Your team is scaling out deep learning training across multiple physical server nodes, each packed with NVIDIA H100 GPUs. During distributed training, the nodes must constantly share gradient updates using collective communication libraries like NCCL. Which networking technology should you implement to prevent inter-node network latency from bottlenecking the GPUs?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Pay close attention here, because this one bites people in production all the time. When you link multiple GPU servers together for distributed training, the network is almost always the bottleneck. The GPUs finish calculating gradients in milliseconds, but then they have to wait for the network to sync up. If you're running standard TCP/IP over Ethernet, you're routing traffic through the CPU, which adds massive latency. That's a dealbreaker. To solve this, we use InfiniBand with RDMA (Remote Direct Memory Access). RDMA lets one GPU talk directly to another GPU's memory in a different server, completely bypassing the CPU and the OS kernel. It's like having a direct warp-speed bridge between servers. Trust me on this, for serious multi-node training, InfiniBand with RDMA is the gold standard. Got it? Sweet. Let's keep rolling.
Full explanation below image
Full Explanation
Distributed deep learning training across multiple nodes relies on frequent communication to synchronize model parameters (gradients) across all GPUs. This synchronization is usually managed by collective communication libraries like the NVIDIA Collective Communications Library (NCCL), which perform operations like AllReduce and AllGather. In a multi-node cluster, standard Ethernet and TCP/IP protocol stacks introduce significant latency and CPU overhead because data must be copied through multiple kernel and user-space buffers. To eliminate this bottleneck, high-performance computing (HPC) infrastructures implement InfiniBand with Remote Direct Memory Access (RDMA) support (Option B). RDMA allows a GPU on one node to read and write directly to the memory of a GPU on another node without involving either host operating system or CPU, resulting in extremely high bandwidth and sub-microsecond latency. Bonding standard Ethernet ports (Option A) increases bandwidth but does not solve the latency and CPU overhead caused by the TCP/IP stack. Software-defined networking (Option C) improves routing management but does not reduce protocol processing latency. Reducing nodes (Option D) simplifies the network but defeats the purpose of scaling out compute capacity. Therefore, InfiniBand with RDMA is the critical infrastructure component required.