# Sovereign AI reference architectures

Canonical: https://codeatoms.ai/sovereign-ai-reference-architectures/
License: CC BY 4.0
Publisher: CodeNinja Atoms (https://codeatoms.ai)

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.

| Design | Sector | Country | Compute | Three-year cost | DOI |
|---|---|---|---|---|---|
| [Turbine Island Watch](https://codeatoms.ai/steam-turbine-condition-monitoring-pakistan/) | 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](https://codeatoms.ai/rail-fleet-condition-monitoring-us/) | 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](https://codeatoms.ai/dieless-sheet-metal-cell-aerospace-mro-us/) | 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](https://codeatoms.ai/dc-interference-gas-pipelines-pakistan/) | 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](https://codeatoms.ai/test-site-camera-monitoring-us/) | 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](https://codeatoms.ai/vegetation-mapping-lidar-us/) | 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](https://codeatoms.ai/restricted-crop-monitoring-saudi-arabia/) | 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](https://codeatoms.ai/farm-data-dashboard-pakistan/) | 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](https://codeatoms.ai/tank-gauge-integrity-pakistan/) | 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](https://codeatoms.ai/plant-reliability-assessment-saudi-arabia/) | 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](https://codeatoms.ai/ot-security-cip-evidence-us/) | 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](https://codeatoms.ai/structure-phase-construction-saudi-arabia/) | 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](https://codeatoms.ai/steel-production-count-pakistan/) | 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](https://codeatoms.ai/port-digital-twin-us/) | 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](https://codeatoms.ai/factory-fire-monitoring-saudi-arabia/) | 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](https://codeatoms.ai/wildfire-risk-distribution-us/) | 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](https://codeatoms.ai/truck-turn-container-terminal-us/) | 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](https://codeatoms.ai/sovereign-hse-pakistan/) | 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 |