AI Infrastructure Is Not One-Size-Fits-All and Why That Matters

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.

Webinar Recording

Additional Notes

Key Moments

  • Flex architecture (2:32)
  • Security + standardization (3:35)
  • Agentic AI and CPU fit (8:58)

Jump to what matters to you

Timestamp Title Description
01:25 AI Workloads: It’s Not Just LLMs Small models, edge AI, and why the “one size” myth is dangerous.
02:32 Flex Out, Flex In: Hybrid Deployment Models Why enterprises are reconsidering where AI runs.
03:35 Data Integrity & Security in AI How to secure massive data flows across environments.
04:59 Standardization + Kubernetes Replicating environments across cloud and on-prem.
06:52 Modernizing the Data Center Replacing legacy CPUs with EPYC: sustainability and scale benefits.
08:58 Agentic AI + CPU Efficiency Why CPUs are well-suited for smaller, distributed models.
10:28 Scaling with Open Standards (UAL) Open accelerator links and AMD’s multi-element architecture.
11:55 The Helios Platform How AMD combines CPU, GPU, NPU, and networking into a total AI solution.

Want to discuss AMD EPYC for your AI infrastructure?