AI infrastructure isn’t one-size-fits-all—and assuming it is could cost you performance, security, and time to market. AMD’s Ravi Kuppuswamy shares how enterprises are balancing on-prem and cloud, securing massive data flows and adapting to smaller, distributed AI models. Learn why flexibility, standards, and data-center CPUs are critical to AI time to market and performance at scale.

• AI Workloads: It’s Not Just LLMs
• Flex Out, Flex In: Hybrid Deployment Models
• Data Integrity & Security in AI
• Standardization + Kubernetes
• Agentic AI + CPU Efficiency
• Scaling with Open Standards (UAL)