insideHPC Guide to How Expert Design Engineering and a Building Block Approach Can Give You a Perfectly Tailored AI, ML or HPC Environment – Part 3

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In this insideHPC technology guide, “How Expert Design Engineering and a Building Block Approach Can Give You a Perfectly Tailored AI, ML or HPC Environment,”we will present things to consider when building a customized supercomputer-in-a-box system with the help of experts from Silicon Mechanics.

When considering a large complex system, such as a high-performance computing (HPC), supercomputer or compute cluster, you may think you only have two options—build from scratch from the ground up, or buy a pre-configured, supercomputer-in-a-box from a major technology vendor that everyone else is buying. But there is a third option that takes a best-of-both-worlds approach. This gives you “building blocks” expertly designed around network, storage and compute configurations that are balanced, but also flexible enough to provide scalability for your specific project needs.

Taking a Holistic Approach – The Silicon Mechanics Perspective

The Silicon Mechanics Atlas AI Cluster configuration is more than just a combination of the nodes, hardware  components and software that sit within the system. Because of the role of GPUs, NVIDIA has been heavily  involved in developing this ecosystem to specifically address the requirements for organizations involved in  AI, ML, DL, and HPC projects. But, as this system shows, there are many other elements that go into the  design, holistically, that allow the functionality required for these workloads.

Everything within the system—the hardware and software—works together to achieve the goals above and  enable AI, ML, and HPC. That’s why the Silicon Mechanics team takes a holistic approach to each customer’s  project. From the beginning, they focus on building a balanced solution that is optimized for the particular  type of data being produced via the AI project. For example, an AI project that only relies on images will have  different storage requirements than a data set with text and images. This might require some  customization of the system to fit the project.

Silicon Mechanics engineers have the skills and experience to build this balanced system from the ground up,  rather than just relying on a set of pre-configured lists of components that are built to support a vague,  generic concept. As the Silicon Mechanics Atlas AI Cluster configuration shows, knowing what elements work  together allows the adoption of a building-block model that can quickly—but still effectively—adjust  to specific workloads. In addition, the building blocks are already optimized for purpose on their own and, in  many cases, tested to work with related technologies, giving you the best of both worlds. The key is having  engineers who know all the component elements, all the vendors, new technology, and how they work  together.

The result is that you can be confident that your system will give you everything you need for a successful AI,  ML and HPC project—not just for now, but for the years ahead. For more information on this or other use case specific clusters made using a building block model, visit

Over the past few weeks we’ve explored Silicon Mechanic’s new insideHPC Guide:

Download the complete “How Expert Design Engineering and a Building Block Approach Can Give You a Perfectly Tailored AI, ML or HPC Environment,” courtesy of Silicon Mechanics.