Local LLMs: Use AI Without Giving Up Control of Your Data

Local LLM hosting means running open-source language models on your own hardware or in a European cloud instead of sending data to a US-based API. Sensitive business data stays in your own data center, significantly reducing legal complexity. b.telligent advises you on model selection, hardware sizing, and implementation.

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Why Local LLM Hosting Belongs on Your Agenda Now

The legal basis for data transfers to the United States has become less certain. On June 29, 2026, the US Supreme Court ruled in Trump v. Slaughter that the president may dismiss members of the Federal Trade Commission without cause. FTC oversight is a key pillar of the EU-U.S. Data Privacy Framework. The adequacy decision remains in force until the European Commission withdraws it or the Court of Justice of the European Union overturns it. In July 2026, the European Data Protection Board called on the Commission to review the matter, while noyb (European Center for Digital Rights) announced plans to file a lawsuit.

If you build AI applications on US-based APIs, you are building on a foundation that could change within months. After Safe Harbor and Privacy Shield, this would be the third disruption in ten years. Local LLM hosting removes this issue from your architecture because your data never leaves the company.

What You Should Clarify Now

  • Which of our AI applications process data that must not leave the company?
  • What transfer mechanism do our current AI services rely on, and do we have a fallback option?
  • Which applications are currently blocked solely by the sensitivity classification of their data?
  • What availability do we need to commit to for our business teams, and what SLA does our provider offer?

Where AI Initiatives Built on Cloud APIs Stall Today

These four blockers come up most often in client conversations:

A Lid on Your Data

‍AI is only allowed near non-critical data. The applications with the greatest leverage never get off the ground, because their data carries a higher classification.

Transfers to the US

‍Legal and regulatory risks when transferring data to US providers. The assessment has to be revisited again and again.

Copy-Paste Instead of Process

‍Reluctance to integrate deeply leads to manual workarounds. The benefit stays with individuals instead of taking effect in the process.

Availability Without Leverage

‍Interfaces aren't always stable, and you have no lever to pull. It's hard to build your own SLA on that.

Cloud API, EU Cloud, or Your Own Hardware: Which Operating Model Is Right for You?

Local does not necessarily mean owning physical hardware. What matters is who controls the environment. A cloud API can be ready in days, but processes data on third-party infrastructure. A self-managed virtual machine (VM) in a European cloud avoids data transfers to the United States without requiring hardware procurement. Your own servers give you full control over access, processing location, and availability.

Comparison: Cloud API (US provider), LLM in an EU cloud (dedicated VM), and on-premises hardware
Criteria Cloud API (US Provider) LLM in an EU Cloud (Dedicated VM) On-Premises Hardware
Processing Location Provider infrastructure, often in the US EU data center, e.g., OVHcloud, Hetzner, IONOS Your own data center
Transfer Mechanism Required Yes (DPF or Standard Contractual Clauses) No, if the provider and location are both in the EU No
Control Over Access Contractual, with limited ability to verify Technical, through your dedicated VM Complete
Availability and SLA Provider SLA Can be defined internally Can be defined internally
Model Stability Provider determines versions You decide when to switch You decide
Model Selection Provider portfolio All open-weight models All open-weight models
Cost Model Per token, usage-based Fixed monthly instance costs Capital investment plus electricity and operations
Time to Value Days Weeks Weeks to months
Operational Effort Low Medium Higher, but within your own operating model
Best fit if... ...your data is non-sensitive and you want to test quickly. ...you want to get started quickly and your workload profile is still unclear. ...you have strict requirements, consistently high workloads, or available data center capacity.
Local hosting does not replace cloud APIs in every scenario. In most cases, a deliberate split based on data sensitivity and process criticality makes sense. A gateway can route requests according to rules you define.
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Sovereign and Independent of US Providers

Digital sovereignty means your company decides where data is processed, which software is used, and under what conditions. Running large language models in a European cloud or on your own servers puts that decision back in your hands.

LLMs are becoming a critical business resource. What is critical should run reliably and consistently. Open-weight models give you that option: the weights are under your control, and the model continues to run even if a provider changes its pricing, terms, or availability.

What You Become Independent From

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    Regulatory developments: If the legal basis for data transfers to the United States changes, it has no impact on a local architecture.

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    Geopolitics: Export controls, sanctions, and political shifts do not interfere with your operations.

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    Pricing in an oligopolistic market: A small number of providers determine the market for commercial models. Your infrastructure costs are not tied to their pricing.

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    Other companies’ product decisions: Model versions will not be discontinued or changed in their behavior without your involvement.

The Decisions You Need to Make When Getting Started

Running LLMs locally raises four interconnected questions.
If you answer them separately, you will often procure the wrong hardware for the wrong model.

Which Models, and How Many?

The right selection depends on the task, foreign-language quality, context length, and licensing. For RAG applications, smaller models are often sufficient, supplemented by an embedding model and a reranking model.

What Hardware Supports Your Workload?

GPU memory, memory bandwidth, and the number of concurrent users are key. Drivers, the operating system, and serving parameters must match the hardware; otherwise, performance is left on the table.

How Does Your Data Get to the Model?

This includes the RAG architecture, vector database, permissions, and knowledge base updates. Permissions must be enforced during retrieval—not only in the user interface.

How Does This Become a Running Service?

Deployment through an inference server such as vLLM, monitoring latency and utilization, model updates, rollback procedures, and an SLA you can actually meet.

Local LLM: Your Fast Start

In a no-obligation conversation, we'll work out together which use cases are worth running locally, which operating model fits your IT, and which hardware will carry your requirements.

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Our Approach: Reproducible Metrics Instead of Anecdotal Estimates

There is plenty of information online about the hardware requirements and performance of local models, but much of it cannot be reproduced in practice. That is why we run our own tests: which models run on which hardware, the throughput they achieve, how many people can use them concurrently, and the monthly electricity or cloud costs involved.

To do this, we built an internal benchmarking framework. It automatically identifies working deployment configurations and tests around 20 models on defined hardware over the course of a weekend. We update our measurements whenever new model releases or cloud hardware become available. We make our methodology transparent so you can understand and validate our figures.

Quality benchmarks alone do not answer these questions. They provide no insight into throughput, user capacity, or operating costs on specific hardware.

Assessment

‍We assess your use cases, categorize them by data sensitivity and business value, and review your existing infrastructure. The result: a prioritized roadmap and an operating model recommendation.

Architecture and Sizing

Model selection, sizing based on measured performance, RAG architecture, and an authorization concept. If requested, we test your candidate models on your target hardware. The result: reliable figures for procurement and your business case.

Implementation and Operations

‍We build the platform, connect the first application, establish monitoring, and hand over operations to your team. The result: a locally hosted LLM with defined availability and a documented operating model.

  • Data and AI from a single team: Your data platform, RAG architecture, and model operations are delivered by the same team.

  • Our own measurement data: We size your environment based on our own measurements, not vendor specifications.

  • Experience in regulated environments: Projects in healthcare, financial services, and the public sector.

Running LLMs Locally: Frequently Asked Questions

What does "hosting LLMs locally" mean?

Hosting LLMs locally means running a large language model with openly available weights on infrastructure you control: your own servers in your data center, or a virtual machine in a European cloud. Requests and responses never leave that environment. Access runs through your own API, usually OpenAI-compatible, so existing applications can continue to be used.

Why local LLMs instead of cloud APIs?

Three motives dominate our client conversations. Compliance, data protection, and transparency form the first cluster. The second is the desire for solutions that can be operated reliably, with an SLA built around your own requirements. The third is better cost predictability and budgeting.

Which data leaves the company with local hosting?

With a properly configured local setup, none: no prompts, no documents, no responses. The first step usually focuses on data that isn't permitted to leave the company at all, for compliance and confidentiality reasons. Reliability and predictability then often prove convincing enough that processes with a lower classification level move to the local solution as well.

What hardware do I need for a local LLM?

That comes down to three factors: model size, number of concurrent users, and the response speed you require. The deciding factor is available GPU memory, not raw compute power. For RAG applications with smaller models, individual GPUs are often enough; for productive multi-user operation, a GPU server with an inference server such as vLLM becomes relevant. We measure which combination achieves your response times before you procure anything.

Are local models as good as ChatGPT, Claude, or Gemini?

At the very top end of performance, the large commercial models are ahead. For most business tasks, that isn't the deciding factor. Summarizing, classifying, extracting, and answering questions against your own knowledge base are all handled reliably by open models of the right size. We test suitability against your real tasks, not against public benchmarks.

Do RAG and local LLMs work well together?

Yes, exceptionally well. A model only knows what it learned in training. Retrieval augmented generation (RAG) supplies the model with the relevant excerpts from your documents and databases at runtime. Local hosting and RAG are a natural fit, because both building blocks run inside your own network and the data never goes anywhere.

Is local hosting suitable for regulated industries such as healthcare, banking, or the public sector?

Yes, and that's where the leverage is greatest. Running locally reduces the procedural effort involved in compliance, cuts dependencies, and makes processes more robust. That applies everywhere, but especially in heavily regulated environments where processing sensitive data would otherwise remain a showstopper.

Bring Your AI Into Your Own Data Center

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Let us know which use cases you are considering and what data is involved. We will get back to you with an initial assessment of whether local deployment makes sense and which operating model is the right fit.

Dr. Michael Allgöwer

Management Consultant

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