When building large-scale AI GPU clusters for training or inference, the backend network should be high-performance, lossless, and predictable to ensure maximum GPU utilization. This is hard to achieve when using Ethernet for the back-end network. This guide showcases a high-level reference design for an 8,192 GPU cluster, describing how it can be achieved with DriveNets Network Cloud-AI, equipped with 400Gbps Ethernet connectivity per GPU. This design explores network segmentation, high-performance fabrics, and scalable topologies, all optimized for the unique demands of large-scale AI deployments.
In this guide you will learn about:
- Network architecture of the GPU cluster
- Blueprint example – an 8,192 GPU cluster build
- Rack elevation and data center layout
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