Hosting a private LLM: what it takes and what it protects — iWV Sovereign AI Stack

Hosting a private LLM: what it takes and what it protects

Q: Can a private LLM use our internal documents?

Yes — that is the point of the retrieval layer. It connects the model securely to your document set so answers are grounded in your own knowledge base, without that content leaving the environment.

Q: How is a private LLM different from just using a public chatbot?

A public chatbot sends your prompts and documents to a shared platform. A private LLM runs inside an environment you control, so the business knowledge that makes it useful never becomes an exposure.

Private LLM7 min readiWV Editorial

Private LLMs give organisations a way to use large language models without sending sensitive business information through a shared public AI platform. However, a private LLM is only as private as the environment supporting it. The real question is what the setup protects, where data is processed and who has access.

Key takeaways

Private LLM infrastructure goes beyond the model. A production environment requires GPU compute, private infrastructure, retrieval capabilities and ongoing operations..
Operations determine long term reliability. The infrastructure needs to be monitored, secured and maintained as workloads and usage evolve.
Privacy depends on the full environment. Prompts, outputs, reference data and fine tuned parameters can remain within your organisation's control when the architecture is designed accordingly.

The real value of an LLM comes from its ability to work with business specific information such as internal policies, product documents, customer data and organisational knowledge. With a public AI service, connecting these sources may require data to leave your controlled environment. A private LLM brings the AI workload into an environment where your organisation can define how data is stored, processed and accessed.

Figure 1: The four layers of a production private LLM

GPU computeInference and optional fine-tuning

Private environmentSecure networking, isolated storage

Retrieval layerGrounds answers in your documents

Managed operationsMonitoring, patching, support

Reference & training dataINSIDE YOUR BOUNDARY

Fine-tuned parametersYOURS AND PRIVATE

Prompts & outputsNOT LOGGED EXTERNALLY

Business intelligenceNEVER LEAVES

The model comes to your data, rather than your data being sent to the model.

Figure 2: Private LLM vs public LLM API

	Public LLM API	Private LLM (iWV)
Where data goes	Sent to the provider	Stays in your environment
Fine-tuning	Parameters live on a shared platform	Parameters remain private to you
Prompt & output logs	Retained under provider policy	Under your control
Cost model	Per token, scales with usage	Predictable monthly subscription
Model choice	Limited to provider's catalogue	Deploy the models that fit your case

The trade-offs that decide whether a use case can go to production.

What It Takes to Run a Private LLM

A private LLM is not simply a model running on a server. A production environment requires four key layers:

GPU Compute infrastructure for inference and, where required, fine tuning. Capacity should be sized around user demand, model requirements and response time targets.
A private environment with secure networking, isolated storage and controlled access to protect the model and its data.
A retrieval layer that securely connects the LLM to internal documents and knowledge sources, allowing responses to be grounded in business specific information.
Monitoring and operations to maintain performance, security and availability through ongoing patching, monitoring and maintenance

Most teams underestimate the operational layer. Running a model for a week is easy. Running it reliably for a year, under load, is the hard part.

What a Private LLM Keeps Within Your Environment

A properly designed private LLM helps keep the business information that makes AI useful within your organisation's control:

Training and reference data stays within the private environment.
Model parameters including those shaped through fine tuning remain private and under your control.
Prompts and outputs are processed within your environment rather than being shared with a public AI platform.
Business knowledge and intelligence remain within your defined security and data boundaries.

Common Private LLM Use Cases

Organisations often use private LLMs when AI needs access to sensitive, confidential or proprietary business information:

Internal knowledge assistants that help employees find answers across company policies, documents and internal knowledge.
Document automation for tasks such as summarising, classifying, extracting information and drafting content.
Customer facing applications that use proprietary business information to provide more relevant and context aware responses.
Internal data analysis for information that organisations cannot or should not submit to public AI platforms.

Focus on AI Not Infrastructure

Many private LLM projects become infrastructure projects. A managed approach removes that complexity by providing the compute, private environment and ongoing operations needed to run the AI workload. Your team can focus on applying AI to business use cases rather than managing the underlying infrastructure. This is the approach behind the iWV Sovereign AI Stack, providing private LLM hosting with the infrastructure and operations managed for you.

Frequently asked questions

Which models can we run on a private LLM?

You are not restricted to one provider's catalogue. The environment supports deploying and running models chosen for your specific use case, security requirements and performance needs.

How much GPU do we need?

It depends on the number of concurrent users, model size and response-time targets. The environment is right-sized to your workload rather than sold as a fixed block.

Can a private LLM use our internal documents?

Yes. Private LLMs can be connected to internal documents through a retrieval layer. This allows the model to reference approved business information when generating responses without requiring that content to be sent to a public AI platform.

Do we need an AI team to run it?

No. iWV manages the infrastructure and operational layers, including setup, monitoring and support, so your team can focus on the use case rather than the stack.

How is a private LLM different from just using a public chatbot?

A private LLM gives businesses greater control over how sensitive data is processed. Instead of sending prompts and internal documents to a public AI service, the model can run within a controlled environment where data access, processing and security can be managed according to the organisation's requirements.

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Hosting a private LLM: what it takes and what it protects