What is the best approach to address processing delays in an AI model for fraud detection during peak business hours?

Implementing GPU load balancing across multiple instances is an effective approach to address processing delays in an AI model for fraud detection during peak business hours. Load balancing distributes the workload evenly across multiple GPUs, which can significantly enhance processing efficiency and reduce the risk of bottlenecks. By utilizing multiple instances, the system can handle larger volumes of input data simultaneously, improving response times and overall performance during times of high demand.

This method ensures that no single GPU becomes overwhelmed by too many requests, allowing for more consistent and reliable performance even when demand peaks. It optimizes resource utilization, leading to faster processing and better management of the AI model's workload.

In contrast, switching to CPU resources from GPUs would likely result in slower processing speeds, as GPUs are typically better suited for the parallel processing tasks required in AI and machine learning. Disabling GPU monitoring is counterproductive, as it removes valuable insights into resource usage and performance, which are essential for optimizing system efficiency. Increasing the batch size of input data could potentially exacerbate delays if the system cannot handle large batches effectively during peak times. Therefore, focusing on load balancing presents the best strategy for managing processing delays.