Supermicro releases custom AI server platforms with integrated GPU and storage optimization.

Partnerships are playing a key role for Supermicro Computer Inc. as it develops AI server solutions tailored for graphics processing units, or GPUs, specifically designed for artificial intelligence workloads. In June, Supermicro introduced rack scale plug-and-play liquid-cooled AI SuperClusters for Nvidia Corp.'s Blackwell GPUs. Supermicro is one of only a handful of players capable of developing customized servers to power Nvidia GPUs for AI workloads.

The critical role of networking and GPU direct storage emerged as a key focus during discussions at the Supermicro Open Storage Summit series. Rob Davis, vice president of storage technology at Nvidia, explained: "We have networks from Nvidia that are also supplied by Supermicro, including switches and adapters in both in InfiniBand and Ethernet. Those are connected all the way into the GPU servers using a technology called GPU direct storage. What GPU direct storage does is eliminate the interface to the local central processing unit and move data directly from the network storage into GPU memory." The panel also included William Li, director of solution management at Supermicro; Steve Hanna, head of product management of high-capacity NVMe SSDs at Micron Technology Inc.; Shimon Ben-David, chief technology officer of WekaIO Inc.; and Jon Toor, chief marketing officer of Cloudian Inc.

Rapid data movement from various repositories is essential for AI workload processing, prompting Supermicro to design its Reference Architecture to help businesses meet high-performance computing requirements. As William Li noted, "Without fast storage, AI will be slowed down. That's why we have introduced Supermicro Reference Architecture for AI use cases. We have been working with all these great partners for a long time on total solutions, including GPU storage servers, high-performance computing, as well as the fastest network devices." Addressing unstructured data management challenges, Shimon Ben-David explained that WekaIO enables enterprise data stacks to handle various IO patterns: "We see customers currently using multiple different storage environments, maybe they are using object store of high capacity ingest, and then they are moving it to a parallel file system for preprocessing the data, cleaning, tagging and reaching, and then training the data. We are working with our partners, with Supermicro and Micron, on larger capacity effective drives and with our partners in Cloudian around our ability to virtualize the capacity of an entire environment by an object store."

The storage landscape is undergoing significant transition driven by AI infrastructure demands. Cloudian announced a strategic collaboration with Supermicro this month delivering a data management solution designed to simplify large-scale AI deployments, with Cloudian's software optimized for next-generation AI workflows on Supermicro's storage servers. Jon Toor noted: "This is software that runs on standard servers, and you can start with a small number of servers, as few as three, and then you can grow that limitless within a single storage environment. What this means for your AI workflow is you've now got a single source of truth, an S3 compatible storage pool that you can use for all your different workflows from data ingest and machine learning to data analytics to deploying your model." Micron's release of its 9550 PCIe Gen 5 SSD in July highlighted the increasing importance of high-capacity drives in data center infrastructure. Steve Hanna emphasized: "It's explicitly designed for GPU feeding, AI training and caching. The HDD to SSD transition is a fairly recent phenomenon in these networked data lakes where before people would buy the GPUs, but they would underinvest in their storage. The data set sizes are just getting absolutely massive, you need more storage. The speed in which we feed that pipe is the bottleneck, so we have to use SSDs by definition, to keep the GPUs utilized."