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You are working on deploying a deep learning model that requires significant GPU resources across multiple nodes. You need to ensure that the model training is scalable, with efficient data transfer between the nodes to minimize latency. Which of the following networking technologies is most suitable for this scenario?
Correct Answer: C
InfiniBand (C) is the most suitable networking technology for scalable, low-latency data transfer in multi- node GPU training. It offers high throughput (up to 400 Gbps) and ultra-low latency (<1 µs), ideal for synchronizing gradients and weights across nodes using NVIDIA NCCL. InfiniBand's RDMA (Remote Direct Memory Access) further enhances efficiency by bypassing CPU overhead, critical for distributed deep learning. * Wi-Fi 6(A) lacks the reliability and bandwidth (max ~10 Gbps) for training clusters. * Fiber Channel(B) is for storage, not compute node interconnects. * Ethernet (1 Gbps)(D) is too slow for large-scale AI training demands. NVIDIA's DGX systems use InfiniBand for this purpose (C).