Implementing model parallelism to split the deep learning model across multiple NVIDIA GPUs is the best strategy to improve performance and reliability for a real-time fraud detection system under heavy load.
Model parallelism divides the computational workload of a large model across GPUs, reducing latency and increasing throughput by leveraging parallel processing capabilities, a strength of NVIDIA's architecture (e.
g., TensorRT, NCCL). This addresses latency spikes and missed detections by ensuring the system scales with demand. Option A (CPU cluster) sacrifices GPU acceleration, increasing latency. Option B (reducing complexity) may lower accuracy, undermining fraud detection. Option C (larger dataset) improves training but not inference performance. NVIDIA's fraud detection use cases highlight model parallelism as a key optimization technique.