Skip to main content
Contact Us
NETWORKING · EDGE AI

When should you upgrade from 2.5GbE to 10GbE?

Upgrade when edge AI workloads involve multi-camera analytics, multi-sensor fusion, multi-node inference, or latency-sensitive control where networking CPU overhead is limiting performance.

01

The short answer

You should upgrade from 2.5GbE to 10GbE when your edge AI workload involves multiple high-resolution video streams, multi-sensor fusion, multi-node inference clusters, or latency-sensitive control loops where CPU overhead from networking is limiting inference performance.

An upgrade is rarely a rip-and-replace programme — a reasonable decision framework looks at four questions in sequence.

02

The four-question framework

  • //
    Is the network the bottleneck? Separate bandwidth saturation from latency issues. 10GbE addresses bandwidth headroom; RDMA and RoCEv2 address CPU-bound latency. The right fix depends on which one is biting.
  • //
    Which workloads are most affected? Prioritise multi-camera analytics, machine vision, and multi-node clusters rather than upgrading every link uniformly. Hybrid networks (10GbE at the core, 2.5GbE or 1GbE at the edge) are usually the practical path.
  • //
    Can the upgrade be staged? Most sites can transition incrementally rather than mid-lifecycle. Identify the high-impact links first; leave the rest until next refresh.
  • //
    Is the hardware RDMA-capable? Specifying RDMA-capable NICs from the outset makes the RoCEv2 step a configuration change rather than a second procurement cycle. Cheaper to plan for it now than retrofit later.
03

The practical answer

The right time to upgrade is usually at the point of next platform refresh rather than mid-lifecycle. For projects being specified today with a three-to-five-year horizon, 10GbE with RDMA-capable hardware is now the defensible baseline, even where the current workload does not yet demand it.

For a fuller view, see our pillar article: Why edge AI is outgrowing 2.5GbE.