Bare Metal GPU Hosting
GPU Server Hosting for AI Training, LLM Inference & Machine Learning
Dedicated bare metal GPU servers in Frankfurt and Strasbourg — no shared resources, no setup fees, no annual contracts. Full root access and predictable performance for every AI workload.
Built for AI Workloads
NVIDIA H200 GPU Servers: Train, Fine-Tune, and Serve at Full Speed
141 GB of HBM3e VRAM and 4.8 TB/s of memory bandwidth per GPU, with NVLink for up to 282 GB combined VRAM across dual-GPU configurations. Fine-tune 70B-parameter models, run high-throughput LLM inference, or train from scratch on hardware dedicated exclusively to your workload: no cloud quotas, no noisy neighbors, no per-hour billing.
Why teams switch from cloud GPUs to bare metal
Dedicated GPU Hosting vs. Cloud: What Actually Differs
The Cloud GPU Problem
Cloud GPU instances (A100, H100 on AWS/GCP/Azure) share physical hardware between customers. When another tenant runs a memory-intensive job, your VRAM headroom shrinks. Latency spikes. Throughput drops.
On a dedicated bare metal server, the GPU is physically yours. No hypervisor. No VRAM sharing. No performance variability between runs — which matters for reproducible training results and consistent inference SLAs.
What is included in every server:
- ✓100% dedicated GPU — no sharing
- ✓GDPR-compliant EU data centers
- ✓99.9% uptime SLA
- ✓4.2 Tbit/s network backbone
- ✓Free hardware replacement
- ✓No egress fees
- ✓30-min support response
What velia.net provides
Trusted by 5,000+ companies, velia.net operates 6 global data centers, with GPU infrastructure available in Frankfurt (Germany) and Strasbourg (France). Every server includes a 99.9% uptime SLA, free hardware replacement, and 24/7/365 support with a guaranteed 30-minute response.
Test a configuration on a 1-month term, or lock in 3, 6, or 12 months for long-term planning and predictable budgeting, selectable directly at checkout. No forced long-term contracts. No egress fees. No minimum spend. You pay for the server — nothing else.
Common AI Workloads on Dedicated GPU Servers
LLM Training & Fine-Tuning
Fine-tune Llama 3, Mistral 7B, Falcon, or custom foundation models on proprietary datasets. The H200's 141 GB HBM3e VRAM fits 70B parameter models without model parallelism across multiple GPUs.
LLM Inference & API Serving
Run vLLM, llama.cpp, or Triton Inference Server on dedicated hardware. Consistent latency is guaranteed — no performance drops when other tenants load jobs on the same node, as happens with cloud GPU sharing.
Retrieval-Augmented Generation (RAG)
Run a vector database (Qdrant, Weaviate, Milvus), embedding model, and LLM on one dedicated node. Eliminates the inter-service network latency of distributed cloud RAG architectures.
Computer Vision — Training & Inference
Train YOLO, Detectron2, or custom object detection models on image datasets. High VRAM and fast NVMe storage handle large batch sizes without CPU-GPU data transfer bottlenecks.
AI Research & Prototyping
Monthly billing and full root access mean you can run experiments with any framework, any CUDA version, and any model without cloud vendor restrictions or long approval cycles.
MLOps Pipelines & Model Deployment
Run Kubeflow, MLflow, or custom CI/CD pipelines on dedicated hardware. Pre-configure with PyTorch, TensorFlow, CUDA, and container runtimes. No shared cluster quotas limiting your team.
Why AI Teams Choose velia.net
Lower cost than cloud GPU instances
For workloads running 8+ hours per day, a dedicated GPU server costs less than an equivalent cloud instance. Cloud providers charge per-hour rates that add up quickly — plus separate egress, storage, and support fees. With velia.net, you pay one flat monthly rate.
Training data stays in your jurisdiction
Both server locations — Frankfurt and Strasbourg — are in the EU. GDPR-compliant infrastructure. Your training data, model weights, and inference outputs never leave infrastructure you control.
Reproducible training results
On shared cloud GPU instances, other tenants affect your VRAM headroom and memory bandwidth. On a dedicated server, every training run has the same hardware state. Useful when you need to compare experiments or reproduce published results.
Monthly contracts — cancel or change anytime
No annual commitment required. Add a second GPU server for a training sprint, then remove it the next month. You are billed per calendar month with no penalties for changes.
Run any framework or model without restrictions
Root access plus IPMI/KVM remote control. Install any CUDA version, any inference framework (vLLM, Triton, Ollama, llama.cpp), any container runtime. No managed platform that restricts which packages or models you can deploy.
24/7/365 support, not ticket queues
Support response is guaranteed within 30 minutes, 24/7/365. You reach real infrastructure specialists directly — not a remote first-level team reading a script. Hardware replacement is free and handled at no extra cost.
No setup delay
GPU Servers Configured for AI Workloads
Each GPU server can be delivered with PyTorch, TensorFlow, the latest NVIDIA CUDA toolkit, and container runtime pre-installed — so your team can start running training jobs without spending time on OS and driver configuration.
velia.net manages the physical hardware, the data center infrastructure, and the network. You manage the server — with full root access and no restrictions on what you install or run.
Looking for a Different GPU or Server Configuration?
Browse the full GPU server lineup, or talk to our team for advice on the right setup for your AI workload.