What infrastructure characteristic is crucial for handling real-time network optimization in AI systems?

The characteristic of low latency and high availability is vital for real-time network optimization in AI systems. Low latency ensures that the system can process data and respond to inputs almost instantaneously, which is essential for applications that require immediate feedback, such as dynamic routing in network traffic management or real-time decision-making in AI-driven systems. High availability guarantees that the system is consistently operational and capable of handling requests without interruption, which is crucial for maintaining smooth operations and ensuring that optimizations can be applied without delays.

In contrast, high latency processing capabilities would hinder the effectiveness of real-time applications as delays in processing could lead to outdated or less effective optimizations. Support for large batch sizes is more relevant for tasks involving extensive data processing rather than real-time scenarios, where rapid, small transactions are typically more beneficial. Dedicated storage for large datasets is also important for storing and retrieving data but does not directly impact the real-time responsiveness essential for network optimization, making it less relevant in this context.