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Top Trends in Networking for AI 2026

Top Trends in Networking for AI 2026 This report examines the growing complexity of networking AI workloads — from datacenter clusters to the edge — covering key challenges like power consumption, emerging standards, and vendor solutions as agentic AI rapidly becomes central to business operations.

This report focuses on what is commonly termed scale out and scale across: the connections between clusters in AI datacenters, as well as the interconnections between datacenters, and between datacenters and the network edge.

  • Networking for AI is unprecedented in its complexity, challenging even the most technically seasoned of enterprise users.
  • Ethernet remains the preferred datacenter network.
  • The challenges of networking for AI are forcing enterprise customers to look outward. Hyperscalers, neoclouds, and altscalers are benefiting from demand for AI help.
  • Services and solutions that address the need to deploy AI at the network edge are on the rise.

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As AI infrastructure investments accelerate, DriveNets’ scheduled fabric technology helps hyperscalers, NeoClouds, and enterprises build high-performance GPU backend networks based on open Ethernet. By simplifying deployment and improving network efficiency, DriveNets helps reduce time to first token while improving the economics of AI infrastructure at scale. The result is a faster, more cost-efficient path from infrastructure investment to business value.

The company is now working with leading AI vendors such as AMD, Broadcom, and others to tighten the integration between networking and compute in multi-vendor AI environments, maximizing cluster performance and GPU utilization to substantially improve token economics.