GPU Cluster - Interconnect HPC Systems with GPU to Form a Cluster

What Is a GPU Cluster?

A GPU cluster consists of multiple interconnected GPU Computing systems that provide their GPU computing performance for shared, parallelizable workloads. For smaller workloads or as an entry point into GPU Computing, GPU Workstations can alternatively be used as standalone computing systems. A modern GPU cluster typically consists of GPU compute nodes, a management or orchestration layer, shared storage, and a high-performance cluster network:

• Management & Orchestration Management and orchestration services are used to manage cluster resources, distribute workloads, and control access. Depending on the environment, schedulers, Kubernetes, or OpenStack, for example, can be used for this purpose.
• GPU Computing Systems The GPU systems form the compute nodes of the cluster and provide the actual GPU computing performance.
• Shared Data Storage Centralized or distributed storage provides the compute nodes with shared access to required data, models, and results.
• Cluster Network The cluster network connects the compute nodes with each other as well as with storage and management systems. Depending on the requirements, high-performance Ethernet or InfiniBand networks with high bandwidth and low latency are used for compute-intensive AI and HPC workloads.
• Operating System & Software The compute nodes are equipped with an operating system as well as the drivers, runtime environments, libraries, and software stacks required for GPU Computing, such as NVIDIA CUDA or AMD ROCm.

When Are GPU Clusters Used?

GPU clusters are primarily used when computing tasks can be distributed across multiple GPUs or compute nodes, or when the required GPU computing performance and memory capacity exceed the capabilities of a single system. At high GPU densities, such systems can, for example, be operated in a Liquid Cooling Data Center.

• Artificial Intelligence (AI) & Deep Learning: Training, fine-tuning, and inference of large AI models can be scaled across multiple GPUs and compute nodes. Platforms such as Kubernetes can be used to orchestrate containerized AI workloads and manage the available resources.
• Scientific Computing & Simulations: For complex simulations in physics, chemistry, or weather research.
• High-End Rendering: Used in the film and animation industry to build render farms.
• Big Data Analytics: When large volumes of data need to be processed in the shortest possible time.

In addition, a GPU cluster can provide centralized GPU resources to multiple users or applications. Depending on the software and cluster architecture, the available resources can be distributed across different workloads and users.

Modern GPU clusters are individually designed according to the workload, number of GPUs, storage requirements, and required network bandwidth.

The actual network bandwidth required depends on the number of GPUs, workload, data volume, parallelization method, and cluster size. For distributed AI training and HPC, low latency and efficient data transfer between compute nodes are particularly important in addition to bandwidth. NVIDIA ConnectX-7, for example, supports NDR InfiniBand and Ethernet up to 400 Gb/s; newer ConnectX-8 technology reaches up to 800 Gb/s.

GPU Cluster Prices & Costs for GPU Cluster Systems at HAPPYWARE

It is difficult to provide a general price for a complete GPU cluster. This is mainly because the prices of GPU systems of this type always depend on project pricing and the number of cluster nodes.

If you would like to purchase a GPU cluster tailored to your specific requirements, please contact us. We will provide you with a GPU cluster configured according to your requirements, based on your preferred Linux environment and using GPU Servers from manufacturers such as Supermicro, ASUS, or GIGABYTE.

Note for Educational and Research Institutions: We can offer special conditions for GPU clusters intended for use in public institutions or for research and educational purposes. If you are planning a corresponding GPU cluster project, please contact us!

From planning to implementation – our expert Jürgen Kabelitz can help! Call him and discuss all questions regarding your project with him!

Buy or Rent a GPU Cluster – Options Available from HAPPYWARE

We also offer our customers a wide range of options for GPU clusters. Learn more about our comprehensive services here:

• Individual Configuration We can configure and offer GPU clusters optimized for your applications.

• Vendor-Independent Consulting Our consulting is completely vendor-independent. This allows us to recommend the solution that makes the most technical and economic sense for your requirements.

• Personal Expert Consulting with Many Years of Experience Benefit from our many years of expertise. Our experienced specialists provide personal and professional advice – from planning through implementation.

• Support with Your Decision Together with you, we determine whether purchasing or renting a GPU cluster is the more economical option for your company. Factors such as usage period, utilization, scalability requirements, and operating costs are taken into account.
• Systems with Full Scalability Depending on the selected architecture, GPU clusters can be expanded with additional compute nodes. This allows the available GPU computing performance to be adapted to increasing requirements.
• Financing Options If required, we advise you on various financing options for our GPU cluster solutions. Please contact us regarding leasing or hire-purchase options.

Do you still have questions? Are you missing any information about our GPU clusters? Simply contact our experts and ask any remaining questions!