Rethinking the transport layer for AI-first architecture
The advent of AI marks a new era in terms of how digital infrastructure is built, connected, and scaled. Each new generation of AI model demands exponentially greater GPU power, storage capacity, and interconnectivity.
As these workloads rise in complexity, the network responsible for moving data between GPU clusters and across data centers, also referred to as “scale-across”, faces mounting pressure.
Traditionally seen as a straightforward background utility, the transport layer is fast emerging as a strategic foundation for AI-driven infrastructures.
IP Optical Marketing team, Ribbon Communications.
It is evolving from simply moving packets to intelligently orchestrating massive data flows with deterministic performance, low-latency, and seamless scalability.
AI-optimized data centers, built for training and deploying large-scale models, require dense GPU fabrics, vast storage, and, most importantly, high-bandwidth, ultra-low latency connectivity. As workloads increase, so too does the critical importance of Data Centre Interconnect (DCI) solutions.
The transport layer is no...
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