GPU堆到万卡之后，最贵的问题变成了「空转」

TL;DR - A Chinese tech-media analysis arguing that once GPU clusters reach 10,000+ accelerators, the dominant cost is idle time ("空转"), so AI infrastructure competition is shifting from raw chip supply to networking, super-node interconnect, and scheduling that turn nominal FLOPs into effective throughput. It matters because it reframes infra value around utilization economics rather than card count.

• Framing anchor: NVIDIA's newly announced Spectrum-6 Ethernet switch, positioned for the next-gen Vera Rubin platform and gigawatt-scale "AI factories," is cited as evidence that the cluster — not the single GPU — is now the unit of competition.
• Utilization is the core claim: buying 10,000 GPUs does not yield 10,000x compute; data-waiting, uneven task allocation, and resource fragmentation leave expensive silicon idle. An AI Infra scheduling practitioner quoted says systems engineering should lift GPU utilization from ~20–30% to above 70%.
• System-level trend examples: Arista's 1.6T 7060XE7 platform (June) targeting clusters from thousands to hundreds of thousands of XPUs and calling the network a "critical backplane"; DriveNets (July) linking two H200 clusters 52 miles apart into one logical super-cluster, plus an AMD MI350 reference architecture; super-node designs such as GB200/GB300 NVL72 and equivalents from AMD and Huawei.
• Monetization signal: Broadcom reported $10.8B Q2 AI semiconductor revenue (+143% YoY) with networking near 40% of AI revenue; Arista posted ~$3.04B Q2 revenue (+38% YoY). The article also notes scheduling/virtualization moving from free open-source tooling to paid commercial products as deployments scale.