Self-Hosted LLMs: Requirements, Costs, and Implementation Steps – A Practical Guide to Locally Deploying Open-Source LLMs for Business-Critical Applications

Thomas, who works in mechanical engineering, knows the dilemma: his project managers could create proposals and write specifications much faster with AI support. However, sending sensitive customer data to external AI providers is simply out of the question for him.

The answer is self-hosted Large Language Models (LLMs). They enable companies to leverage the benefits of generative AI without losing control over their data.

Self-hosted LLMs run entirely on your own infrastructure—be it on local servers or in your private cloud. This ensures that all processed information stays within the company and remains subject to your own security policies.

For mid-sized companies with 10 to 250 employees, this represents a realistic alternative to cloud-based AI services. Especially in regulated industries or when handling trade secrets, this approach is often the only way to use AI productively.

But how much does such an implementation really cost? What hardware do you need? And how complicated is the process in practice?

This guide provides concrete answers—no marketing promises, just realistic figures and field-proven recommendations.

What are self-hosted LLMs?

Self-hosted LLMs are AI language models that you run entirely on your own IT infrastructure. In contrast to cloud services like ChatGPT or Claude, these models run locally—without any data ever leaving your company.

The term “Large Language Model” describes AI systems trained on billions of parameters to understand and generate human-like text. Well-known open-source examples are Meta’s Llama family, models from Mistral AI, or Microsoft’s Phi series.

Advantages over Cloud LLMs

The key benefit is clear: complete data control. Your trade secrets, customer data, or development projects never leave your IT environment.

In addition, you avoid the often significant API costs associated with cloud providers in the long run. If you make heavy use of current models, this can quickly add up to four-digit monthly sums.

Another plus: you are no longer dependent on the availability of external services. Outages at large international providers no longer impact you directly.

Setting Realistic Expectations

Let’s be honest: currently, self-hosted LLMs do not match the performance of the latest cloud-based models. GPT-4o or Claude 3.5 Sonnet are often superior in complex reasoning tasks.

However, for many business applications, the quality of open-source models is more than enough. Document summarization, email drafts, or answering FAQs work excellently with Llama 3.1 8B or Mistral 7B.

The art lies in striking the right balance between performance, cost, and data protection. Not every task requires the most powerful model available.

Hardware Requirements and Costs

The hardware requirements depend largely on the size of the chosen model. As a rule of thumb: for every billion parameters, you need around 2 GB of GPU memory with 16-bit precision.

GPU Requirements by Model Size

For most mid-sized business applications, models with 3B to 8B parameters are sufficient. These will run smoothly on a single gaming GPU or workstation graphics card.

Other Hardware Components

Besides the GPU, you will need enough RAM. Plan at least 32 GB—64 GB is better. While the model itself runs on the GPU, your application logic and data processing require system RAM.

For storage, use NVMe SSDs. Models with 7–8 billion parameters take up about 4–8 GB of disk space, depending on quantization. Plan for at least 1 TB of SSD storage.

The CPU is less important as long as it is up to date. A modern Intel Core i5 or AMD Ryzen 5 is more than enough.

Cloud vs. On-Premises Cost Comparison

A cloud GPU instance with an NVIDIA A100 costs about $3–4 per hour from most providers. Using it eight hours a day results in monthly costs of $480–640.

A comparable on-premises solution pays for itself within 6–12 months. On top of that, you can use the hardware for other applications as well.

For smaller businesses, a dedicated server is often the more economical choice. A well-equipped system costing €5,000–8,000 will cover most use cases.

Software and Open-Source Models

The range of high-quality open-source LLMs in 2025 is impressive. Meta’s Llama family dominates the market, but Mistral AI, Microsoft, and others have also developed strong alternatives.

Recommended Open-Source Models

Llama 3.2 3B: Perfect for simple tasks like text summarization or email drafts. Runs smoothly on consumer hardware and is highly efficient.

Mistral 7B: The all-rounder for mid-sized businesses. Excellent German language skills and robust performance for most business applications.

Llama 3.1 8B: Currently the best trade-off between performance and resource requirements. Especially strong with structured tasks and programming.

Microsoft Phi-3.5 Mini: Surprisingly powerful despite only 3.8 billion parameters. Specifically optimized for business applications.

For specialized use cases, there are custom-tuned variants. Code Llama is ideal for programming tasks, while Llama-2-Chat is particularly well suited to dialogue.

Deployment Tools and Frameworks

Ollama has established itself as the standard for straightforward LLM deployments. Installing a new model takes just one command: ollama run llama3.1:8b.

vLLM provides greater performance for production environments. Its focus is on optimal GPU utilization and handling multiple requests in parallel.

Text Generation Inference (TGI) from Hugging Face scores with advanced features such as token streaming and dynamic batching.

For companies seeking a full solution, LM Studio is a good fit. The graphical interface makes installation and management much easier.

Licensing Models and Legal Considerations

Many open-source LLMs are released under permissive licenses. Llama 3.1, for example, uses the “Llama 3 Community License”, which explicitly allows commercial use.

Mistral AI publishes its models under the Apache 2.0 license—one of the most business-friendly open-source licenses available.

Nevertheless, you should always check the licensing terms. Some models have usage restrictions or require attribution.

A commonly overlooked aspect: even open-source models can be subject to patents. A legal review before deployment in production is recommended.

Implementation Steps for Practice

A successful LLM implementation follows a structured approach. Don’t just dive in headfirst—a well-thought-out pilot saves time and avoids costly mistakes.

Step 1: Define Use Case and Select Model

Start with a specific application. What tasks should the LLM perform? Document creation, answering customer inquiries, or code generation?

Define success metrics. How quickly should a response be generated? What quality do you expect? A 3B-parameter model replies in a fraction of a second, while a 70B model may take several seconds.

Test different models with your specific queries. Use platforms like Hugging Face or local installations via Ollama.

Step 2: Set Up Hardware and Install

Procure hardware according to the model you select. For the initial phase, a single server with a powerful GPU is often sufficient.

Install a current Linux operating system—Ubuntu 22.04 LTS or Ubuntu 24.04 LTS are tried and tested. Windows also works, but Linux offers better performance and easier driver management.

Set up Docker for reproducible deployments. Many LLM tools provide ready-made container images.

Install NVIDIA CUDA drivers and the container runtime for GPU acceleration. Test the setup with a simple CUDA example.

Step 3: Launch the Pilot Project

Start with a manageable use case. Email drafting or document summarization are ideal entry points.

Develop initial prompts and test them extensively. A good prompt is like a clear specification—the more precise the instructions, the better the results.

Collect feedback from your end users. What works well? Where are improvements needed? Use these insights for further optimization.

Document all configurations and learnings. This greatly eases future expansion.

Step 4: Integration and Scaling

Integrate the LLM into your existing workflows. APIs allow connection to CRM systems, project management tools, or internal applications.

Implement monitoring and logging. What requests are being made? How long do responses take? This data helps with ongoing optimization.

Plan for backup and recovery. Model files and configurations should be backed up regularly.

Prepare for scalability. Load balancers can distribute traffic to multiple instances as demand grows.

Step 5: Production-Ready Deployment

Implement high availability with multiple instances. If one server fails, others will automatically take over.

Set up automated updates. New model versions should be able to be rolled out in a controlled manner.

Establish governance processes. Who is allowed to deploy new models? How are changes documented and approved?

Train your IT team on the LLM infrastructure. Emergency plans and runbooks make maintenance easier.

Security and Compliance

Self-hosted LLMs offer inherent security advantages, but still require well-designed safeguards. The fact that your data never leaves the company is just the first step.

GDPR Compliance and Data Protection

A local LLM processes personal data exclusively on your infrastructure. This greatly reduces compliance risks, but does not eliminate them entirely.

Implement deletion concepts for training data and chat histories. Even when running locally, you must be able to comply with the right to be forgotten.

Document all data processing activities. What data flows into the model? How long are logs stored? You will need this information for GDPR documentation.

Review the training data used by open-source models. Could they possibly include your own company data from public sources?

Network Security and Access Control

Isolate LLM servers within your internal network. Direct internet access is usually unnecessary and only increases your attack surface.

Implement strong authentication for all access. API keys should be rotated regularly, and user accounts configured on a least-privilege basis.

Use TLS encryption for all connections—even internal traffic. Unencrypted transfer of sensitive prompts and responses poses a security risk.

Monitor all system accesses. SIEM tools can automatically detect suspicious behavior and issue alerts.

Data Governance and Audit Trails

Classify data by confidentiality level. Not all information requires the same level of protection—but you need to know what’s being processed where.

Log all LLM interactions. Who asked what, and when? This information is invaluable in case of security incidents.

Implement Data Loss Prevention (DLP). Automated scans can prevent credit card numbers or social security numbers from ending up in prompts.

Plan regular security audits. External penetration tests can uncover vulnerabilities your internal team might overlook.

Business Case and ROI

Investing in self-hosted LLMs often pays off faster than expected. But how do you actually calculate the return on investment for your company?

Cost Savings vs. Cloud APIs

Using current cloud-based LLMs can easily result in monthly costs ranging from several hundred to a thousand euros per team, depending on usage.

A self-hosted solution with Llama 3.1 8B has an up-front cost of about €8,000. Ongoing costs are limited to electricity (about €50–100 per month) and maintenance.

Break-even is typically reached within 12–18 months—depending on how much the system is used.

Measuring Productivity Gains

Productivity gains are harder to quantify but are often even more significant. If your project managers spend 30% less time creating proposals, what’s the value of that?

A project manager earning €80,000 per year and spending 10 hours a week on documentation means about €20,000 spent annually on this activity. Increasing efficiency by 30% saves €6,000 a year.

Multiply that by the number of affected employees. With ten project managers, you’d save €60,000 per year.

There are also intangible benefits: higher employee satisfaction due to less repetitive work, faster response times for customer inquiries, and improved documentation quality.

Break-Even Calculation for Your Company

Make a simple calculation: Add up hardware costs (€8,000–15,000), implementation effort (€5,000–20,000 depending on complexity), and ongoing operating costs (€1,000–2,000 per year).

Subtract the saved cloud API costs and quantified productivity gains. Most mid-sized businesses reach payback within 18–36 months.

Also consider strategic advantages: independence from cloud vendors, complete data control, and the ability to train proprietary models.

Challenges and Solutions

Self-hosted LLMs are not plug-and-play. However, common pitfalls can be avoided with the right preparation.

Maintenance and Updates

The major challenge: new model versions are released regularly. In particular, Meta and Mistral AI roll out upgrades quickly.

The solution is automated update processes. Container-based deployments enable rapid rollbacks if new versions cause trouble.

Schedule maintenance windows for major updates. Switching from an 8B to a 70B-parameter model may require new hardware.

Performance Optimization

Optimizing GPU utilization is an art in itself. Quantization can cut memory requirements by 50–75% with only a slight loss in quality.

Tools like bitsandbytes offer 4-bit quantization, letting you run larger models on smaller hardware. Llama 3.1 70B, for example, runs in quantized form on sufficiently powerful hardware.

Batch processing multiple requests at once greatly increases throughput. Modern inference engines like vLLM handle these optimizations automatically.

Scaling with Growing Usage

What happens when your 50-person company grows to 200 employees? Load balancers distribute requests across multiple LLM instances.

Kubernetes is excellent for automatic scaling. As demand increases, new containers get started; as it drops, resources are released.

Hybrid approaches combine local and cloud-based LLMs intelligently. Standard requests are handled internally, while complex tasks are forwarded to cloud APIs.

Conclusion and Recommendations

In 2025, self-hosted LLMs have become a realistic option for mid-sized businesses. The technology is mature, open-source models offer solid quality, and costs remain manageable.

Start with a specific use case and a small setup. An RTX 4090 for €1,600 is more than enough for initial experiments. Gather experience before investing in larger hardware.

The break-even calculation works for most companies with 20–30 active users. Smaller teams should use cloud APIs at first and switch later.

Don’t forget the organizational side: train your IT team, establish governance, implement security concepts. Technology alone does not make a successful AI strategy.

When is the best time to get started? Now. The learning curve is steep, but those who start today will have a key competitive edge tomorrow.

Need help implementing your project? Brixon AI supports mid-sized companies from the first workshop to production-ready deployment—always with a focus on measurable business value.

Frequently Asked Questions