Performance
Intensive
Computing

Sales of AI semiconductors are poised for big growth. AI is transforming the data center. Sustainability services are hot. Manufacturers are saving big money with cloud. And Americans are surprisingly lacking in tech understanding.

In Part 1 of this 2-part Tech Explainer, we explored the difference between how machine learning and deep learning models are trained and deployed. Now, in Part 2, we’ll get deeper into deep learning to discover how this advanced form of AI is changing the way we work, learn and create.

What’s the difference between machine learning and deep learning? That’s the subject of this 2-part Tech Explainer. Here, in Part 1, learn more about ML. 

Supermicro has a new class of storage servers that support E3.S Gen 5 NVMe drives. They offer up to 256TB of high-throughput, low-latency storage in a 1U enclosure, and up to half a petabyte in a 2U.

Liquid cooling can offer big advantages over air cooling. According to a new Supermicro solution guide, these benefits include up to 92% lower electricity costs for a server’s cooling infrastructure, and up to 51% lower electricity costs for an entire data center.

Supermicro’s H13 Petascale Storage Systems is a compact 1U rackmount system powered by the AMD EPYC 97X4 processor (formerly codenamed Bergamo) with up to 128 cores.    

In an interview, Bernhard Homoelle, head of the HPC competence center at German system integrator SVA, explains how his company serves customers with help from AMD and Supermicro. 

AMD and Supermicro are jointly offering high-performance AI alternatives with superior price and performance.

AMD strengthens its commitment to the cloud and enterprise data centers with new "Bergamo" CPUs, "Genoa-X" cache, Instinct accelerators.

Like a hockey team with players in different positions, an AI system with both a GPU and CPU is a necessary and winning combo. This mix of processors can bring you and your customers both the lower cost and greater energy efficiency of a CPU and the parallel processing power of a GPU. With this team approach, your customers should be able to handle any AI training and inference workloads that come their way.