Testing Challenges and Strategies for AI Networking

In the dynamic realm of data centers, AI is setting new benchmarks. AI models demand unprecedented bandwidth, driving data centers to transition from 400G to 800G, and eventually to 1.6T speeds. This rapid evolution is crucial for meeting the massive data demands.

AI workloads differ significantly from traditional data center workloads. Unlike conventional data centers that manage web traffic and server flows with CPUs, AI data centers handle massive datasets with specialized hardware like GPUs. This shift necessitates a robust and efficient networking infrastructure along with a new approach to testing the AI fabric.

During this session we’ll discuss:

• The high-bandwidth, low-latency demands of AI
• Why stress testing the AI fabric is critical for identifying potential bottlenecks and ensuring maximum efficiency
• Comprehensive testing methods that accurately simulate AI workloads and identify network issues before they impact operations
• How emulating AI workloads helps organizations gain confidence in their network infrastructure and minimize the risk of costly disruptions

Agenda:
9:30am – Coffee & Pastries
9:45am – Presentation
11:30am – Lunch