GPU computing servers for AI and HPC workloads

Are you looking for the best GPU performance for your business? Then our GPU servers are the perfect choice for you. We’ve thoroughly tested every component and conducted our own benchmarks to ensure we can offer you the best servers for simulation and rendering.

What are the requirements for a GPU server?

For the best performance, we recommend installing NVIDIA graphics cards. These currently offer the best GPU performance for complex applications and are therefore ideal for demanding computations. Whether for scientific calculations, deep learning programs, or GPU rendering, our GPU servers from Gigabyte and Supermicro offer you the best performance in these applications. With up to eight configurable NVIDIA graphics cards in 2U or 4U chassis, you’re perfectly equipped to handle the most demanding computing requirements. With every GPU server, we offer you the option to select state-of-the-art data center graphics cards, such as the NVIDIA H200.

Selecting hardware for a GPU server:

Which graphics cards are suitable for the optimal GPU server?

Cutting-edge, high-performance graphics cards, such as NVIDIA’s latest Ampere GPUs, form the foundation of high-performance GPU servers. Large video memory, high bandwidth, and a high number of floating-point operations are also essential. In our GPU server configurators, you’ll find the most modern graphics cards (NVIDIA L40S, NVIDIA H100, NVIDIA H200), which were developed specifically for artificial intelligence (AI), large language models (LLMs), and high-performance computing (HPC).

Embrace the future of artificial intelligence, large language models, and scientific computing with the NVIDIA H200!

The NVIDIA H200 Tensor Core graphics card is the best solution for anyone who wants to stay at the forefront of artificial intelligence (AI), large language models (LLMs), and high-performance computing (HPC). Here are the reasons why our H200 GPU servers should be your first choice:

Groundbreaking Performance

With the NVIDIA H200, you’ll reach a new dimension of computing power. Thanks to the revolutionary Hopper architecture and 141 GB of HBM3e memory with a bandwidth of 4.8 TB/s, the H200 offers nearly double the capacity and 1.4 times the memory bandwidth compared to the NVIDIA H100. This means your AI models and scientific computations will run faster and more efficiently.

Accelerated AI and LLMs

The H200 doubles inference speed when processing large language models such as Llama2 compared to H100 GPUs. This enables you to train and deploy complex AI models in record time, which is crucial for gaining a competitive edge in the rapidly evolving AI landscape.

Outstanding HPC Performance

For memory-intensive HPC applications such as simulations and scientific research, the H200 delivers results up to 110 times faster than conventional GPUs. The higher memory bandwidth ensures that data can be retrieved and processed efficiently, reducing bottlenecks in complex processing.

Energy Efficiency and Cost Savings

GPU servers with the NVIDIA H200 are not only powerful but also energy-efficient. They offer better energy efficiency and lower total cost of ownership, making them the ideal choice for data centers and enterprises looking to reduce operating costs while maximizing their computing power.

Flexibility and scalability

With NVIDIA H200 servers, you can design computing resources to be flexible and scalable. Whether you use a single GPU or multiple GPUs in a cluster, the H200 adapts to requirements and scales the necessary performance for each task with every additional GPU installed.

What other components are suitable for a GPU server solution?

Processor

Intel Xeon and AMD EPYC processors are particularly well-suited for GPU systems, as they support key server features such as ECC RAM and virtualization. When selecting a CPU, you should prioritize a high clock speed. The number of cores is generally less relevant, as computations are performed via the graphics cards.

Memory

The amount of RAM required depends heavily on the intended use. While a small rendering server with four graphics cards performs well with just 64 GB of RAM, a more diverse solution with up to ten graphics cards may require 256 GB of RAM or more. All additional planned plugins and services will consume extra RAM capacity.

To ensure system stability, we recommend using only ECC RAM. ECC RAM can detect and immediately correct 1-bit errors, preventing system crashes, security vulnerabilities, and file corruption.  

Redundancy and Hot-Swap Capabilities

In a GPU solution, the ability to quickly replace components via hot-swap is crucial. This reduces downtime and increases flexibility. Redundancy for power supplies is almost mandatory, as it ensures the server can continue running even if a module fails.