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.