What does inefficient batching risk causing?

Inefficient batching refers to the process of grouping input data in a way that does not optimize resource use, leading to operational challenges in data processing. When batching is not performed effectively, it can significantly reduce throughput, which is the measure of how many operations can be completed in a given time frame. This inefficiency typically arises when batches are too small or not carefully constructed, leading to delays in processing and a lower amount of data being managed at once.

With poorly optimized batching, the system is not utilizing its capabilities to process data efficiently, resulting in fewer operations being completed over time. This means that while resources may be available, they are not being utilized to their fullest potential, which translates to a significant reduction in the overall throughput of the system.

In contrast, excessive memory usage and increased inference latency may occur as byproducts of inefficient batching, but they do not directly define the primary risk that comes from it. Improved resource allocation, however, is usually the goal of effectively structured batching and is not a risk at all. Thus, the correct choice highlights a crucial aspect of optimizing processing in generative AI and ensures that systems run efficiently under expected loads.