AI infrastructure is no longer defined by training clusters alone. As inference becomes the dominant AI workload, operators must support a growing mix of architectures with different demands on latency, bandwidth, and connectivity.
Traditional approaches to network and fiber design are being challenged by disaggregated inference, context-aware systems, and emerging agentic workloads. Supporting these environments requires infrastructure that can scale across multiple traffic patterns, compute tiers, and connectivity domains.
This whitepaper explores how AI infrastructure is evolving from a single-fabric model to a collection of specialized architectures, each with unique network and fiber requirements.
Discover how RoCE-based Ethernet fabrics, workload-driven design, and multi-domain optical infrastructure are helping operators build scalable, future-ready AI environments.
