What is the best strategy to ensure consistent performance across GPUs in a real-time video analytics application?

Implementing dynamic load balancing that assigns workloads to GPUs is the best strategy for ensuring consistent performance across GPUs in a real-time video analytics application. This approach allows for the optimal distribution of processing tasks based on the current capabilities and workload of each GPU. It enables the application to adapt to varying workload demands and hardware performance, ensuring that no single GPU becomes a bottleneck while others remain underutilized. By monitoring the load on each GPU and dynamically adjusting the task assignments, the system can maximize resource utilization and maintain consistent processing times across all GPUs.

Using a single GPU for each camera feed would not leverage the available computational resources effectively, especially if some feeds require less processing power than others. This could lead to situations where some GPUs are idle while others are overloaded, resulting in inconsistent performance.

Randomly distributing video streams across all available GPUs lacks a strategic approach. This method does not consider the processing power or current load of each GPU, which can lead to uneven performance and inefficiencies, as some GPUs may be assigned more demanding streams than they can handle effectively.

Allocating the most computationally intensive tasks to the GPU with the least memory usage is counterproductive. Typically, the GPU with the least memory may not be equipped to handle heavy computations efficiently, which could