Sovereign AI FAQ: private LLM deployment, data control and vLLM

Procurement and architecture answers on sovereign AI: what the term means in practice, whether a system can run without public model APIs, why self-hosting is not the same as sovereignty, which infrastructure and GPUs are needed, what quantisation changes, and how retrieval and data-protection duties fit inside a controlled boundary. The answers draw on the serving and compression research that made private deployment practical.

Keywords sovereign AI · private LLM · vLLM · self-hosting · GDPR · quantisation

"Sovereignty" is used loosely. A review of 341 publications found the term applied in divergent ways, each relating to meaningful control, ownership and other claims to data, by agents ranging from individuals to countries^[1]. In this FAQ it means operational control over where prompts, documents, embeddings and weights are processed.

Self-hosting became practical because of serving and compression research. vLLM manages the per-request key-value cache like virtual memory and reports 2–4× higher throughput than earlier systems at the same latency^[2]. Quantization reduces the memory a model needs: AWQ, a 4-bit weight-only method, protects a small fraction of salient weights to limit the error^[3]. There is also a security rationale for controlling what leaves the environment: language models can memorise and reproduce training data, including personal information^[4]. Network design usually starts from the zero-trust architecture defined by NIST^[5].

Sovereign AI means the organisation controls where data is processed, where model weights live, who can administer the infrastructure, how logs are retained and which external services the system depends on. Hosting in an EU region alone does not automatically make a deployment sovereign.

Yes. Open-weight models can be served on infrastructure controlled by the organisation using runtimes such as vLLM. The application can expose an OpenAI-compatible API internally while keeping inference inside the organisation's own network boundary.

No. A self-hosted server can still depend on external telemetry, licence checks, remote administration, external vector databases or uncontrolled support access. Sovereignty is an end-to-end property of the architecture and operating model.

A typical production deployment includes GPU compute, controlled model storage, an inference runtime such as vLLM, an internal gateway, authentication, observability, secrets management, a model-update workflow and backup or failover procedures. Kubernetes is useful when the organisation needs repeatable operations and multiple workloads, but it is not mandatory for every deployment.

The answer depends on the model, quantisation format, context length, concurrency and latency target. A deployment should be sized from a workload trace rather than a single parameter-count rule. A100-class, H100-class and equivalent accelerators are common enterprise options, but smaller models can often run efficiently on less expensive hardware.

Often, yes. Formats such as FP8, INT8 and INT4 can reduce model memory requirements and may improve throughput in suitable workloads. The quantised model should still be evaluated on the organisation's actual tasks because quality and tool-use reliability can change.

Yes. Retrieval-augmented generation can be fully self-hosted. Documents, embeddings, indexes and inference can stay inside the same controlled environment. The main design questions become access control, tenant isolation, document freshness, evaluation and auditability.

Use a default-deny network policy, internal model and container registries, controlled DNS and egress rules, internal observability, and explicit allow-lists for any external dependency. The architecture should be testable with outbound internet access disabled.

No. Infrastructure control helps with data governance, but GDPR obligations also depend on lawful basis, purpose limitation, data minimisation, retention, access rights and processor relationships. The AI platform should make those controls easier to implement and audit.

LMXAI designs the model serving layer, gateway, retrieval, observability and operational controls as one production system. The objective is to keep models replaceable, avoid unnecessary vendor lock-in and hand over an architecture the organisation can operate and audit.

• Sovereign vLLM deployment + vendor checklist
• vLLM GPU sizing for sovereign inference
• vLLM quantization: AWQ, GPTQ, FP8 and INT4
• Production vLLM on Kubernetes
• Sovereign AI workspace vs Copilot
• NL: Soevereine LLM-deployment
• Production RAG and AI agents FAQ