GPU servers offer enormous computing capacity and make it possible to handle tasks that traditional CPUs alone would struggle with. Their ability to process numerous operations in parallel makes them particularly well suited to many modern applications.

What are GPU servers?

A GPU server is a server equipped with graphics processors (Graphics Processing Units, or GPUs). GPUs were originally developed to render graphics, particularly for computer games and animations. In recent years, however, their high performance has also made them suitable for general computing tasks. They are particularly effective when calculations need to be carried out in parallel. While traditional servers use CPUs that typically process tasks sequentially, GPUs can handle multiple processes at the same time.

Fact

The main difference between a CPU and a GPU lies in their architecture and intended use. A CPU is optimised for general computing tasks and typically processes them sequentially, making it versatile but less efficient for highly parallel workloads. GPUs, by contrast, are specifically designed to process large numbers of smaller tasks in parallel. While a CPU has a relatively small number of powerful cores, a GPU often has thousands of smaller cores that can operate simultaneously.

What are the advantages of GPU servers?

Thanks to their architecture, GPU servers offer numerous advantages over traditional CPU-based servers.

High computing performance with large data volumes: GPUs are designed to process large amounts of data in parallel. This enables them to complete demanding tasks far more quickly than conventional CPUs, which may take days or even weeks to perform the same calculations.

Efficiency for parallel tasks: Applications such as machine learning and artificial intelligence, image and speech recognition, and simulations benefit greatly from the ability of GPUs to process multiple operations simultaneously.

Cost benefits for high-performance computing: Although the initial cost of a GPU server can be higher depending on the hardware, faster processing times and the ability to handle multiple tasks simultaneously can offset the additional expense.

Scalability: GPU servers can be expanded to meet growing computing requirements.

Adaptability: With a wide range of frameworks and tools, such as TensorFlow and PyTorch, GPU servers can be optimised for different workloads and requirements.

Which areas are GPU servers used in?

GPU servers have a wide range of potential uses. They are particularly relevant for applications that require high computing power and parallel processing. Key areas of application include generative AI, large language models, multimodal models and established applications in artificial intelligence and machine learning. GPU servers can be used for training demanding models as well as for fine-tuning and inference.

GPU servers also play an important role in the processing of image, video and audio data. They are used for applications such as computer vision, real-time analytics, image and speech recognition, and media processing. They are also a powerful solution for rendering, 3D graphics and other graphics-intensive applications.

GPU servers are also well suited to complex simulations in scientific fields such as physics, chemistry, climate research and life sciences. Their large number of cores allows many smaller tasks to be processed simultaneously, enabling calculations to be efficiently parallelised. This also makes GPU servers an important infrastructure component in high-performance computing.

Other modern areas of application include digital twins, industrial simulations and robotics. In these scenarios, GPU servers can calculate, simulate and optimise complex virtual models at high speed. Depending on the application, they can also provide significant benefits for data-intensive analytics workloads.

Which high-performance server GPUs are currently available?

Choosing the right GPU is essential for getting the best possible performance from your GPU server. A comparison of server GPUs shows that different models are suited to different areas of application:

  • NVIDIA B200: The B200 is a high-end data centre GPU based on the NVIDIA Blackwell architecture and is designed for highly compute-intensive AI infrastructures. It is particularly well suited to powerful systems used for training, fine-tuning and inference of large models.
  • NVIDIA RTX PRO 6000: The RTX PRO 6000 Blackwell Server Edition combines AI and visual computing performance in a single GPU. It is suitable for inference, rendering, simulations and graphics-intensive workloads in the data centre.
  • NVIDIA L40S: The NVIDIA L40S is a versatile server GPU for AI, graphics and video workloads. It is particularly useful when AI applications need to be combined with rendering, 3D graphics or media processing.
  • NVIDIA H200: The NVIDIA H200 is a powerful development of the Hopper generation and is particularly well suited to generative AI, large language models and high-performance computing. Its strengths are especially apparent in memory-intensive workloads.
  • NVIDIA H100: The NVIDIA H100 is designed for AI and high-performance computing applications. Its tensor cores are specifically optimised for machine learning and AI training.
  • NVIDIA A100: The NVIDIA A100 supports accelerated training and inference of AI models. With its third-generation tensor cores, it delivers strong performance for deep learning tasks as well as high-performance computing.
  • NVIDIA A30: The NVIDIA A30 combines computing performance with efficiency. It is particularly well suited to workloads that involve both training and inference, such as AI-powered analytics or cloud services.
  • Intel Gaudi 3: This AI accelerator was developed specifically for artificial intelligence and machine learning. Its architecture is designed for energy efficiency and high scalability, making it an alternative to NVIDIA GPUs, particularly for workloads based on supported AI frameworks.
  • AMD Instinct MI350X: The AMD Instinct MI350X is designed for AI and HPC workloads in the data centre. It is suitable for training large models, fast inference and complex scientific computations.
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