UK What RDMA is
RDMA (Remote Direct Memory Access) is a networking technology that lets one system read from or write to another system’s memory without routing the data through the CPU at either end, reducing latency and freeing processor resources for AI inference workloads.
In a conventional network stack, every packet is handled by the CPU: copied, checksummed, and queued before being passed to the application. On busy edge AI nodes, that CPU overhead competes directly with the inference workload for the same processor cycles.
RDMA bypasses most of that work. Data moves directly between the memory of two systems through the network interface card, with minimal CPU involvement.
Why it matters for edge AI
The practical effects for edge AI workloads are direct and measurable:
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Lower latency Data arrives at the inference engine faster and with more predictable timing, which matters disproportionately in vision-guided robotics, machine vision, and closed-loop control.
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Freed CPU cycles Compute resources stay available for the AI workload rather than being consumed by the networking stack. On a busy inference node, that often translates directly into useful throughput.
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Better multi-node scaling Clusters of edge devices behave more like a tightly-coupled system, which becomes essential once a deployment moves beyond a single inference node.
How it's delivered
RDMA is most commonly delivered over Ethernet using RoCEv2 (RDMA over Converged Ethernet, version 2), which makes it practical to deploy on standard industrial network infrastructure rather than specialist fabrics. Combined with 10GbE, this is the practical baseline for multi-node edge AI deployments today.
For a fuller view, see our pillar article: Why edge AI is outgrowing 2.5GbE.