How do I choose the right GPU server?

September 19, 2022

GPU Server – essential for demanding high-performance applications

Graphical processing units, or GPUs for short, were originally developed to display textures and shadows from computer games on the user’s screen. Today, professional users use GPU servers in many different areas that require a lot of computing power. While a few years ago only a very small part of all companies and organizations had points of contact with supercomputing or high performance computing (HPC), these technologies have now also arrived in medium-sized companies. Typical fields of application are, for example, AI, machine and deep learning, big data, CAD, in short: every type of complex calculations and analyses. But everyday applications such as virtual desktops also help GPU servers to achieve greater efficiency and performance.

  • What can GPU servers do better than CPU servers?

  • GPU Server: Typical application scenarios

  • GPU Server: Typical application scenarios

  • How do I choose the right GPU server?

  • The required performance is crucial for GPU servers

  • Rent or buy GPU servers?

  • Rent GPU servers inexpensively

  • GPU servers: The HPC technology of the future

What can GPU servers do better than CPU servers?

But why do many professional users who need a lot of performance move away from the classic CPU server to the GPU server? The keyword here is “performance”. GPUs have many more processor cores than CPUs. However, the GPU cores are less complex and are only ever good for certain types of calculations. NVIDIA, currently the leading manufacturer on the graphics card market, distinguishes between three types of cores:

CUDA cores

These cores are specially designed for parallel processing and are mostly used in practice in combination with the NVIDIA CUDA toolkit. CUDA was originally introduced as a shading architecture, but today it is a programming model, architecture, platform and API in one. Many applications and frameworks, for example for deep learning, have built-in CUDA support, making it easier for the user to set up.

tensor cores

Tensor Cores help users increase throughput for HPC and AI applications. Mixed Precision Computing dynamically adjusts calculations to increase speed while maintaining precision. With Tensor Float 32, for example, accelerations of up to ten times are possible in AI training today. For complex calculations, this means massive time savings for the user.

RT cores

The RT in RT Cores stands for “Ray Tracing”. These cores accelerate special ray-traced graphics so they can be rendered in real-time. Typically, developers of video games and other moving image applications use this technology.

GPU Server: Typical application scenarios

In general, it can be said that GPU servers are mostly used for problems in which the same algorithm is always applied to different data sets. In technical jargon, this parallelization principle is also known as SMID, in short: “single instruction, multiple data”. This is due to the particularly good performance of GPUs in floating-point operations and the handling of matrices and vectors. GPU servers have been established for a long time, for example in the fields of biochemistry and pharmacy. Here they are often used for calculating protein convolutions and other methods where matrix operations and solving complex linear equations are important. In the recent past, it has primarily been the fields of AI, machine and deep learning

How do I choose the right GPU server?

The selection of the GPU server depends primarily on the type and nature of the application. It is only worth using a GPU server if there is a bottleneck in CPU performance due to a large number of tasks that can be run in parallel. At NVIDIA, for example, users will find a list of all applications, frameworks, and developer tools that support CUDA. As a rule of thumb: The use of applications with CUDA support makes setup and work much easier. If you use programs you have developed yourself, you should make sure to use GPU-capable tools and libraries right from the start.

The required performance is crucial for GPU servers

Another important selection criterion is the required performance. Unfortunately, this can often only be roughly estimated in the run-up to a project. A first clue is the floating-point performance of the individual GPUs, which should make them comparable with each other. In practice, however, this is not always the case since these are only theoretical values ​​and not real benchmarks. Many manufacturers also offer benchmark tests for their GPUs, but not for each individual application in particular. Our tip: If you want to be on the safe side, use a GPU server from the cloud.

Rent or buy GPU servers?

As already mentioned, choosing the right GPU server can be difficult due to a lack of experience. So instead of buying a GPU server outright and spending a lot of money on a product that may not be the right thing after all, it’s worth taking a look at the rental market. Data center operators now also offer GPU servers with common graphics cards in a rental model. Here users can test the IT infrastructure for a limited period of time.

Rent GPU servers inexpensively

Our Powerlance GPU servers are immediately ready for use, have a flexible runtime and offer you optimal performance for AI, Big Data, HPC, and much more. To our GPU servers

In many cases it is even worth using a GPU server from the cloud for the entire duration of the project. By operating in an external data center, the administration of the hardware is no longer necessary and the user benefits from the professional IT infrastructure of the provider. With usage-based billing models, customers only pay for the time they really need the GPU server – this is a real advantage, especially in the development phase. Another important point is the issue of scalability. If the user notices in the course of the project that performance and resources are becoming scarce, new hardware must first be purchased and installed for an on-premises (on-site at the company) GPU server. With a GPU server from the cloud, it can store, work memory,

GPU servers: The HPC technology of the future

In conclusion, it can be said that GPU technology has gained massively in importance in recent years. More and more fields of application rely on the performance of modern GPU servers. This trend will continue. It is worthwhile for many users and developers to check whether their applications also benefit from GPU performance. Processes can often be accelerated or made even more efficient. GPU servers from the cloud offer an uncomplicated entry option. In any case, it is advisable to contact an experienced provider and get advice.

 

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