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

Thomas from mechanical engineering knows the dilemma: his project managers could create quotes and write requirement specifications much faster with AI support. But transferring sensitive customer data to external AI providers is unthinkable for him.

The solution is called self-hosted Large Language Models (LLMs). These allow companies to leverage the benefits of generative AI without losing control over their data.

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

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

But what does such an implementation really cost? What hardware do you need? And how complicated is the actual rollout?

This guide provides you with concrete answers—without marketing promises, but with realistic figures and proven recommendations from practice.

What are self-hosted LLMs?

Self-hosted LLMs are AI language models that you run entirely on your own IT infrastructure. Unlike cloud services like ChatGPT or Claude, these models run locally—meaning no data leaves your company.

The term «Large Language Model» refers to AI systems trained with billions of parameters to understand and generate human-like text. Well-known open-source representatives include Meta’s Llama family, Mistral AI’s models, or Microsoft’s Phi series.

Advantages over cloud LLMs

The main advantage is obvious: complete data control. Your trade secrets, customer data, or development projects never leave your IT environment.

There are also significant savings in the long term on API fees from cloud providers. If you make intensive use of current models, costs can quickly reach four-figure sums per month.

Another plus: you are not dependent on the availability of external services. Outages at major international providers no longer affect you directly.

Setting realistic expectations

But let’s be honest: currently, self-hosted LLMs do not achieve the performance of the latest cloud models. GPT-4o or Claude 3.5 Sonnet still outperform them in complex reasoning tasks.

For many business applications, however, the quality of open-source models is more than sufficient. Document summarization, email drafts, or FAQ responses work excellently with Llama 3.1 8B or Mistral 7B.

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

Hardware requirements and costs

Hardware requirements largely depend on the size of the chosen model. As a rule of thumb: for every billion parameters, you will need about 2 GB of GPU memory at 16-bit precision.

GPU requirements by model size

For most medium-sized company use cases, models between 3B and 8B parameters are sufficient. These run smoothly on a single gaming GPU or workstation graphics card.

Other hardware components

Besides the GPU, you will need sufficient RAM. Plan for at least 32 GB, preferably 64 GB. While the model itself runs on the GPU, application logic and data processing require system RAM.

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

The CPU is secondary, as long as it is modern. A current Intel Core i5 or AMD Ryzen 5 is quite sufficient.

Cloud vs. on-premises cost comparison

A cloud GPU instance with an NVIDIA A100 costs around 3–4 US dollars per hour at many providers. At 8 hours daily use, monthly costs amount to $480–640.

A comparable on-premises solution pays for itself after just 6–12 months. Plus, you can also use the hardware for other purposes.

For smaller companies, a dedicated server is often the more economical solution. A well-equipped system for €5,000–8,000 covers 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 other providers have also developed strong alternatives.

Recommended open-source models

Llama 3.2 3B: Perfect for simple tasks like text summarization or drafting emails. Runs smoothly on consumer hardware and stands out for its efficiency.

Mistral 7B: The all-rounder for medium-sized companies. Excellent command of German and solid performance in most business applications.

Llama 3.1 8B: Currently the best compromise between performance and resource requirements. Particularly strong with structured tasks and coding.

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

For specialized use cases, there are fine-tuned variants. Code Llama is excellent for programming tasks, while Llama-2-Chat particularly excels in dialogs.

Deployment tools and frameworks

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

vLLM offers higher performance for productive environments. The focus is on optimal GPU utilization and parallel request processing.

Text Generation Inference (TGI) from Hugging Face shines with advanced features like token streaming and dynamic batching.

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

License models and legal aspects

Many open-source LLMs are under permissive licenses. Llama 3.1, for example, uses the «Llama 3 Community License,» which expressly allows commercial use.

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

Nonetheless, you should review the license terms. Some models have usage restrictions or require attribution.

An often-overlooked point: even with open-source models, patents may apply. A legal review before putting them into production is recommended.

Practical implementation steps

Successful LLM implementation follows a structured approach. Don’t jump in at the deep end—a well-thought-out pilot approach saves time and avoids costly mistakes.

Step 1: Define use case and select model

Start with a concrete use case. What tasks should the LLM handle? Document creation, responding to customer inquiries, or code generation?

Define success metrics. How quickly should a response be generated? What quality do you expect? A 3B parameter model answers in fractions 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 with Ollama.

Step 2: Hardware setup and installation

Procure hardware according to your model choice. For starters, a single server with a powerful GPU is often sufficient.

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

Set up Docker for reproducible deployments. Many LLM tools come with pre-built container images.

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

Step 3: Start a pilot project

Begin with a manageable use case. Email drafts or document summarization are good starting points.

Develop your first prompts and test them extensively. A good prompt is like an exact requirements specification—the more precise the instructions, the better the results.

Gather feedback from future users. What works well? Where are improvements needed? These insights feed into optimization.

Document all configurations and learnings. This makes later expansions much easier.

Step 4: Integration and scaling

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

Implement monitoring and logging. What requests are being made? How long do responses take? These data will help with optimization.

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

Prepare for scaling scenarios. Load balancers can distribute requests to multiple instances as usage increases.

Step 5: Production-ready deployment

Implement high availability with multiple instances. If one server fails, others 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 in handling LLM infrastructure. Emergency plans and runbooks make maintenance easier.

Security and compliance

Self-hosted LLMs offer inherent security advantages but still require thoughtful safeguards. The fact that data does not leave your company is only the first step.

GDPR compliance and data protection

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

Implement deletion concepts for training data and conversation histories. Even if the model runs locally, you must still be able to fulfill the right to be forgotten.

Document all data processing steps. What data flows into the model? How long are logs stored? You’ll need this information for GDPR evidence.

Review the training data of the open-source models you use. Might they possibly contain your own company data from public sources?

Network security and access control

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

Implement strong authentication for all access. API keys should be rotated regularly; user accounts configured according to least privilege principles.

Use TLS encryption for all connections—even internally. Transmitting sensitive prompts and responses unencrypted poses a security risk.

Monitor all system access. SIEM tools can automatically detect suspicious activities and send alerts.

Data governance and audit trails

Classify data by confidentiality level. Not all information needs the same protection—but you need to know what is processed where.

Log all LLM interactions. Who entered which questions and when? This information is valuable 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 testing uncovers vulnerabilities that internal teams may overlook.

Business case and ROI

Investment in self-hosted LLMs often pays off faster than expected. But how do you determine concrete return on investment for your business?

Cost savings vs. cloud APIs

Using current cloud LLM offerings can quickly lead to monthly costs in the mid to high triple digits per team, depending on usage.

A self-hosted solution with Llama 3.1 8B costs around €8,000 up front. Ongoing costs are limited to electricity (about €50–100 monthly) and maintenance.

The break-even point is thus at 12–18 months, depending on how intensively you use the system.

Making productivity increases measurable

More difficult to quantify, but often more significant, are productivity gains. If your project managers need 30% less time to create quotes, what is that worth?

A project manager with a yearly salary of €80,000 who spends 10 hours per week on documentation costs you about €20,000 a year for this activity. A 30% efficiency gain saves €6,000 per year.

Multiply by the number of affected employees. With 10 project managers, you achieve annual savings of €60,000.

There are also soft factors: higher employee satisfaction due to less routine work, faster response times for customer inquiries, and improved documentation quality.

Break-even calculation for your business

Create a simple calculation: add up hardware costs (€8,000–15,000), implementation effort (€5,000–20,000 depending on complexity), and ongoing running costs (€1,000–2,000 annually).

Subtract the saved cloud API costs and quantified productivity gains. Most medium-sized companies reach amortization within 18–36 months.

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

Challenges and solution approaches

Self-hosted LLMs are not automatic. However, common stumbling blocks can be avoided with the right preparation.

Maintenance and updates

The biggest issue: new model versions are released regularly. Especially Meta and Mistral AI publish upgrades quickly.

The solution lies in automated update processes. Container-based deployments allow fast rollbacks if new versions cause issues.

Schedule maintenance windows for major updates. Changing models from 8B to 70B parameters may require new hardware.

Performance optimization

Optimizing GPU utilization is an art in itself. Quantization can reduce memory usage by 50–75%, with only minimal quality loss.

4-bit quantization with tools like bitsandbytes allows running larger models on smaller hardware. Llama 3.1 70B runs quantized on sufficiently powerful hardware.

Batch processing for handling multiple requests at once greatly increases throughput. Modern inference engines like vLLM can optimize this automatically.

Scaling as usage grows

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

Kubernetes is excellent for automatic scaling. As load increases, new containers start; as it drops, resources are released.

Hybrid approaches cleverly combine local and cloud LLMs. Standard requests are handled by the internal system, while complex tasks are sent to cloud APIs.

Conclusion and recommendations for action

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

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

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

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

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

Need support with implementation? Brixon AI supports medium-sized companies from the first workshop through to production-ready implementation—always focused on measurable business benefits.

Frequently asked questions