On-premises LLMs
On-premises LLM deployment
For organisations that cannot send prompts and documents to a hosted AI provider, Integrated AIS deploys self-hosted large language models that run entirely inside infrastructure you control. The model comes to your data, so your data never has to leave your environment — and you get the capability of a modern LLM without the exposure of sending your most sensitive text to someone else's servers.
What on-premises LLM deployment means
An on-premises LLM deployment runs a large language model on hardware inside your own network — your data centre or a private cloud tenancy you control — instead of calling a hosted model API across the public internet. Every prompt, every retrieved document and every generated answer stays within your boundary. The difference from a public AI service is not the quality of the model but the custody of the data flowing through it: nothing is transmitted to, logged by, or retained by a third-party provider.
This has become practical because open-weight models now match hosted services closely enough for most enterprise workloads, and because the tooling to serve, monitor and update them in production has matured. A self-hosted LLM is no longer a research project — it is a deployable capability, provided it is architected for the reliability and maintenance burden a production system actually carries.
Who needs a self-hosted LLM
The common thread is data that cannot leave the building. Financial services firms handling client and transaction data, public sector and defence bodies working with protectively marked information, healthcare organisations bound by patient confidentiality, and professional firms holding privileged material all reach the same wall: the capability is compelling, but the data governance around a public AI API is impossible to satisfy.
A self-hosted LLM resolves that tension. It brings the model inside the same controls that already govern your most sensitive systems, so adopting AI stops being a data-exfiltration risk and becomes a normal extension of your existing architecture — something your security and compliance functions can sign off on rather than block.
How we deliver it
We start by right-sizing the deployment to your actual workload — which model, what hardware, how many concurrent users, what latency — so you neither under-serve your users nor over-spend on infrastructure you will not use. We then architect the serving stack, retrieval and access controls inside your environment, install and harden it with every dependency accounted for, and stand up the monitoring and update process that keeps a self-hosted model reliable and current in production.
This sits inside our broadersecure deployment capabilityand oursecurity posture. Where your requirement is stricter than on-premises, the same deployment can be taken to a fullyair-gapped architecture; where the constraint is legal residency rather than isolation, ourdata sovereignty approach may be the better fit. Establishing which you actually need is the first thing we do.
Common questions
What does it mean to run an LLM on-premises?
An on-premises LLM runs on hardware you own or control — in your own data centre or a private cloud tenancy — rather than being called as a hosted API over the public internet. Prompts, context and outputs stay inside your network boundary, so sensitive data is never sent to a third-party model provider. You keep the same class of large language model; what changes is where the inference happens and who has custody of the data around it.
Which models can you deploy on-premises?
We are model-neutral. Open-weight model families can be deployed and run entirely within your infrastructure, and we select the one that fits your accuracy, latency, hardware and licensing constraints rather than steering you toward a model we happen to sell. Where a workload genuinely needs a frontier hosted model, we say so — but the point of an on-premises deployment is that the capable open-weight options now cover the large majority of enterprise workloads without data ever leaving your walls.
What hardware does a self-hosted LLM need?
It depends on the model size and how many concurrent users you need to serve. Many production workloads run comfortably on a single well-specified GPU server; larger models or higher throughput call for a small cluster. Part of our assessment is right-sizing the hardware to your actual usage so you are not over-provisioning, and designing the deployment to scale as demand grows rather than rebuilding it later.
Is a self-hosted LLM secure enough for regulated data?
That is precisely the use case it exists for. Because the model runs inside your security architecture, the same access controls, network segmentation, logging and accreditation that govern your other systems extend to the AI layer. For the strictest requirements the deployment can be fully air-gapped with no external network path at all. We design the deployment to pass your security and compliance sign-off rather than to work around it.
Read further
In-depth writing on self-hosted and on-premises AI:
Talk to us about a self-hosted LLM
If you need the capability of a modern large language model without sending your data to a third party, we can help you work out which architecture your constraint requires — and build it.
Get in touch