The demand for GPU computing power for AI is growing rapidly. Whether training custom models, fine-tuning foundation models, or running inference in production — dedicated GPU servers have become critical
infrastructure.
Bare Metal vs. Cloud vGPU
There are two approaches, each with distinct advantages:
Bare Metal GPU Servers give you exclusive access to physical hardware — no shared resources, no noisy neighbors. Ideal for consistent, high-performance workloads like LLM training and fine-tuning.
Cloud vGPU Instances offer flexible virtual GPU resources with dedicated VRAM — granularly configurable without long-term hardware commitment. Perfect for inference, rendering, and variable workloads.
The Hidden Cost of Hyperscalers
GPU instances at major cloud providers are notoriously expensive. A quick comparison:
- AWS p5.48xlarge (8x H100): ~€25,000/month on-demand
- Dedicated bare metal GPU server: significantly less with fixed monthly pricing
The difference adds up fast. With dedicated hardware, there are no surprise egress fees, no spot instance interruptions, and no waiting lists.
Why Data Sovereignty Matters for AI
Training AI models often involves sensitive business data. Running these workloads on infrastructure subject to the US Cloud Act creates compliance risks for European companies.
An alternative: GPU servers hosted in German data centers, operated by a German company under EU jurisdiction. No extraterritorial access, full GDPR compliance by design.
Available GPU Tiers
Bare Metal (dedicated hardware):
- NVIDIA RTX 4000 SFF Ada (20 GB GDDR6) — inference & light ML
- NVIDIA RTX PRO 6000 Blackwell (96 GB GDDR7) — LLM training, up to 4 GPUs
- NVIDIA H100 SXM5 (80 GB HBM3) — large-scale training
Cloud vGPU (flexible instances):
- Tesla T4 (16 GB) — cost-efficient inference
- A10 (24 GB) — ML training & rendering
- A100 (80 GB) — LLM training with MIG support
- H200 (141 GB HBM3e) — maximum performance
All hosted in ISO 27001 / DIN EN 50600 certified data centers in Munich, powered by 100% renewable hydroelectric energy.
Getting Started
Whether you need a single vGPU for initial experiments or a multi-GPU cluster for production training — the key is choosing infrastructure that matches your workload, budget, and compliance requirements.
→ Learn more about GPU Server hosting
→ Cloud GPU instances with vGPU
→ Compare configurations
Top comments (0)