Which strategy is focused on scalability and cost factors?

The answer focuses on dedicated GPU clusters because these are explicitly designed to enhance scalability and manage cost factors while handling large models and extensive training datasets in generative AI applications. By utilizing dedicated GPU clusters, organizations can effectively allocate resources, allowing multiple tasks and workloads to run simultaneously, which leads to improved performance and reduced training time.

Furthermore, dedicated GPU clusters can be scaled up or down based on demand, providing flexibility in terms of cost management. They allow the organization to optimize resource usage by paying for only what is needed, making them a strategic choice for those looking to balance performance and budget effectively.

While the other strategies mentioned can contribute to improved performance, they do not primarily address scalability and cost factors in the same way dedicated GPU clusters do. For instance, batch normalization is mainly focused on improving the speed and stability of the neural network training process rather than scaling resources. Model parallelism and pipeline parallelism are techniques used to split model training across multiple devices to handle larger models but do not inherently manage cost efficiency as effectively as utilizing dedicated GPU clusters.