In optimizing workloads for an AI-driven financial modeling application, how should you allocate CPU and GPU resources?

In optimizing workloads for an AI-driven financial modeling application, employing GPUs for data analytics while using CPUs for mathematical calculations is a strategic approach. This is because GPUs are specifically designed for parallel processing, which makes them highly efficient for tasks involving large datasets typical in data analytics. The ability of GPUs to perform many operations simultaneously allows them to quickly process and analyze extensive amounts of real-time data, such as stock prices or financial trends.

Conversely, CPUs excel in handling sequential tasks and complex computations that are frequently part of mathematical calculations, such as precise financial models or algorithms. The structure of CPU architecture allows them to manage these tasks effectively, especially when the computations involve logic operations or require significant single-threaded performance.

This approach ensures that each type of resource is utilized to its strengths, enhancing the overall performance of the application and allowing it to handle complex financial modeling efficiently. Other options that suggest reversing the allocation or using GPUs for all functions do not leverage the strengths of each processor type, potentially leading to performance bottlenecks or inefficient resource use.