Engineers are troubleshooting slow step time and poor scaling efficiency in a multi-rack distributed AI...

The correct answer is B because distributed AI training performance depends heavily on high-bandwidth, low-latency inter-node communication. NVIDIA DGX SuperPOD reference architecture states that InfiniBand “continues to evolve and lead data center network performance,” with NDR InfiniBand providing “400 Gbps per direction” and “extremely low port-to-port latency.” It also notes that InfiniBand provides additional performance-optimization features, including adaptive routing and collective communication with NVIDIA SHARP.

NVIDIA Network Operator documentation also states that it delivers “high-throughput, low-latency networking for scale-out, GPU computing clusters” and that RDMA supports memory-to-memory transfers that “bypass the CPU and kernel networking stack,” with support for InfiniBand and RoCE protocols. This directly supports deploying a lossless InfiniBand or RoCE fabric for distributed training traffic such as all-reduce communication.

Why the other options are incorrect: Wi-Fi is unsuitable for high-performance multi-rack GPU training communication. Stateful firewalls and deep-packet inspection between training nodes would add latency and bottlenecks. Adding switch ports without fixing oversubscription and latency does not solve distributed all-reduce scaling inefficiency.