What strategy would best improve the performance of a real-time fraud detection system experiencing latency spikes?

Implementing model parallelism to split the model across multiple GPUs is an effective strategy for improving the performance of a real-time fraud detection system that is facing latency spikes. Model parallelism allows different parts of a model to be processed simultaneously across multiple GPUs, which can significantly reduce the time it takes to produce predictions. Since real-time systems require quick responses, distributing the workload helps to handle larger models and more complex computations without introducing considerable delays.

By utilizing multiple GPUs, the computational burden is shared, leading to enhanced throughput and reduced latency. This approach is particularly beneficial in scenarios where the model is too large to fit into the memory of a single GPU or when the model complexity requires substantial computational power to maintain effective performance.

Other strategies, such as increasing the dataset size or reducing the model's complexity, may not directly address the issue of latency spikes. While increasing data can improve the training and potentially the model's accuracy, it doesn't inherently solve performance issues related to real-time inference. Reducing the complexity of the model can help speed up processing but may also lead to a loss in detection capabilities, potentially impacting the fraud detection system's effectiveness. Deploying on CPUs instead of GPUs is typically not advantageous for high-performance tasks that require rapid processing capabilities, as GPUs