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Sovereign AI Stacks: Deploying Air-Gapped LLMs for Government, Banking and Telecom
Paperback
Publisher: Independently Published
Published: Oct 20 2026
Pages: 366
Weight: 1.40
Height: 0.76 Width: 7.00 Depth: 10.00
Language: English
Make it work like ChatGPT, but nothing leaves the building.
That is the brief from every ministry, central bank and telecom operator whose regulator has said the data stays in the country. Most AI tutorials assume the internet: public registries, model hubs, SaaS dashboards and someone else's API. None of that works in a data centre with no route out.
Sovereign AI Stacks is the reference design that does. It takes you from an empty rack to a multi-tenant LLM platform that runs on hardware you own, with no connection to any vendor, and gives you the evidence an auditor or procurement panel will ask for.
What you will build- A threat model and the technical controls behind Tanzania's, Kenya's, the UAE's, Saudi Arabia's and the EU's data-protection rules
- GPU sizing you can defend line by line, from model memory to data-centre power
- A signed, verified supply chain that carries container images and 300 GB model weights across the air gap
- An offline Kubernetes 1.37 cluster on Talos Linux, with Cilium default-deny networking and GPU nodes
- vLLM serving open-weight models behind one metered, multi-tenant AI gateway
- Keycloak identity, Kyverno admission policy and tenant-isolated RAG over sensitive documents
- Self-hosted metrics, logs and traces, a redacted audit trail and runtime security
- Acceptance tests in Swahili, Arabic and English that block a model upgrade when one language regresses
- Backups, disaster-recovery drills, zero-downtime upgrades and a handover checklist
Every chapter opens with a production failure: the chat UI that phoned home, the 300 GB transfer that arrived corrupted, the cache that served a confidential answer to the wrong user. Each one ends with the fix and a hands-on lab. All the code is in a public companion repository, linted and tested, with every version pinned.
Who should buy this book- Infrastructure and platform architects at ministries, regulators, banks and telecom operators
- Systems integrators bidding on on-premises AI tenders
- Senior DevOps engineers and SREs handed the mandate above and a rack of GPUs
You should be comfortable with Linux, Kubernetes and YAML. No machine-learning background is required.
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