What process allows for efficient processing of multiple requests in AI models?

The process that allows for efficient processing of multiple requests in AI models is continuous batching. This approach involves aggregating incoming requests over a short period and then processing them as a single batch rather than individually. This not only optimizes the use of computational resources but also takes advantage of the parallel processing capabilities of AI architectures.

Continuous batching improves throughput and lowers latency by reducing the overhead associated with initiating individual processing requests. By grouping tasks together, the model can minimize idle times and better utilize memory and processing power. This method is particularly beneficial for systems with fluctuating workloads, where the request arrival rate can vary significantly, allowing the system to dynamically adjust how it processes those requests.

In contrast, other methods like concurrent processing primarily focus on handling multiple tasks at the same time without necessarily grouping them into batches, while batch processing may not adapt to incoming requests in real-time. Multi-request handling, while relevant, does not encapsulate the concept of effectively grouping requests for optimized processing as continuous batching does. Thus, continuous batching stands out as the most efficient solution for managing multiple requests in AI models, especially in scenarios where speed and resource utilization are critical.