Running a Local LLM on Your Own Server: Ollama, Open Models and Enterprise Privacy

Quick answer: Running a large language model (LLM) on your own server was once only for big companies, but now even a mid sized organization can do it. Three things are needed. First, hardware, a GPU and enough memory to run a powerful model. Second, a runtime tool, the most common is Ollama, which makes downloading and running a model locally very easy. Third, an open weight model, models like Llama, DeepSeek, Mistral and Qwen that can be downloaded for free and run locally. Once the model is installed, all queries and data stay on your server, no internet is needed and you pay no API fees. By adding a RAG that works with your own documents, you can build a fully private AI assistant based on your organization's knowledge.

We covered why local AI is critical for privacy and KVKK in cyber security with local offline AI and the risk of employees leaking data to cloud AI in Shadow AI. This article addresses the practical side, how to run a local LLM in your organization.

1. Hardware, what you need

The speed and size of a local model depend on the hardware. The most critical component is the GPU's memory (VRAM), because the model must fit into it.

  • Small models (about 7 to 8 billion parameters). Run comfortably on a mid range GPU, enough for most daily tasks.
  • Medium models (about 13 to 34 billion). Need a stronger GPU or multiple GPUs, give higher quality results.
  • Large models (70 billion and above). Require serious hardware or quantization.

Quantization is a technique that reduces the model's memory need by storing it at lower precision, fitting it into a smaller GPU with a small loss in quality. This lets larger models run on more modest hardware.

2. Runtime tool, Ollama

Running a model from scratch used to be a technical job, but tools like Ollama have greatly simplified it. Ollama lets you download and run a model locally with a single command, and offers an API so your own applications can connect to this local model. There are alternative tools too, but the logic is the same, host the model locally and access it locally.

3. Choosing an open weight model

There are many powerful models you can run locally, downloadable for free. The choice depends on the task and hardware.

Model family Where it stands out
Llama General purpose, broad ecosystem
DeepSeek Coding and reasoning
Mistral Efficient, strong on small hardware
Qwen Multilingual, wide size range

These models are open weight, meaning you can download and run them on your own hardware, and your usage is not reported outside. DSET's KAOS engine also uses this approach, running with a local model in sovereign mode so data does not leave, as we explained in KAOS AI cyber security scanning tool.

4. RAG with your own documents

A local model alone gives general knowledge, but the real power emerges when you feed it with your organization's own knowledge. RAG (retrieval augmented generation) makes the model first find and use relevant information from your documents when answering a question. Your documents are stored in a vector database, when a question comes the model retrieves the relevant pieces and bases the answer on them. So you get a fully local assistant that knows your organization's policies, product knowledge or technical documentation. We covered the security of RAG and the vector database in RAG and vector database security.

5. Securing the local model

A local model preserves privacy but is itself a system and needs security. Limit access to the model's API by authorization, allow access only from the internal network. Inspect the inputs to the model, because a local model is also open to prompt injection, which we covered in LLM prompt injection and jailbreak defense. And when you connect the model to an application, make sure that application is secure too. Being local does not mean being secure, security is separate work.

Local or cloud, when which

A local LLM is not always the best option, the decision depends on the nature of the work. For general, non secret and variable loads the cloud can be flexible. But if the data is sensitive (customer data, source code, security, forensics), if there is a constant and predictable load, or if you want to avoid API dependency, a local LLM is the right choice. Many organizations use both together, sensitive work locally, general work in the cloud.

Step by step deployment flow

Making a local LLM ready for enterprise use consists of a few logical steps.

  1. Prepare the hardware. Set up a server with the GPU and memory the model requires. Keep the operating system and GPU drivers up to date.
  2. Install the runtime tool. Install a tool like Ollama to prepare the layer that will host the model.
  3. Download the model. Download an open weight model suitable for your task, at an appropriate quantization level. Starting small is good for getting to know the hardware.
  4. Set up and restrict access. Make the model's API accessible only from the internal network, do not expose it outside. Add authentication.
  5. Connect the application layer. Set up an interface for employees to use or an integration into existing tools. A simple chat interface is enough for most needs.
  6. Add RAG. Load your enterprise documents into a vector database so the model answers based on them.
  7. Security and monitoring. Inspect inputs, keep access logs and update the model regularly.

This flow is the same for a single user and for a team, the difference is in scale and hardware.

Quantization levels and the right choice

Quantization reduces a model's memory need by storing its weights with fewer bits. This is the key to fitting a larger model onto a smaller GPU, but brings a small trade off in quality.

Level Memory need Quality When
High precision Highest Best Plenty of VRAM, quality priority
Medium quantization Medium Very good Balance, ideal for most organizations
Aggressive quantization Lowest Acceptable Limited hardware, speed priority

In practice, for most organizations a medium quantization offers the best balance between quality and hardware. The model does most daily tasks at nearly full quality while fitting onto a modest GPU. For work where quality is critical, higher precision is chosen, and for work where speed is the priority, more aggressive quantization.

Performance, concurrency and scaling

Setting up a local model for a single user is easy, but if a team will use it at the same time, performance planning is needed. A few concepts are important. Throughput is how many tokens per second the model produces and depends on the hardware. Concurrency is how many requests can be served at the same time. If there are many users, either a stronger GPU, or multiple GPUs, or a layer that queues requests is needed.

There are two ways to scale. Vertical scaling is setting up a single stronger server. Horizontal scaling is distributing the load across multiple servers. For small and medium organizations a single strong server is usually enough, at large usage load balancing comes in. DSET's KAOS engine uses this kind of scaling for its parallel expert agents.

Cost analysis, local or cloud

Whether a local LLM is economical depends on usage intensity. The cloud works on a pay as you go model, cheap at low usage but quickly expensive at heavy and constant usage. A local solution requires an upfront hardware investment, but afterward there is no per use fee.

The rough logic is this, if your usage is low and variable the cloud can be flexible. But if your usage is high, constant and predictable, a local solution becomes more economical past a certain point in terms of total cost of ownership. When you add the value of privacy and independence that cannot be measured in money, for organizations processing sensitive data a local solution is often the right decision. We covered the sovereignty and privacy value of local AI in cyber security with local offline AI.

Context window and working with long documents

The amount of text a language model can consider at once is called the context window. This determines how much information the model can process in one go. A small context window is enough for short questions but cannot process a long document in its entirety.

The way to work with long documents is the chunking technique together with RAG. The document is split into meaningful pieces, each piece is stored in the vector database, and when a question comes the model retrieves only the relevant pieces. So even a huge collection of documents that does not fit in the context window can be used effectively by the model. We covered the security of this approach in RAG and vector database security.

Fine tuning or RAG, which and when

There are two basic ways to make a local model specific to your organization, and they are often confused.

RAG (retrieval augmented generation) gives the model information from outside. Your documents are kept in a database, when a question comes the model retrieves them and bases the answer on them. If the information changes often, must stay current or you want to cite sources, RAG is the right choice. Updating the information is just updating the database.

Fine tuning retrains the model itself. If you want to give the model a certain style, format or expertise, fine tuning is suitable. But when the information changes the model must be retrained, which is more costly than RAG.

In practice the right start for most organizations is RAG, because it is flexible, updatable and cheaper. Fine tuning is added when a special style or format is needed. The two can also be used together.

Common setup mistakes, observability and maintenance

Mistakes often made when setting up a local AI are easily prevented if known in advance.

  • Leaving the model exposed. If the local model's API is accessible from the internet, the privacy advantage is lost and an attack surface appears. Access should be only from the internal network.
  • Skipping input inspection. A local model is also open to prompt injection. Inspecting inputs is essential, which we covered in LLM prompt injection and jailbreak defense.
  • Sizing the hardware wrong. Choosing the model without fitting it to the hardware means either not working or working very slowly. Quantization and the right model size solve this.
  • Neglecting maintenance. A local AI is not set up and forgotten. The model must be kept up to date, performance monitored and access logs audited.

Observability is an inseparable part of a good local setup. Monitoring the model's performance, errors and usage preserves both security and quality. At DSET we build local AI infrastructure end to end, together with these security and maintenance layers.

Example setup scenarios

The right setup varies by the organization's size and purpose. Three typical scenarios make the decision concrete.

  • Small team. A team of a few people runs a small or medium model on a single mid range GPU server for daily tasks (summarizing, code help, text). Setup is simple, cost is low and data does not leave.
  • Mid sized organization. For dozens of users a stronger server or multiple GPUs are needed. A queuing layer to manage concurrent requests and RAG for access to enterprise knowledge are added.
  • Security lab. The most sensitive use. The model runs in an air gapped network, there is no internet at all. DSET's KAOS engine is designed for this scenario, running fully local in sovereign mode.

In every scenario the principle is the same, using the power of AI without taking the data outside. The difference is in scale and hardware.

Local AI setup checklist

Before completing a local LLM setup, verify these items.

  • Does the hardware have the GPU and memory to run the chosen model (including quantization)?
  • Is the model API accessible only from the internal network, closed to the outside?
  • Are authentication and access authorization set up?
  • Is there input inspection (prompt injection defense)?
  • Are RAG and the vector database for enterprise knowledge set up securely?
  • Are access logs kept and monitored?
  • Is a model update and maintenance process defined?
  • Is it offered together with an AI usage policy?

A setup that completes this list is private, secure and sustainable. At DSET we set up all of these steps together with security layers.

Frequently Asked Questions

Does a local LLM absolutely need very expensive hardware? No. Small and medium models run on a mid range GPU and cover most daily enterprise tasks. With quantization even larger models can fit on modest hardware.

Is Ollama paid? Ollama and the open models you run are free to download. The cost is hardware and electricity, there is no per use API fee.

Is a local model as good as the cloud? The largest cloud models may be ahead, but open models are more than enough for many tasks, and when sensitive data is involved privacy comes before power.

How do I run it with my own documents? With RAG. Your documents are loaded into a vector database, when a question comes the model retrieves the relevant pieces and bases the answer on them, and all of this happens locally.

Sources

To build a local LLM, RAG and secure AI infrastructure for your organization, contact DSET.