Distributed Computing @ Scale for AI Training & Inference – Main Stage Keynote
As AI models continue to scale, both training and inference are growing rapidly in operational importance. Training pushes the limits of compute density and interconnect scale, while inference now dominates production workloads. Together, these forces are reshaping AI system architectures.
Meeting these demands requires a next-generation networking fabric that can:
- Scale up within and across a small number of racks to tightly couple XPUs for high-throughput training and low-latency inference
- Scale out across entire data centers using flat, high-performance topologies that support large-scale training and high-fanout inference workloads
- Scale across geographically distributed data centers, enabling unified AI fabrics that support million-plus-XPU training and inference environments.
We will present the latest advancements in industry initiatives—including Ethernet Scale-Up Networking (ESUN), Scale-Up Ethernet Transport (SUE-T), and Open Cluster Design for AI—and show how Ethernet is democratizing large-scale AI deployments through insights from G42 and other AI operators.
Hasan Siraj is Vice President, Product Management at Broadcom, where he oversees product
management across the company’s routing, switching & high-performance NIC
portfolio—including Tomahawk, Trident, Jericho and Thor Ultra NIC. Additionally, he drives
Broadcom’s open networking and open-source strategy. He works closely with hyperscalers,
enterprises, service providers, OEMs, and ODMs to build a thriving ecosystem around
Broadcom silicon. Hasan is also facilitating the build-out of some of the world’s largest AI
infrastructure deployments and expanding Broadcom’s merchant silicon into adjacent markets
through strategic partnerships and new go-to-market models. Prior to Broadcom, he led Cisco’s
Enterprise Routing and Switching teams. He holds a Master’s in Electrical Engineering from
Cornell University and an MBA from The Wharton School.
