Implementing an on-premise LLM: a step-by-step guide

Implementing an on-premise LLM means running a language model on your own hardware or in your own private cloud, without sending your data to an external API from OpenAI or Google. For companies that work with sensitive customer, patient or business data, that's often the difference between being allowed to use AI or not. This step-by-step guide walks you from use case to operations, so you know what's involved before you start.

The approach is deliberately practical. You don't need to build a data centre to get going, but you do need to make the right choices around model, hardware and integration. This guide expands on our broader pillar about local AI for business and focuses specifically on implementation.

Step 1: Pick the use case (and start small)

Don't start with the model, start with the problem. An on-premise LLM is a means, not an end. Pick one concrete task where the model adds value and where you hit a wall today with public AI services. Think of an internal knowledge assistant that answers questions based on your own documentation, a tool that summarises contracts, or a classifier that sorts incoming email.

Choose a use case that meets three conditions. First, there's a clear reason to run locally: sensitive data, compliance requirements, or simply not being allowed to send data to third parties. Second, the task is defined tightly enough to measure whether it works. Third, there's enough volume to justify the investment. An assistant that's used three times a week doesn't justify its own GPU server.

Write down what "good enough" means before you build anything. For a summarisation task that might be: 90 percent of summaries are usable without correction. A benchmark like that stops you from endlessly fine-tuning a model that already does the job.

Step 2: Which open-source model and what size do you choose?

For an on-premise LLM you'll almost always pick an open-source model, because you need to be able to run the weights on your own hardware. The common families in 2026 are Llama (Meta), Mistral, Qwen (Alibaba) and Gemma (Google). They're all free to download, differ in licence and strengths, and come in several sizes. Our guide on open-source AI models for SMBs compares these families in more depth.

The main dial you turn is size, expressed in the number of parameters (billions, marked as B). A 7B or 8B model runs on a single consumer GPU or even a modern CPU, and is often fine for classification, summarisation and simple question answering. Models of 30B to 70B deliver noticeably better reasoning, but demand serious GPU capacity. Don't reach for the biggest model by default: a smaller model that fits your task is cheaper, faster and easier to operate.

Pay attention to quantisation too. By compressing the weights to, say, 4-bit (formats like GGUF or AWQ) a model fits in far less memory, with limited quality loss. That lets you run a model that needs 40 GB on paper on hardware with 12 to 16 GB of video memory. For most SMBs, a quantised model of 7B to 14B is the best starting point.

Step 3: Provision hardware or private cloud

Now that you roughly know which model you want to run, you can decide on hardware. There are three realistic options. The first is your own hardware on site: a server or workstation with one or more GPUs under your own control. This gives you the most control and keeps all data within your own walls, but it takes an upfront investment and someone to manage it.

The second option is a private cloud: you rent a dedicated GPU server from a European provider, running only your workload. Your data stays within the EU and doesn't go to a shared AI service, while you outsource the purchase and physical maintenance. For many companies this is the pragmatic middle ground.

The third option is a hybrid setup: light models locally, heavier tasks on a private GPU in the cloud. The GPU's memory (VRAM) is the deciding factor: it needs to be larger than the model plus some room for the context. Budget 8 to 12 GB for a quantised 7B model, and 40 GB or more for a 70B model.

Step 4: How do you set up the inference software?

The inference software is the layer that loads the model and routes requests to it. You don't write this yourself; there are mature tools that do exactly this. The choice depends on your scale.

For getting started, prototypes and small teams, Ollama is the easiest path. You install it, pull a model with a single command and get a local API right away. It runs fine on a single machine and hides a lot of complexity. llama.cpp sits close to it and excels at running on CPU or modest hardware, with strong support for quantised GGUF models.

Once you need multiple concurrent users or high throughput, vLLM is the standard. It's built for production and handles many requests at once far more efficiently, thanks to techniques that use GPU memory cleverly. A common pattern is: start with Ollama to prove the use case works, then migrate to vLLM when it goes to production. All three offer an API that resembles OpenAI's, so you can reuse existing code with minimal changes.

Step 5: Connect the model to your systems and data

A bare model doesn't know your company. It knows nothing about your products, your customers or your internal procedures. The most effective way to fix that without retraining the model is RAG (retrieval-augmented generation): for every question, you fetch the relevant pieces from your own documents and pass them to the model as context. That way it answers based on your current information instead of only its training data. We explain this fully in our guide on RAG for your business.

In practice you build a pipeline for this: your documents are split into chunks, converted to embeddings and stored in a vector database. When a question comes in, the system finds the most relevant chunks and sends them along with the question to your on-premise LLM. The big advantage in a local setup is that both the documents and the questions never leave your infrastructure.

Alongside RAG, you connect the model to the systems where the work happens: your CRM, your ticketing system, an internal chat interface or an email processor. Keep this integration modular, so you can swap the underlying model later without rebuilding the rest. This custom connective work is exactly what we build through local AI solutions.

Step 6: Operations, monitoring and access

An on-premise LLM isn't a project that's finished at launch; it's a system you operate. Four things should be in order from day one.

First, updates: new model versions appear regularly and are often better. Plan how you test a new model against your "good enough" benchmark before you put it into production. Second, monitoring: track how fast the model responds, how much it's used and whether quality stays stable. That way you spot problems before users complain.

Third, access control: define who's allowed to query the model and the connected data, and use authentication on the API. A local model isn't automatically secure if anyone on the network can reach it. Fourth, logging: keep a record of who asked what and when, so you can investigate incidents and meet compliance requirements. This operational layer ties closely to broader AI data security in your business, especially when you work with sensitive data.

Checklist and indicative timeline

The table below sums up the six steps with an indicative effort. The figures are directional: a tightly scoped use case on modest hardware sits at the low end, a production setup with multiple integrations at the high end.

Would you rather not figure it all out yourself? We help SMBs from use case to a working, managed setup, with the right model choice and hardware for your situation. That way you start with AI without handing over your data.