia | No hardware bought; the study runs on the operator's own in-country servers | No hardware line to price: the cost is the assessment work itself (Appendix A) | 10.5281/zenodo.23157965 |
| Grid Context Watch | energy and utilities | United States | One node of eight 141 GB HBM-class GPUs: 753 GB of FP8 weights, 904 GB with the 1.2 planning factor, against 1,128 GB | About 510,000 dollars to own the reasoning node, about four fifths the deepest three-year AWS commitment (Appendix A) | 10.5281/zenodo.23157957 |
| Structure Phase Watch | heavy industry and construction | Saudi Arabia | One node of eight 141 GB HBM-class GPUs holds GLM 5.3 at FP8 (753 GB of weights, 904 GB with headroom) | About 642,000 US dollars with support and power at the Saudi industrial tariff | 10.5281/zenodo.23126448 |
| Steel Count Ledger | heavy industry and construction | Pakistan | One node of eight 141 GB HBM-class GPUs holds GLM 5.3 at FP8 (753 GB of weights, 904 GB with headroom), in a dedicated data center behind an export licence checkpoint | About 4,445,000 US dollars for 300 installation points, of which the counting kits are 2,832,000 and cannot be rented; owning the frontier node costs about 686,000 against 751,000 on AWS's deepest three-year plan | 10.5281/zenodo.23126563 |
| Port Twin | maritime and ports | United States | No equipment bought: about 1.1 GB of fp16 weights on the port's existing enterprise virtual machines | No hardware line to price: the design adds software and integration work to servers the port already runs | 10.5281/zenodo.23126431 |
| Factory Fire Watch | heavy industry and construction | Saudi Arabia | None bought: the requirement asks for no server or GPU, and the field gateways carry no model runtime | About 328,000 to 439,000 US dollars with support and power; the eight-factory pilot is about 21,000 to 26,000 of equipment | 10.5281/zenodo.23126565 |
| Feeder Firewatch | energy and utilities | United States | One node of eight 141 GB HBM-class GPUs holds GLM 5.2 at FP8 (753 GB of weights, 904 GB with headroom) | About 841,000 US dollars with support and power at the Texas industrial power price | 10.5281/zenodo.23159328 |
| Terminal Pulse | maritime and ports | United States | One node of eight 141 GB HBM-class GPUs holds GLM 5.3 at FP8 (753 GB of weights, 904 GB with headroom), at about 10 kW | About 722,000 US dollars with support and power at the US industrial power price | 10.5281/zenodo.23159331 |
| Sovereign HSE Watch | oil and gas | Pakistan | One node of eight 141 GB HBM-class GPUs holds GLM 5.3 at FP8 (753 GB of weights, 904 GB with headroom) | About 670,000 US dollars with support and power at Pakistan's industrial tariff | 10.5281/zenodo.23119714 |
What each sovereign AI reference architecture contains
| Part | What the design states |
|---|---|
| Sources joined | the existing systems the design reads, named, with what each one holds |
| Object model | the typed objects, properties and links that make the operation an ontology, published as hyper-ontology/1 JSON |
| Models | open-weight models only, each with its licence and why it was picked |
| Compute | the hardware sized from the models' own weights, from no new hardware to a GPU node |
| Three-year cost | owning against renting, every price cited with its source and date |
| Human control | the named person who approves every action that changes the physical world |
By country
United States. Consist Watch, Hangar Form Cell, Pit Camera Watch, Baseline, Grid Context Watch, Port Twin, Feeder Firewatch, Terminal Pulse.
Pakistan. Turbine Island Watch, Corridor Guard, Field Ledger, Loop Integrity Watch, Steel Count Ledger, Sovereign HSE Watch.
Saudi Arabia. Fodder Watch, Reliability Atlas, Structure Phase Watch, Factory Fire Watch.
Built from the published designs only: each paper's At a glance table and the dataset at Hugging Face. Their object models are collected at industrial ontology examples; questions by sector at sectors. Designed on Praxis; object models load into Hyper Ontology. Updated 2026-10-18.
Want one of these designs for your own operation? Talk to us.