Build a SaaS^? tool where AI infrastructure teams model a planned GPU^? cluster by entering GPU^? type, rack count, switch family, optics type, topology, RDMA protocol, and expected collective communication pattern. The product produces topology diagrams, estimated fabric bandwidth, transceiver power load, congestion risk, failure-domain analysis, and a vendor-neutral comparison of Ethernet, InfiniBand, and emerging optical approaches. The first version can focus on procurement-stage planning using public vendor specs, user-entered BOMs, and simple all-reduce traffic models.

AI clusters are scaling past tens of thousands of GPUs^? while networking and optics consume a growing share of power and cost. New products such as 800G/1.6T switching, co-packaged optics, rail-optimized fabrics, and open accelerator interconnects make architecture decisions more complex and expensive to reverse.