What is a key difference between training and inference architectures in AI deployments?

The distinction highlighted in this answer centers on the differing requirements and priorities of training versus inference in AI systems. Training a model involves the use of large datasets to adjust model parameters, which requires extensive computational resources and power due to the need for complex calculations over numerous iterations. This process is generally resource-intensive, necessitating high compute power and significant memory.

In contrast, inference—the process of deploying the trained model to make predictions on new data—places a strong emphasis on low latency and high throughput. This ensures that the model can respond quickly and efficiently to incoming data requests, which is critical for real-time applications.

By prioritizing low latency and high throughput during inference, systems can deliver fast responses, making them suitable for practical applications, such as those requiring immediate feedback. This key difference underlines the complementary nature of these two stages in the AI lifecycle, with each having its own computational and performance needs. Other options revolve around different aspects that do not encapsulate the primary focus on compute power versus responsiveness in the context of training and inference.