US The short answer
2.5GbE is sufficient for lightweight edge AI workloads such as single-camera inference or gateway aggregation, but it is usually insufficient for multi-camera video analytics, multi-sensor fusion, or multi-node edge AI clusters, where 10GbE combined with RDMA and RoCEv2 has become the practical baseline.
2.5GbE is a legitimate upgrade path from 1GbE. It reuses existing cabling, gives a meaningful bandwidth lift, and handles general-purpose industrial workloads well. For a lot of edge deployments, it is the right answer.
Where it falls short
The 2.5GbE link starts to fall behind the workload once the edge platform is handling any of the following:
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Multiple 4K/8K video streams A single 4K feed can consume 300–400 Mbps. A handful saturates a 2.5GbE link well before the compute side is being stretched.
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LiDAR, radar, or multi-sensor fusion Combined telemetry from rich sensor suites exceeds practical 2.5GbE throughput, particularly in autonomous and mobile platforms.
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Multi-node inference clusters Inter-node traffic grows faster than 2.5GbE can carry cleanly. Once an architecture moves beyond a single inference box, link headroom becomes the constraint.
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Latency-sensitive control loops 2.5GbE doesn't address the CPU-bound data-movement issues that RDMA solves. Faster link speed alone doesn't fix tail-latency.
2.5GbE as a transitional step
In practical terms, 2.5GbE is a transitional step rather than a destination for serious edge AI. If an edge platform is being specified today for workloads that will grow over the next three to five years, 10GbE with RDMA-capable hardware gives meaningful runway; 2.5GbE often does not.
For a fuller view, see our pillar article: Why edge AI is outgrowing 2.5GbE.