Why 10GbE networking matters for AI workloads
GPUs are fast. If the network feeding them is slow, that speed is wasted. Here is when bandwidth becomes the bottleneck and why 10GbE (and beyond) matters.
Where the network bites
AI workloads move surprising amounts of data: model weights loading into VRAM, embeddings and documents streaming for RAG, batches of requests hitting an inference API, and datasets feeding fine-tuning. On a 1GbE link, any of these can stall the GPUs while they wait for data.
Signs you have outgrown 1GbE
- Inference throughput plateaus even though the GPU is not fully utilised.
- Loading large models or datasets takes minutes, not seconds.
- RAG pipelines feel slow when pulling many documents at once.
- Multiple users or services share one server and contend for bandwidth.
What 10, 40 and 100GbE buy you
Stepping up to 10GbE removes the most common bottleneck for production inference and data movement. For multi-GPU servers, heavy RAG, or moving very large datasets, 40GbE and 100GbE keep the network ahead of the GPUs.
We deliver 1 and 10GbE as standard, with 40 and 100GbE available. We size the uplink to your workload so the network is never the limiting factor.
Matching network to workload
A single private assistant may be perfectly happy on 1GbE. A SaaS inference backend serving many users, or a multi-GPU training box, benefits from 10GbE or more. The right answer comes from your real traffic, which is exactly what we map during workload discovery.
In short: When GPUs wait on data, faster networking pays off; we size 1 to 100GbE to your workload. Request a custom quote