Process Large Data In No Time.


The Advantages of GPUs

Meeting today’s challenges in design, creativity and science, the new GPUs (Graphics Processing Units) combine professional graphics with processing power and AI (Artificial Intelligence) acceleration. Whether 3D rendering, machine learning, or AI: process large or complex amount of data in no time with the NVIDIA A10 graphics card! Hosted, managed and made with love in Germany.

Parallel Processing

GPUs are designed to perform thousands of calculations simultaneously, making them well-suited for tasks that require massive parallelism, such as video processing, scientific simulations, and machine learning.

Lower Costs

GPUs are cost-effective for certain workloads because they can perform a large number of calculations in parallel. This means that fewer GPUs are required to achieve the same performance as traditional CPUs, reducing hardware costs and total cost of ownership.
Easy Use Icon

High Performance

GPUs provide high performance for data-intensive tasks, such as machine learning, artificial intelligence, and scientific simulations. They are optimized for parallel processing, which means that they can process large amounts of data in parallel, providing significant performance improvements over traditional CPUs.

Fly High With Our Cloud

Flexible Icon


Dynamically adapt to changing requirements and environments. We provide the resources, as needed. Ready, whenever you are.
Flexible Icon


We’re built for scale out – and so is our Managed Cloud, based on OpenStack. With us, even sky is not the limit.
Flexible Icon


We’ve seen it all. So we know how to handle tricky situations. Make the most of your Cloud Infrastructure – with us.
Flexible Icon

GDPR Compliant

Data Security and Privacy never get old. And we never get tired of keeping your data safe, here in Germany.
Flexible Icon

Cost Efficient

Get things done with us in a timely manner and pay only for what you use. Saving costs? Sounds savvy.
Flexible Icon

Focus On You

You know your core business best. So do we, when it comes to managing Cloud infrastructures.

Fair. Transparent. Valuable. That’s our Pricing.

What Is Nice To Know

What does GPU stand for?

GPU stands for Graphics Processing Unit. It is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. Originally developed to accelerate 3D graphics rendering, GPUs have evolved to be used for a wide range of computationally intensive tasks, such as scientific simulations, machine learning, and cryptocurrency mining. Compared to CPUs, GPUs have many more cores, which allows them to perform many more calculations in parallel, making them much faster for certain types of processing tasks.

Is using GPUs a new hype or pretty common?

Using GPUs (Graphics Processing Units) for general-purpose computing is not a new concept, but it has become increasingly common in recent years due to the rise of deep learning, big data, and other computationally intensive applications.

In the past, GPUs were primarily used for graphics rendering, but their parallel processing capabilities made them well-suited for other types of applications that require heavy computation, such as scientific simulations and data processing. However, the high cost of GPUs and the specialized programming skills required to use them effectively limited their widespread adoption for these applications.

In recent years, however, the development of libraries and frameworks, such as CUDA and Tensorflow, has made it easier to program and use GPUs for a wider range of applications. Additionally, advancements in GPU hardware, such as increased memory bandwidth and the development of specialized processors, have made GPUs more powerful and efficient, further driving their adoption.

So while using GPUs for general-purpose computing is not a new concept, their increased adoption in recent years can be seen as a reflection of the growing need for high-performance computing in many fields, as well as the ongoing development and refinement of GPU technology and software.

How much faster is a GPU compared to a CPU?

For tasks that can be parallelized and require large amounts of data processing, GPUs can be significantly faster than CPUs. This is because GPUs are designed with many more processing cores than CPUs, which allows them to perform many more calculations in parallel.

For example, tasks such as image and video processing, scientific simulations, and machine learning can see significant speed improvements when using GPUs over CPUs. In some cases, the speedup can be hundreds of times faster.

Who can I contact in Case of a Problem?
You can get help at and in the LiveChat at the bottom right.

Need More Info Around the Cloud?

We know – new things take time to get used to. That’s why where here for you to answer any questions, clear all concerns or receive feedback: get in touch with us by using the LiveChat in the bottom right corner, for example. We are happy to hear from you!