Continuous batching processes multiple AI tasks together to improve hardware utilization and throughput. Recent advances introduce asynchronicity to this method, allowing tasks in the batch to be handled flexibly and independently rather than strictly sequentially.
This asynchronous approach reduces latency and increases throughput by enabling overlapping execution of tasks. It offers a path to optimizing AI workloads for better performance and responsiveness in real-time and large-scale scenarios.
