For a project that requires high throughput and low latency in real-time AI inference, which computing architecture should be prioritized?

Prioritizing GPUs for AI inference and using CPUs for data pre-processing is the most suitable choice for a project that demands high throughput and low latency in real-time AI inference. GPUs are specifically designed to handle parallel processing tasks and are highly efficient for the types of matrix and tensor computations that underpin AI models, especially deep learning. Their architecture allows for simultaneous processing of multiple data elements, making them ideal for executing inference at scale efficiently.

In contrast, data pre-processing tasks typically involve operations that may not benefit as much from parallel processing. CPUs, with their optimized architecture for handling diverse tasks and low-latency operations, are well-suited for pre-processing data before it is sent to the GPU for inference. This division of labor leverages the strengths of both types of processors, ensuring that high-performance AI inference can occur while still maintaining fast data preparation.

Choosing a different architecture, such as solely using CPUs for both tasks, would lead to slower processing times and higher latencies that are not suitable for real-time applications. Deploying AI inference on CPUs and data pre-processing on FPGAs might introduce additional complexities and latency due to the interfacing between different types of hardware that don’t optimize the workflow as effectively for high-throughput inference scenarios. Therefore,