Performance Intensive Computing

Quantum computing promises to solve problems faster by simultaneously investigating many possible solutions at once. That’s far easier said than done.

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.

AMD has introduced its EPYC 9X84X series processors, formerly codenamed Genoa-X. The new CPUs are designed specifically for technical workloads, and they support up to 1.1GB of L3 Cache.

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. 

Generative AI systems such as ChatGPT are grabbing the headlines. Find out how this super-smart technology actually works. 

In a recent test, Supermicro workstations and servers powered by 3rd gen AMD Ryzen Threadripper PRO processors ran engineering simulations nearly as fast as a dual-processor system, but needed only two-thirds as much power.

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. 

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