The Core of GPUaaS Competition Is Network Connectivity, Not GPUs

AI training performance is often determined by data exchange speed rather than GPU compute speed. In large-scale clusters, AllReduce communication, which synchronizes parameters between GPUs, occurs repeatedly, so network bottlenecks delay overall training time.

Roles of Internal and External Connectivity

NVLink acts as an internal highway for fast data exchange between GPUs within a server. NVIDIA maximizes collaboration efficiency by connecting 72 GPUs into a single NVLink domain via the GB200 NVL72.

For scaling between servers, InfiniBand, RoCE, and high-speed Ethernet are used.

• InfiniBand: A dedicated high-speed network with strengths in low latency and large-scale communication
• RoCE: Delivers high performance by leveraging RDMA over existing Ethernet infrastructure
• High-speed Ethernet: Evolving into an option with enhanced AI-specific optimizations

Shifting Competitive Landscape

GPUaaS has evolved from simple equipment leasing to a workload-operating platform. Domestic providers are seeking differentiation not just by securing GPU quantities, but through operational maturity that intricately designs internal and external networks and organically integrates them with power, cooling, and storage.