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DTEND:20260513T154000Z
UID:43ff430f5b2f190938db838e5e76457c
DTSTAMP:1781198334
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DESCRIPTION: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.

URL;VALUE=URI: https://onug.net/events/distributed-computing-scale-for-ai-training-inference-main-stage-keynote/
SUMMARY:Distributed Computing @ Scale for AI Training 
DTSTART:20260513T154000Z
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