AI SERVICES

Local AI and On-Premises LLMs

Local AI means that language models run on the company’s own hardware or in a German data center, without queries or documents being sent to an external cloud provider. torck, based in Maxhütte-Haidhof near Regensburg, plans, procures, and operates such systems for industry and commerce, using Open Weight models and its own applications, such as knowledge systems.

When Local AI Makes Sense

On-premises solutions are particularly worthwhile in situations where sensitive data must not leave the premises, such as personnel files, design documents, or customer data. Data sovereignty is a second reason. Those who operate models themselves determine the version, logging, and access. With high, predictable usage volumes, costs can often be easily calculated using in-house hardware. In production and warehousing, offline operation is also crucial, as analysis must continue even if the Internet connection goes down.

SENSITIVE DATA

Employee and customer data remain in-house

Contracts, design documents, and personnel files are often intended to remain within a closed environment. A local model processes them without transferring them to third parties. The section on data protection below discusses the applicable obligations in this regard.

DATA INTEGRITY

Models, Interfaces, Control

Those who operate models themselves specify the version, logging, and access rights. Integration with legacy systems is handled through defined interfaces. Learn more about AI consulting →

FORESEEABLE COSTS

Costs That Can Be Calculated

Hardware, licenses, and operations can be planned over the useful life. Cloud fees, on the other hand, vary depending on usage. Learn more about hardware and purchasing →

OFFLINE OPERATION

Production continues even without the Internet

For machines, plant control systems, and warehouses, data analysis must function even without an Internet connection. A local model at the plant achieves this without relying on external data centers. Learn more about plant monitoring →

When Local AI Isn't the Right Choice

Low usage and non-critical data

For occasional use and data that doesn't require special protection, a cloud solution is often faster to set up and easier to manage. Owning your own hardware is only cost-effective if it's used to full capacity on a regular basis.

When models change quickly

Your own hardware is fixed, while new model generations are released frequently. If you want to try out many models, it’s better to test them in the cloud and run only proven applications locally.

Very large models aren't always practical for local use

Very large models with long response times are costly to run locally when there are only a few queries. For standard tasks, smaller open-weight models are sufficient on existing hardware. You can determine the appropriate size by running a test on your own data.

Which models are suitable?

Open-weight models such as Mistral, Llama, and Qwen can be run on your own hardware. The choice of model depends on the languages, license, and required context length. This article provides an overview of the models and their applications Mistral, Llama, and Qwen: Open-Source Models in Enterprise Use.

Planning Hardware

The selection depends on the model size, the desired response time, and the number of concurrent users. The graphics cards' memory is usually the limiting factor. You can make a preliminary selection in the LLM Finder. This article discusses whether it's better to buy or rent AI Hardware in 2027: Buy, Rent, or Wait?

Operation and Maintenance by torck

For private models within the company, see On-Premise LLM for Business: Setup, GPU Costs, 5-Year TCO. The following shows how knowledge systems integrate corporate knowledge: RAG and Knowledge Systems. The following describes how to get started with generative AI: AI in Business.

torck sets up the systems, secures them, and maintains models and applications. Upon request, we can operate the systems on an ongoing basis with agreed-upon response times. Alternatively, we can hand them over to your IT department, complete with documentation.

Frequently Asked Questions About Local AI

How much does a local AI system cost?

The costs consist of hardware, software, operations, and personnel. We do not provide flat-rate prices because the model size, number of users, and data requirements determine the cost. We first clarify the use case and then prepare a cost estimate based on the service life, which we compare with the cloud costs.

Can I process sensitive data using local AI?

Yes, provided that access, logging, and storage are managed in-house or at a German data center. Which obligations under the GDPR and the AI Regulation (EU) 2024/1689 apply depends on the specific circumstances. This article lists laws and regulations to help contextualize technical decisions. It is not legal advice. Please consult your legal department or a law firm to determine whether and how a regulation applies to your company.

Which model is right for local operations?

Open-weight models such as Mistral, Llama, and Qwen can be run on your own hardware. The choice depends on the intended use, such as the languages, license, and required context length. We test the candidates using your own documents before purchasing hardware.

How long does the implementation take?

That depends on the scope. A pilot project involving a single model and a very limited set of documents can be set up quickly, while a production environment with interfaces requires more lead time. We won’t provide a time estimate until we’ve analyzed the use case.

Data Protection and Obligations for Local Operations

Local operation shifts responsibilities in-house, but they do not disappear. The GDPR requires that processing operations be traceable and documented, regardless of where the model runs. As of February 2, 2025, the EU AI Act requires employees to have sufficient AI expertise (Regulation (EU) 2024/1689, Art. 4). When processing is outsourced to a service provider such as torck, a contract in accordance with Article 28 of the GDPR is required. For information on access risks associated with U.S. providers, see The Cloud Act and the GDPR: Where Your AI Data Should Be Stored. As of October 25, 2026.

This article refers to laws and regulations to put technical decisions in context. It is not legal advice. Whether and how a rule applies to your company is a question for your legal department or a law firm.

CONTACT

Let's talk about your project.

Tell us about your project. We’ll give you an honest assessment of whether on-premises AI is right for your situation and what it will take. The initial consultation is non-binding.