Batch processing and asynchronous calls improve the throughput of a Retrieval-Augmented Generation (RAG) system by optimizing resource utilization and parallelizing tasks. Throughput refers to the number of requests a system can handle per unit of time. Batch processing groups multiple queries into a single batch, allowing the system to process them together, which reduces overhead and leverages hardware acceleration (e.g., GPU parallelization). Asynchronous calls decouple the acceptance of requests from their processing, enabling the system to handle incoming queries without waiting for prior tasks to finish. For example, a RAG system using batch processing might process 10 user queries in a single GPU inference step instead of 10 separate steps, cutting total computation time. Similarly, asynchronous handling allows the system to accept new requests while still processing older ones, avoiding idle time.
However, these optimizations often come at the cost of increased latency for individual queries. Latency measures the time taken to complete a single request. In batch processing, a query might wait in a buffer until the batch is full, adding delay. For instance, if a batch size of 10 is required for optimal GPU utilization, the first query in an empty batch could wait for nine more requests before processing starts. Asynchronous systems can introduce queuing delays if the processing pipeline is overloaded, even though they improve overall throughput. For example, a sudden spike in requests might cause a backlog, forcing some queries to wait longer in a queue even as the system processes more total requests. These trade-offs are inherent in systems prioritizing throughput over individual speed.
The impact on latency depends on implementation choices. Smaller batch sizes reduce wait times but may underutilize hardware, while larger batches maximize throughput at the expense of latency. Asynchronous systems can mitigate this by prioritizing time-sensitive requests or using dynamic scaling. For example, a RAG system might use dynamic batching—processing batches as soon as they’re ready or after a short timeout—to balance latency and throughput. Similarly, asynchronous pipelines can employ load balancing or autoscaling to manage queue lengths. Developers must tune these parameters based on workload patterns: high-traffic systems benefit more from batching and async handling, while low-latency requirements might favor smaller batches or synchronous processing for critical queries.
