An AI/ML team is designing a fraud-detection model for an online banking application.

GPU-based compute connected by a high-throughput, low-latency network best satisfies the combined training and inference requirements. Model training performs large numbers of parallel matrix and tensor operations, which GPUs execute far more efficiently than general-purpose CPUs. Frequent retraining also requires rapid movement of training data between storage, host memory, accelerators, and—when training is distributed—between GPUs on different servers. High-throughput networking prevents the accelerators from sitting idle while gradients, parameters, or data batches are exchanged. Real-time fraud inference adds a latency requirement: transaction features must reach the model quickly and predictions must return within the application’s response window. Cisco’s AI/ML data-center blueprint emphasizes predictable, high-performance networking for accelerator clusters, while Cisco UCS GPU platforms are designed for high-throughput inference with minimized latency. CPU-only instances may handle lightweight inference but are inefficient for frequent large-scale training. Local SSDs improve data access but do not provide the required inter-compute fabric. Generic cloud elasticity can add capacity, yet Option C does not guarantee GPU acceleration or the deterministic low-latency east-west connectivity stated in the question. Dedicated multicore CPUs and distributed storage remain limited by CPU parallelism and network overhead. Option B directly couples the correct accelerator architecture with the fabric characteristics needed for scalable training and immediate inference, making it the complete solution. Cisco AI/ML data-center networking blueprint

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