GPU Server

Q: Which GPU is right for my AI project?

That depends on the workload: For inference and rendering, we recommend the RTX 4000 SFF Ada (20 GB GDDR6, compact and energy-efficient). For LLM training and multi-GPU setups, the RTX PRO 6000 Blackwell (96 GB GDDR7) is the better choice. We analyze your workload free of charge.

Q: Are multi-GPU setups possible?

Yes, we implement multi-GPU configurations for demanding AI and ML workloads. The GPUs can also be connected to the INGATE Cloud via Direct Connect for hybrid workloads.

Q: What software is pre-installed?

Upon request, we pre-install CUDA, cuDNN, TensorRT, and container runtimes for GPU workloads. We support all major ML frameworks including PyTorch, TensorFlow, and JAX.

Q: Does my training data stay in Germany?

Yes, your data and models remain on sovereign German infrastructure. As an owner-managed German GmbH, we are not subject to the US Cloud Act.

Q: Which operating systems are supported?

All major distributions: Ubuntu, Debian, CentOS, openSUSE, FreeBSD, and Windows Server. We can also pre-install any operating system of your choice at no extra cost.

Q: Is a different hardware configuration possible?

Yes, we can configure any system to your specifications — including GPU configuration, storage, and networking. Our hardware consulting is included.

Q: What is inference and how does it differ from training?

Inference refers to using an already trained AI model to make predictions or decisions in real time — such as image classification, speech recognition, or chatbots. Training, on the other hand, is the upstream process where the model learns from large datasets and optimizes its parameters. Training requires massive computing power and is typically performed on GPUs like the NVIDIA H100 or A100, which feature Tensor Cores and high VRAM bandwidth (HBM3). Inference can often be run on more efficient GPUs like the NVIDIA L4, T4, or RTX series, as throughput and energy efficiency matter more than raw computing power.

Q: What are the differences between GPUs for rendering and AI workloads?

Rendering GPUs like the NVIDIA RTX PRO 6000 are optimized for visualization, CAD, VFX, and simulation. Their focus is on RT Cores (raytracing) and high VRAM for large scenes. AI GPUs like the NVIDIA H100 or A100 prioritize Tensor Cores for matrix operations and offer higher memory bandwidth through HBM3 technology — critical for training large models. Some GPUs like the NVIDIA L4 or the RTX series can handle both tasks well, making them ideal for companies that need both rendering and inference. INGATE advises you individually on which GPU configuration optimally suits your workload.

Dedicated NVIDIA GPU power for AI, machine learning, and rendering — server location Germany, 100% carbon-neutral electricity.

Compare GPUs

GDPR

compliant

99.99%

Availability

100%

Green Energy

Dell AI Server · Any GPU

Custom GPU Infrastructure

INGATE delivers any Dell PowerEdge AI server — individually configured with any currently available GPU on the market. Whether NVIDIA H100, H200, B200, B300, RTX PRO 6000 Blackwell, or AMD Instinct: We build your system precisely to your requirements.

From a single inference GPU to a multi-node cluster with NVLink — we advise you personally, analyze your workload, and recommend the optimal configuration for maximum performance per euro.

Any Dell PowerEdge AI server configurable

Any GPU: NVIDIA H100/H200/B200/B300, RTX PRO, AMD Instinct and more

CUDA, cuDNN & TensorRT pre-installed on request

Multi-GPU setups with NVLink & Direct Liquid Cooling

Direct Connect to INGATE Cloud

Free workload analysis & hardware consulting

Request a Quote

Dell PowerEdge AI Server — All Models Available

We deliver any Dell PowerEdge AI server individually configured to your specifications. Here is an overview of all available model series:

PowerEdge XE9785

8× GPU · Air-Cooled · HGX B300

PowerEdge XE9785L

8× GPU · Liquid-Cooled · HGX B300

PowerEdge XE9780

8× GPU · Air-Cooled · HGX B300

PowerEdge XE9780L

8× GPU · Liquid-Cooled · HGX B300

PowerEdge XE9712

GB300 NVL72 · Rack-Scale

PowerEdge XE9685L

8× GPU · Liquid-Cooled · AMD EPYC

PowerEdge XE9680

8× GPU · Air-Cooled · HGX B200

PowerEdge XE9680L

8× GPU · Liquid-Cooled · HGX B200

PowerEdge XE8712

Highest GPU density · up to 144 GPUs

PowerEdge XE8640

4× GPU · Air/Liquid Cooling

PowerEdge XE7745

Up to 8× GPU · AMD EPYC · 4U

PowerEdge XE7740

Up to 8× GPU · Intel Xeon™ · 4U

PowerEdge R770

Mainstream AI · RTX PRO 6000

PowerEdge R760xa

Multi-GPU · Versatile

PowerEdge R7725

Dual AMD EPYC · Up to 6× GPU

PowerEdge R6725

AMD EPYC · Compact · 1U

PowerEdge M7725

Modular · IR7000 Rack · Up to 74 Nodes

PowerEdge XR9700

Edge · Ruggedized · Robust

All models individually configurable — with any currently available GPU. Contact us for your custom quote.

Your Advantages at a Glance

GPU infrastructure for AI and ML is complex. At INGATE, we analyze your workload and recommend the optimal GPU configuration. Your data and models remain on sovereign German infrastructure.

Personal GPU consulting: workload analysis instead of shopping cart

Dedicated hardware without shared resources

Premium Network: Lowest Latency via Direct Peering to Telekom & Vodafone

CUDA, cuDNN & TensorRT integration support

German infrastructure, no Cloud Act

From a single GPU to multi-GPU clusters

Direct Connect to INGATE Cloud

GPU Comparison

Dedicated NVIDIA GPUs for every performance tier.

Model	GPU RAM	Tensor TFLOPS	Tensor Cores	Use Case
NVIDIA H100	80 GB HBM3	~3,958	640 (FP8)	LLM Training, HPC
NVIDIA RTX PRO 6000 Blackwell	96 GB GDDR7	~3,511	680 (FP4)	LLM Training, Multi-GPU
NVIDIA L40s	48 GB GDDR6	~1,466	568 (FP8)	Training, Inference, VDI
NVIDIA RTX 6000 Ada	48 GB GDDR6	~1,322	568 (FP8)	Rendering, Training
NVIDIA L4 Ada	24 GB GDDR6	~485	240 (FP8)	Inference, Video, VDI
NVIDIA RTX 4000 SFF Ada	20 GB GDDR6	~307	192	Inference, Rendering
NVIDIA A2	16 GB GDDR6	~36	128	Inference (Entry)

Additional GPU models available on request.

More Services

Personal Hardware Consulting

Which GPU architecture suits your framework? How much GPU memory do you need? Is multi-GPU worthwhile? We analyze and recommend — for maximum performance per euro.

CUDA Support & Data Sovereignty

CUDA, cuDNN, TensorRT pre-installed on request. Container runtime for GPU workloads. Your training data and models remain on German infrastructure — no US Cloud Act.

Scalable GPU Clusters

From a single GPU to multi-GPU clusters. Direct Connect to the INGATE Cloud for hybrid workloads.

Learn more

Network & Connectivity

IPv4 and native IPv6, IP addresses per RIPE. Network cards up to 100G (Intel, Broadcom, NVIDIA ConnectX-6). Direct Connect for minimal latency.

INGATE Premium Support

Support via email and phone, free 24/7 emergency hotline, personal point of contact, and highly qualified on-site personnel.

Managed Option

Every GPU server can optionally be operated as a managed server — with regular system and security updates, GPU monitoring, and framework updates.

Technical Highlights

State-of-the-art infrastructure in our data centers for your business-critical applications.

Redundant Power Supply

Dual-path A/B power supply down to the rack. Dedicated transformers, UPS, and backup generators.

High-Efficiency Cooling

PUE < 1.20 through free cooling and cold aisle containment. Optimized for high-density up to 20 kW per rack.

Fire Protection

VESDA early detection and damage-free gas extinguishing system.

High-Speed Backbone

Redundant high-performance backbone with multiple 100Gbit/s links. Direct peering at DE-CIX and MuCon-X for lowest latencies.

Physical Security

Security level SK4. Biometric access control and comprehensive video surveillance.

Sustainability

Carbon-neutral operations with 100% green energy. Certified green electricity and waste heat recovery.

Certified Data Centers

Our primary data center EMC Home of Data in Munich holds the following certifications. All additional data centers are at least ISO 27001 certified and powered by 100% renewable energy. Select locations additionally hold SOC 1, SOC 2, and PCI-DSS certifications.

ISO 27001

Information Security

ISO 9001

Quality Management

ISO 50001

Energy Management

DIN EN 50600

DC Availability

CSR 26001

Corporate Responsibility

TÜV Süd

100% Green Energy

Frequently Asked Questions

Answers to the most important questions about GPU servers.

Which GPU is right for my AI project?

That depends on the workload: For inference and rendering, we recommend the RTX 4000 SFF Ada (20 GB GDDR6, compact and energy-efficient). For LLM training and multi-GPU setups, the RTX PRO 6000 Blackwell (96 GB GDDR7) is the better choice. We analyze your workload free of charge.

Are multi-GPU setups possible?

Yes, we implement multi-GPU configurations for demanding AI and ML workloads. The GPUs can also be connected to the INGATE Cloud via Direct Connect for hybrid workloads.

What software is pre-installed?

Upon request, we pre-install CUDA, cuDNN, TensorRT, and container runtimes for GPU workloads. We support all major ML frameworks including PyTorch, TensorFlow, and JAX.

Does my training data stay in Germany?

Yes, your data and models remain on sovereign German infrastructure. As an owner-managed German GmbH, we are not subject to the US Cloud Act.

Which operating systems are supported?

All major distributions: Ubuntu, Debian, CentOS, openSUSE, FreeBSD, and Windows Server. We can also pre-install any operating system of your choice at no extra cost.

Is a different hardware configuration possible?

Yes, we can configure any system to your specifications — including GPU configuration, storage, and networking. Our hardware consulting is included.

What is inference and how does it differ from training?

Inference refers to using an already trained AI model to make predictions or decisions in real time — such as image classification, speech recognition, or chatbots. Training, on the other hand, is the upstream process where the model learns from large datasets and optimizes its parameters. Training requires massive computing power and is typically performed on GPUs like the NVIDIA H100 or A100, which feature Tensor Cores and high VRAM bandwidth (HBM3). Inference can often be run on more efficient GPUs like the NVIDIA L4, T4, or RTX series, as throughput and energy efficiency matter more than raw computing power.

What are the differences between GPUs for rendering and AI workloads?

Rendering GPUs like the NVIDIA RTX PRO 6000 are optimized for visualization, CAD, VFX, and simulation. Their focus is on RT Cores (raytracing) and high VRAM for large scenes. AI GPUs like the NVIDIA H100 or A100 prioritize Tensor Cores for matrix operations and offer higher memory bandwidth through HBM3 technology — critical for training large models. Some GPUs like the NVIDIA L4 or the RTX series can handle both tasks well, making them ideal for companies that need both rendering and inference. INGATE advises you individually on which GPU configuration optimally suits your workload.