Selecting and correctly engineering the Nexus fabric is the most important option for achieving scalable AI performance because distributed LLM training depends heavily on communication among GPUs located in different servers. The C885A M8 provides eight high-performance GPUs and high-speed NICs, but cluster-level performance depends on whether the network can transport collective-communication traffic at high bandwidth with extremely low latency and negligible packet loss.

Cisco AI reference architectures use dedicated, nonblocking backend fabrics based on Cisco Nexus switching for GPU-to-GPU RoCEv2 traffic. Cisco specifically applies rail-optimized designs, PFC, ECN, high-speed Ethernet, and nonblocking leaf-spine connectivity to preserve collective communication efficiency as GPU clusters scale.

Option B overemphasizes host CPU cores. LLM training on the C885A is primarily GPU accelerated, and additional CPU capacity does not compensate for a congested inter-node fabric. Option C is essential for availability but does not directly optimize distributed training throughput or scalability. Option D would actively undermine workload capability because adequate host memory is important for data preprocessing, orchestration, caching, and other AI operations.

Therefore, the network fabric's topology, bandwidth, congestion-control behavior, and latency are central to realizing the performance of a multi-node C885A deployment.

Study Guide Reference: AI Infrastructure Components and Architecture — evaluate network and compute deployments based on bandwidth, latency, scalability, GPU characteristics, and workload requirements.

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