What are GPU servers?

GPU servers offer huge computing ca­pa­cit­ies and allow for projects that wouldn’t be possible with tra­di­tion­al CPUs alone. Their ability to execute processes in parallel makes them well suited to many modern areas of ap­plic­a­tion.

What are GPU servers?

GPU servers are servers equipped with graphic pro­cessors (Graphic Pro­cessing Units or GPUs). GPUs were ori­gin­ally developed for graphic design, in par­tic­u­lar for computer games and animation. However, it’s become clear in recent years that they can also be used for general computing tasks, thanks to their high per­form­ance. Their strengths are on full display when it comes to parallel cal­cu­la­tions. While tra­di­tion­al servers use CPUs, which perform tasks se­quen­tially, GPUs can execute several processes at once.

What are the ad­vant­ages of GPU servers?

Due to their ar­chi­tec­ture, GPU servers offer a number of ad­vant­ages that set them apart from tra­di­tion­al CPU-based servers.

✓ High computing power for large amounts of data: GPUs are designed to process large amounts of data in parallel. That allows them to be very quick with tasks that would take tra­di­tion­al CPUs days or even weeks to process.

✓ Efficient with parallel tasks: Ap­plic­a­tions in the areas of machine learning and ar­ti­fi­cial in­tel­li­gence, image and speech re­cog­ni­tion, and sim­u­la­tions benefit immensely from the ability of GPUs to work on multiple processes sim­ul­tan­eously.

✓ More cost efficient with higher per­form­ance: While it might be more expensive to acquire a GPU server (depending on the exact hardware you want), it will quickly pay for itself with its faster pro­cessing times and the ability to complete several tasks at once.

✓ Scalab­il­ity: GPU servers can easily be expanded to keep pace with your growing needs.

✓ Ad­apt­ab­il­ity: GPU servers can be optimised for various re­quire­ments with frame­works and tools like Tensor­Flow and PyTorch.

Which areas are GPU servers used in?

GPU servers have a wide variety of ap­plic­a­tions. They are es­pe­cially useful in areas that require high computing power and par­al­lel­isa­tion. Some of the main areas of ap­plic­a­tion for GPU servers are ar­ti­fi­cial in­tel­li­gence and machine learning. The training processes for neural networks require enormous computing power that GPUs can easily provide.

GPU servers are also suitable for carrying out complex sim­u­la­tions in sci­entif­ic fields like physics and bio­chem­istry. Thanks to their numerous cores, GPUs can complete several small tasks at once and par­al­lel­ise cal­cu­la­tions. That makes GPU servers the tool of choice in high per­form­ance computing.

You’ll often hear talk of block­chain and crypto­cur­rency in con­nec­tion with GPUs as well. That’s not sur­pris­ing since mining and block­chain-based ap­plic­a­tions also benefit from the parallel ar­chi­tec­ture of GPUs.

And of course, GPUs are also a good choice for graphic pro­cessing. They’re in­dis­pens­able for editing high-res­ol­u­tion videos, animation and virtual reality content. They speed up rendering processes and enable real-time editing.

What are the best high-per­form­ance GPU servers?

Choosing the right GPU is essential for getting the most out of your server. The current top models, which are also offered by hosting providers like IONOS, are setting new standards in per­form­ance. Comparing server GPUs, we can see that different models are suited to different areas of use:

• Nvidia H100: As one of the most powerful GPUs in the world, the Nvidia H100 is ideal for ap­plic­a­tions in AI and high per­form­ance computing. It has improved Tensor Cores that are spe­cific­ally optimised for machine learning and AI training. Its energy ef­fi­ciency and scalab­il­ity make it an excellent choice for companies that need maximum per­form­ance.
• Nvidia A100: The Nvidia A100 supports faster training and in­fer­ences for AI models. With its third-gen­er­a­tion Tensor Cores, it offers ex­cep­tion­al per­form­ance for tasks in deep learning and high per­form­ance computing.
• Nvidia A30: The Nvidia A30 combines computing power with ef­fi­ciency. It’s es­pe­cially suited for workloads that include both training and inference tasks, such as AI-assisted analysis and cloud services.
• Intel Gaudi 3: This GPU was spe­cific­ally developed for AI and machine learning. With ar­chi­tec­ture designed for low energy con­sump­tion and high scalab­il­ity, it’s an al­tern­at­ive to Nvidia GPUs and is optimised for specific AI frame­works.