AMD targets system-level AI infrastructure optimization as agentic workloads reshape enterprise compute
Infrastructure design is being redefined by agentic AI, pushing the industry toward system-level AI infrastructure optimization, balancing performance and cost across diverse workloads rather than focusing on faster chips alone. As inference scales and AI moves closer to users, modular, heterogeneous computing architectures are becoming the foundation of the next wave of enterprise AI.
Agentic AI is introducing complex, end-to-end workloads that are compelling Advanced Micro Devices Inc. to architect and implement its infrastructure more holistically than ever before, according to Mark Papermaster (pictured), chief technology officer and executive vice president of Advanced Micro Devices.
“The workloads are so complex because people are looking at what they do end to end. They’re looking at whole processes, not just one bespoke task,” Papermaster said. “That means you need different computing engines and they need to work together at scale. We’re talking across massive clusters of racks.”
Papermaster spoke with theCUBE’s John Furrier at RAISE Summit, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed the shift toward system-level AI infrastructure optimization and the growing importance of modular, heterogeneous architectures for enterprise AI. (* Disclosure below.)
