Which network technology is essential for the efficient training and inference of AI models?

High-speed networks are essential for the efficient training and inference of AI models due to the large volumes of data that need to be transferred and processed. Training AI models typically involves using vast datasets that are processed in high-throughput environments, which require significant bandwidth to handle the data flow between servers, storage, and computational resources effectively.

In the context of AI, particularly in cloud computing or distributed systems, high-speed connections facilitate quick data access, allowing multiple computational units to work simultaneously without bottlenecks. This capability is crucial for deep learning models that rely on extensive computational power and the ability to exchange parameters swiftly among various components during training.

Moreover, during inference—when the trained model is deployed to make predictions—high-speed networks ensure that input data can be transmitted to the model quickly and that the output can be returned to the user efficiently. Delays in data transmission can severely impact the user experience, especially in real-time applications, making the speed of the network a critical factor.

The other options do not meet the requirements for effective AI model training and inference. For instance, low-speed networks would create significant delays, adversely affecting both the training time and response times during inference. Wireless networks, while useful, often do not provide the consistency and reliability needed for