AI Full Stack: Complete Private AI Infrastructure on Hetzner GPUs
AI Full Stack is a complete private AI infrastructure — inference, fine-tuning, and RAG — on dedicated GPUs in European datacenters. For $4,000/mo, you get QLoRA training pipelines, Qdrant vector search, and vLLM serving on RTX PRO 6000 Blackwell GPUs (96 GB VRAM). Train-by-Night, Serve-by-Day. For AI startups, enterprises, and GDPR-sensitive companies that need full model control without cloud vendor lock-in.
Why run your complete AI stack on dedicated hardware?
The Limitations
- Can’t fine-tune on sensitive data
- RAG is slow/expensive
- Vendor lock-in
- Black-box models
The Freedom
- Fine-tune on YOUR data
- Self-hosted RAG (Qdrant)
- Train-by-Night, Serve-by-Day
- Full Model Control
What Does the AI Full Stack Include?
QLoRA pipeline for fine-tuning up to 120B parameters. Train adapters overnight on 96 GB VRAM.
Qdrant Vector DB + Embedding Models + Reranker. All included.
GEX131 Servers with RTX PRO 6000 Blackwell (96 GB VRAM). Scale with add-on GPU nodes.
What Are the Boundaries of the Service?
To keep this service affordable and sustainable, we adhere to strict boundaries. We run the platform; you run the code.
Our Responsibility (Infrastructure)
- Pipeline Uptime: We ensure the training jobs run.
- Vector DB: We manage Qdrant availability.
- Hardware: We manage the GPUs and drivers.
- Multi-Model Serving: We configure vLLM for multiple models.
- Scenario: ‚Training job failed to start‘ -> We fix it.
Your Responsibility (Application)
- Training Data: You provide the dataset.
- Data Quality: You clean the data.
- Evaluation: You check if the model is good.
- Model Selection: You choose which models to deploy.
- Scenario: ‚Model accuracy is low‘ -> You fix it.
How Much Does AI Full Stack Cost?
Plus $5,000 Setup Fee
- 1x GEX131 Node (RTX PRO 6000 Blackwell, 96 GB VRAM).
- Fine-Tuning Pipeline (QLoRA up to 120B parameters).
- RAG Stack (Qdrant + embeddings + reranker).
- Multi-Model Serving via vLLM.
- Model Registry with LoRA adapter management.
- Self-Service Training Trigger.
Need more capacity? Add GEX131 nodes at +$1,000/mo each.
Have questions about the AI Full Stack service?
How does fine-tuning work?
You upload a dataset, trigger a job via API, and we give you an Adapter ID.
What is Train-by-Night?
We schedule training jobs during low-traffic hours to utilize your idle GPUs.
How fast is the vector database?
We use Qdrant on NVMe drives. It handles millions of vectors with sub-millisecond retrieval.
Do you provide the training dataset?
No. You bring your data. We provide the factory to process it.
How do I deploy the fine-tuned model?
One click. The pipeline pushes the adapter to your Model Registry, ready for inference.
Can I run multiple models simultaneously?
Yes. vLLM supports multi-model serving. You can run embedding, reasoning, and reranker models on the same GPU with intelligent memory management.
Can I use custom models?
Yes, you can pull any model from HuggingFace.
Do I own the weights?
Yes. 100%. You can download them anytime.
Can I fine-tune Llama 3 70B?
Yes. We use QLoRA and 4-bit quantization to fit 70B training on our GEX131 nodes. Models up to 120B parameters are supported.
Is the training environment persistent?
Yes. You have a persistent workspace (Jupyter/VSCode) attached to the GPU.
What about data privacy during training?
Your data stays on the dedicated server. We scrub the storage after you delete the instance.
Can I scale to multiple GPU nodes?
Yes. Add GEX131 nodes at +$1,000/mo each for additional capacity, parallel training, or high-availability setups.
Curious about your potential savings?
Most teams save 40–60% on cloud compute. Use our free calculator to see exactly how much you could save.
What other AI infrastructure products do we offer?
discovery Zoom. We'll review your current cloud spend, identify what's safe to move, and give you an honest Go / No-Go recommendation — no commitment, no sales pitch. If the numbers work, we'll show you how. If they don't, we'll tell you that too.
Interested? Contact us.
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