Performance
Intensive
Computing

Although AI systems are smart, they still need to be trained. The process isn’t easy. But it’s pretty straightforward with just 3 main steps.

Accelerate HPC and AI workloads with the combined power of CPU and GPU compute. 

Channel partners can learn about AMD products and technologies at the AMD Arena site. It’s your site for AMD partner training courses, redeemable points and much more.

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.

AI architecture must scale effectively without sacrificing cost efficiency. One size does not fit all.

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.

Today’s data center has numerous challenges: provisioning hardware and cloud workloads, balancing the needs of performance-intensive applications across compute, storage and network resources, and having a consistent monitoring and analytics framework to feed intelligent systems management.

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

CPU chipsets are not normally considered systems-on-chip (SoC) but the fourth generation of AMD EPYC processors incorporate numerous I/O functionality at a high level of integration.

Google Cloud has been working over the past several years with AMD on developing new on-chip security protocols. More on the release of the AMD EPYC™ 9004 series processors in this part three of a four-part series..