The demand for GPU computing power for artificial intelligence is growing rapidly. Whether training custom models, fine-tuning foundation models, or running inference in production — powerful GPU servers have become a critical infrastructure component. INGATE offers two paths: dedicated bare metal GPU servers and flexible cloud GPU instances with virtual GPUs.
Bare Metal GPU Servers: Full Control Over the Hardware
For workloads that require maximum and consistent GPU performance, our bare metal GPU servers provide the best solution. You get exclusive access to the physical hardware — no shared resources, no noisy neighbors.
NVIDIA RTX 4000 SFF Ada (20 GB GDDR6)
This compact and energy-efficient workstation GPU excels at inference, rendering, and lighter ML workloads. Up to three GPUs can be configured in a single server — an attractive starting point for organizations looking to run their first AI projects on dedicated hardware.
NVIDIA RTX PRO 6000 Blackwell (96 GB GDDR7)
The latest Blackwell generation with 96 GB of GPU memory is designed for demanding LLM training and multi-GPU setups. Up to four GPUs per server enable training large models without relying on cloud instances.
Dell PowerEdge R7725 (H100, L40s, RTX 6000 Ada, L4 Ada, A2)
Our enterprise chassis for maximum flexibility: choose from five GPU models to match your workload. From the NVIDIA H100 SXM5 with 80 GB HBM3 for large-scale model training to the cost-efficient L4 Ada or A2 for production inference. Up to 2× H100 or 6× L4 Ada per server are available.
Cloud GPU: Flexible vGPU Instances
Not every workload needs a dedicated server. With INGATE Cloud GPU, you book virtual GPU instances (vGPU) with dedicated resources and VRAM — granularly configurable and without long-term hardware commitments.
Available GPU Classes
- Tesla T4 (16 GB GDDR6): Cost-efficient entry-level GPU for inference, VDI, and light ML workloads
- A10 (24 GB GDDR6): All-rounder for ML training, 3D rendering, and mixed workloads
- A100 (80 GB HBM2e): Multi-Instance GPU (MIG) for demanding AI workloads and LLM training
- H200 (141 GB HBM3e): Maximum performance for LLM training and large foundation models
Each GPU can be divided into different vGPU profiles — from small slices for inference to the full GPU for training. You pay only for the performance you actually need.
Why INGATE Instead of Hyperscalers?
GPU instances at major cloud providers are notoriously expensive and often unavailable. INGATE offers tangible advantages:
- Guaranteed Availability: No spot instance interruptions, no waiting lists
- Predictable Costs: Fixed monthly prices instead of hourly billing, no hidden egress fees
- Full Control: Root access on bare metal, custom software stacks, no restrictions
- Data Sovereignty: Training with sensitive data in German data centers, without the US Cloud Act — owner-operated GmbH
- Personal Support: Direct contacts instead of ticket queues, free 24x7 emergency hotline
Typical Cost Savings
A comparison using an 8x H100 server as an example:
- AWS p5.48xlarge: approximately 25,000 EUR per month (On-Demand)
- INGATE GPU Server: significantly more affordable — contact us for an individual quote
With continuous use, dedicated GPU hardware pays for itself compared to cloud instances within a few months. And with Cloud GPU, monthly billing and no hidden egress fees mean no nasty surprises on your invoice.
Use Cases
- Private LLM Inference: Run open-source models like Llama, Mistral, or DeepSeek on your own hardware — or as a vGPU in the cloud
- RAG Pipelines: Embedding generation and Retrieval-Augmented Generation with full data control
- Model Fine-Tuning: Fine-tuning foundation models with your proprietary data on H100 or A100
- Computer Vision: Image analysis, object detection, and video processing in production
- Hybrid AI Pipelines: Combine bare metal GPU servers with cloud vGPUs via Direct Connect for maximum flexibility
Getting Started
Whether you need a dedicated GPU server or flexible cloud vGPUs — contact our team at info@ingate.de for personalized advice. We analyze your workload and recommend the optimal configuration: from a single vGPU for initial experiments to a multi-GPU cluster for production model training.
