• When we talk about building AI data centers, east-west GPU fabrics often steal the spotlight. • But there’s another traffic path that’s just as critical: north-south connectivity. • In today’s AI environments, how your data center ingests data and delivers results at scale can make or break your AI strategy. • Why north-south traffic now matters most for AI at scale AI is no longer a siloed project tucked away in an isolated cluster. • Enterprises are rapidly evolving to deliver AI as a shared service, pulling in massive volumes of data from external sources and serving results to users, applications, and downstream systems. • This AI-driven traffic generates the bursty, high-bandwidth north-south flows that characterize modern AI environments: - Ingesting and preprocessing huge datasets from object stores, data lakes, or streaming platforms - Loading and checkpointing large models from high-performance storage - Querying vector databases and feature stores to provide context for retrieval-augmented generation (RAG) and agentic workflows - Serving real-time inference to thousands of concurrent users or microservices AI workloads amplify traditional north-south challenges; often they arrive in unpredictable bursts, can move terabytes in minutes, and are highly sensitive to latency and jitter.

Article Summaries:

  • When we talk about building AI data centers, east-west GPU fabrics often steal the spotlight. But there’s another traffic path that’s just as critical: north-south connectivity. In today’s AI environments, how your data center ingests data and delivers results at scale can make or break your AI strategy. Why north-south traffic now matters most for AI at scale AI is no longer a siloed project tucked away in an isolated cluster. Enterprises are rapidly evolving to deliver AI as a shared service, pulling in massive volumes of data from external sources and serving results to users, applications,

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