Run an LLM on Your Own Infrastructure

What is a large language model?

A large language model (LLM) is a neural network trained on large volumes of text to understand and generate human language. At inference time, a user submits a prompt, the model processes it, and a response is returned. The model itself does not change after training. Only the prompt and the response travel between the user and the system.

That last sentence is where data residency becomes relevant. Every prompt is potentially sensitive. In a bank, it might contain a client name or an account number. In a hospital, it might contain a diagnosis. In a law firm, it can contain privileged instruction. The question of where the model runs is therefore the question of where that data goes.

Why most LLM deployments send data outside your organisation

Public LLM APIs work by routing prompts to a vendor's inference infrastructure. The prompt leaves your network, is processed on hardware you do not control, and a response is returned. The vendor's data processing agreement governs what happens to that data in transit and at rest.

For organisations in regulated sectors, this creates an evidence problem rather than a contract problem. The GDPR does not prohibit using a processor. It requires you to know what the processor does, to assess the transfer where processing reaches a third country, and to be able to demonstrate that assessment on request. Where the provider or its parent company sits under a foreign government access regime, that assessment has to account for access your contract cannot prevent. Sector supervisors and internal information security policies then layer their own control and documentation duties on top.

The result, in many organisations, is a de facto ban on LLM use for any task involving real data, while individual employees route sensitive prompts through public APIs anyway. This is shadow AI: uncontrolled, unaudited, and materially out of compliance.

What it means to run an LLM on your own infrastructure

Running an LLM on your own infrastructure means the model executes on hardware inside your network boundary. The prompt never leaves. The response is generated locally. No data is transmitted to a third party.

This is technically feasible today because capable open-weight models are available for on-premise deployment. The hardware requirement depends on the model and the inference workload, but organisations that already run their own server infrastructure are well within the range where private inference is operationally practical.

The model itself is one component. Around it, an organisation needs:

• Deployment: a validated, reproducible way to install and update the model and the inference layer on their own servers

• Governance: access control that determines which users, systems and applications can submit prompts, and under what policy

• Audit: a record of every inference, sufficient to demonstrate to regulators that the system has been used within its authorised scope

These three requirements, not the model, are what a private AI infrastructure platform addresses.

How Xinity approaches private LLM deployment

Xinity is the layer around the model: deploy, govern and audit. Organisations use Xinity to run validated open models on hardware they control, inside their own network, with access control, policy enforcement and a full audit trail of every inference.

The platform is delivered as software for on-premise and private-cloud environments. Data never leaves your infrastructure. There is no dependency on a public cloud provider.

This approach is described in Xinity's positioning as sovereign by architecture, not by contract. A contract with a cloud vendor constrains what the vendor does with your data. An architecture where the model runs on your own servers eliminates the question entirely.

Who this is relevant for

Private LLM deployment is a live operational requirement for organisations where control over data processing has to be demonstrated, not asserted:

• Financial services: supervised institutions carry documented control duties over the processing of client data, including the ability to show a supervisor where processing happens and who can reach it. Concentration in a small number of external providers has itself become a supervisory concern.

• Healthcare: patient data is special category data. An AI system that processes it inherits the same access control, logging and confidentiality duties as the clinical systems around it, and usually the same internal approval path.

• Public sector: in Austria and across the DACH region, sensitive administration workloads are in practice kept on state-operated or otherwise controlled infrastructure rather than routed through third-party public cloud. Procurement reflects that long before any legal question is reached.

• Legal: legal professional privilege and confidentiality obligations apply to the data a law firm would put into an LLM query.

For organisations at this level of digital maturity, the relevant question is not whether to use LLMs, but how to deploy them in a way that is auditable, controllable and legally defensible.

Getting started

Xinity offers a 30-day pilot programme for organisations ready to evaluate private inference on their own infrastructure. The pilot is designed for organisations that already have an AI team and their own server environment, and want to move from evaluation to a governed production deployment.

To learn more, visit xinity.ai or contact us directly.