How companies use GPU and LLM servers for real business applications
Leading companies worldwide trust WZ-IT
AI servers with powerful GPUs open up completely new possibilities for companies. But what are the specific use cases? And what technical requirements do they bring?
On this page we show you real application scenarios in which companies are already successfully using AI servers. With detailed information on technical requirements, ROI examples and specific implementation details.
Whether you want to automate customer processes, optimize quality control, or build internal knowledge management systems - the following use cases will show you the way.
24/7 customer support & internal knowledge bases
Chatbots based on large language models (LLMs) are revolutionizing customer service. In contrast to rule-based systems, modern LLM chatbots understand context, can process complex requests and communicate naturally.
AI chatbots use Large Language Models (LLMs) like Llama, Gemma, or DeepSeek to conduct human-like conversations. They can be trained on company data and access current information via RAG (Retrieval-Augmented Generation).
Typical use cases are customer support, internal IT helpdesks, HR assistants, and sales consulting. The bots can independently resolve 80-90% of standard queries.
60-80% lower support costs through automation of standard queries
24/7 support without additional staff or shift work
Uniform, high-quality answers without quality fluctuations
Handling thousands of parallel requests without performance loss
We offer both frameworks for different requirements
Ideal for: Development, prototyping, simple setups with few concurrent users. Very user-friendly and quick to set up.
Ideal for: Production environments with high throughput, many concurrent users, low latency. Up to 24x higher throughput than Ollama for large models.
Recommendation: Start with Ollama for proof-of-concept, switch to vLLM for production deployments with >50 concurrent users.
Building a customer support chatbot for an online shop
Model: Llama 3.1 8B (fast, efficient, good tool usage)
RAG System: ChromaDB with product catalog, FAQ, return policies
Interface: OpenWebUI with custom branding
Integration: REST API for website, CRM connection
Result: 85% Automatic Resolution Rate, 24/7 availability, ROI after 6 months
Automated quality control, security monitoring & more
Computer vision applications require intensive GPU calculations for real-time image analysis. From quality control in production to intelligent video surveillance - the use cases are diverse.
Computer vision uses deep learning models to analyze images and videos. Modern models can recognize objects, detect anomalies, track movements, and determine quality metrics.
Typical applications: production defect detection, security & access controls, medical image analysis, retail analytics (customer behavior), and logistics automation.
99%+ detection rate for quality defects, better than human inspectors
Real-time analysis at 60+ FPS, no delays in production flow
90% fewer manual inspections, ROI within 12-18 months
24/7 operation without fatigue or quality loss
Automatic defect detection in production line
Model: YOLOv8 custom-trained for specific product defects
Hardware: 4x cameras (4K), RTX 6000 Blackwell Max-Q, real-time processing
Pipeline: Frame Capture → GPU Inference → Defect Classification → Alert
Integration: SCADA system, automatic rejection of defective parts
Result: 99.2% detection rate, 0% false negatives, 15% waste reduction
Intelligent document search & knowledge management with LLMs
RAG (Retrieval Augmented Generation) combines the power of LLMs with the company's own data sources. This enables employees to access the entire company knowledge in natural language.
RAG extends LLMs with the ability to access external knowledge databases. Documents are converted into vector embeddings and stored in a vector database. On queries, relevant context is retrieved and provided to the LLM.
Use cases: enterprise search, compliance & legal research, engineering documentation, onboarding assistants, and research & development.
80% faster information retrieval compared to manual search
Democratization of expert knowledge, faster onboarding of new employees
Data stays in your own data center, GDPR compliant
Always access to latest documents and policies
Intelligent search in technical documents and manuals
Document base: 50,000+ PDFs, technical specs, CAD descriptions
Embedding model: all-MiniLM-L6-v2 for fast vectorization
LLM: Llama 3.1 70B for complex technical queries
Vector DB: Weaviate with hybrid search (dense + sparse)
Result: 85% fewer support tickets, 4h/week time savings per engineer
Which server solution fits which use case? An overview of requirements.
| Use Case | Recommended GPU | VRAM Requirement | WZ-IT Server | From Price/Month |
|---|---|---|---|---|
| Chatbot (up to 13B model) | RTX 4000 Ada | 20 GB | AI Server Basic | On Request |
| Computer Vision (real-time) | RTX 6000 Blackwell Max-Q | 96 GB | AI Server Pro | on request |
| RAG System (large models) | RTX 6000 Blackwell Max-Q | 96 GB | AI Server Pro | on request |
| Multi-model deployment | 2x RTX 6000 Blackwell Max-Q | 192 GB | Custom Setup | on request |
Start with the smallest suitable configuration
Monitor GPU utilization and inference latency
Upgrade to more powerful GPUs at >80% constant utilization
Multi-GPU setups for higher throughput or larger models
Managed Service: We support you with proper sizing
AI servers are an investment. But how quickly does it pay off? Here are some realistic examples.
Investment: €500/month server + €10,000 setup
Savings: €4,000/month (2 FTE support staff)
Break-even: 3 months
12-month ROI: 380%
Investment: €1,400/month server + €25,000 setup & training
Savings: €8,000/month (3 FTE QC staff) + 15% less waste
Break-even: 6 months
12-month ROI: 340%
Investment: €500/month server + €15,000 setup
Savings: €3,000/month (time savings 50 employees × 4h/month)
Break-even: 6 months
12-month ROI: 230%
Reduction of error costs and rework
Faster time-to-market through better knowledge management
Higher customer satisfaction through 24/7 support
Competitive advantages through AI-powered processes
Scalability without proportional cost increase
The possibilities of AI servers go far beyond the three main use cases:
Private code completion and review with models like DeepSeek Coder or CodeLlama
Automatic analysis of customer feedback, reviews, and social media posts
Prediction of machine failures based on sensor data
Automated creation of product descriptions, blog posts, and social media content
Real-time detection of suspicious transactions and behavior
Supporting doctors in diagnosis through image and data analysis
It depends on your business processes. Do you have recurring support requests? Then a chatbot is ideal. Do you perform visual quality controls? Computer vision can help. Do employees have trouble finding information? A RAG system is the solution. Contact us for a free analysis.
It varies by use case. A simple chatbot can go live in 2-4 weeks. Computer vision projects need 6-12 weeks for training and integration. RAG systems are ready in 4-8 weeks, depending on document volume.
Yes, with sufficient GPU capacity. An AI Server Pro (RTX 6000 Blackwell Max-Q) can host multiple smaller models in parallel or use one large model for different tasks. We help with optimal sizing.
It depends on your use case. Ollama is perfect for development, prototyping, and smaller deployments (up to ~50 concurrent users). It's very user-friendly and quick to set up. vLLM is ideal for production environments with high performance requirements: it offers up to 24x higher throughput than Ollama, lower latency, and better GPU utilization. Our recommendation: Start with Ollama for proof-of-concept, migrate to vLLM when going to production with high traffic expectations.
All data remains on your dedicated server in Germany. No transfer to third parties, no cloud APIs. Full GDPR compliance and data sovereignty.
Not necessarily. Our managed service includes setup, training, and maintenance of models. You only need someone to oversee the technical integration. For advanced customizations, we offer training.
Let's discuss your specific use case
Every company has unique requirements. In a free consultation, we analyze your use case, recommend the right infrastructure, and show you realistic ROI scenarios.
Free use case analysis
Individual server recommendation
ROI calculation for your project
Technical feasibility check
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Whether a specific IT challenge or just an idea - we look forward to the exchange. In a brief conversation, we'll evaluate together if and how your project fits with WZ-IT.
Leading companies trust WZ-IT
Timo Wevelsiep & Robin Zins
Managing Directors of WZ-IT
