Why Network Interconnects Are the New Bottleneck for Large‑Model AI Training

AI large‑model training and inference are causing a sharp increase in the need for intelligent compute resources. Model iteration, larger parameter counts, and the diversification of model types (e.g., text‑to‑image, text‑to‑video) all push compute demand higher, while the explosion of AI‑driven applications accelerates inference‑side requirements.

To meet these pressures, traditional GPU clusters are evolving into "super‑pooled" intelligent computing centers. These centers focus on dense aggregation of GPUs and AI accelerators, requiring upgrades in compute capacity, memory, and especially high‑speed interconnects. The new device form factor—often a "hundred‑GPU super‑server"—introduces fresh challenges for interconnect architecture, storage, platform integration, and cooling.

Network interconnect solutions now coexist at both the node and cluster levels. Inside a node, the private solution is NVIDIA's NVLink, now in its fifth generation and capable of seamless, high‑bandwidth communication among up to 576 GPUs. Open standards such as OAM (Open Accelerator Module) and UBB (Universal Baseboard) defined by the OCP community provide a common hardware topology for AI accelerator modules.

Between nodes, two primary technologies dominate: InfiniBand and RoCEv2. InfiniBand deployments consist of NICs, switches, Subnet Management (SM) services, and cabling, offering superior performance, scalability, and operational simplicity for large clusters. RoCEv2, a pure Ethernet‑based RDMA solution, relies on RoCEv2‑compatible NICs, switches, cables, and flow‑control mechanisms, delivering a distributed networking model.

The coexistence of these solutions reflects the need to balance performance, cost, and ecosystem compatibility when designing next‑generation AI compute infrastructures.