Which strategy would most effectively reduce latency and stabilize frame processing times in a distributed AI application?

Implementing data compression techniques for inter-node communication is the most effective strategy to reduce latency and stabilize frame processing times in a distributed AI application. This approach directly addresses the speed and efficiency of data transmission between nodes in the system. When handling large data sets, such as video streams in AI applications, data can be bulky and slow to transfer. By compressing the data before transmission, the amount of information that needs to be sent across the network is reduced, which minimizes the time it takes for nodes to communicate and share data. This reduction in communication latency leads to faster processing times and more consistent frame rates.

In contrast, increasing the number of GPUs per node, while it may seem beneficial for processing power, does not inherently resolve issues related to data transfer speed and could even exacerbate communication bottlenecks if those nodes are not optimized for efficient inter-node interaction. Reducing video resolution can lower the data load, but it may also compromise the quality and detail necessary for effective AI processing. Optimizing deep learning models for lower complexity can result in faster processing by using less computational power; however, it does not inherently address communication delays between distributed nodes, which can still impact overall performance. Therefore, data compression is the most targeted and effective method for reducing latency