Performance
Intensive
Computing

Catch up on the latest research and analysis around artificial intelligence.

AI training and inference may be two sides of the same coin, but their compute needs can be quite different. 

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.

A Taiwanese hospital’s initial use of AI to interpret medical images with consumer graphics cards fell short. The prescription? Supermicro workstations powered by AMD components. 

The right AI infrastructure can help your customers turn data into actionable information. But building and scaling that infrastructure can be challenging. Find out why—and how you can make it easier. 

Check out the latest analysis and forecasts from top IT market researchers. 

The Student Cluster Computing challenge made its 16th appearance at the SuperComputer 22 (SC22) event in Dallas. The two student teams that were running AMD EPYC™ CPUs and AMD Instinct™ GPUs were the two teams that aced the Linpack benchmark. That's the test used to determined the TOP500 supercomputers in the world.

To compete in the marketplace, enterprises are increasingly employing performance-intensive tools and applications like machine learning, artificial intelligence, data-driven insights and automation to differentiate their products and services. In doing so, they may be unintentionally backing into performance-intensive computing because these technologies are computationally and/or data intensive.

The modern data center must be both highly performant and energy efficient. Massive amounts of data are generated at the edge and then analyzed in the data center.

Last week, Supermicro announced its new H13 A+ server solutions, featuring the latest fourth-generation AMD EPYC™ processors.