Batching and parallel decoding are techniques to speed up AI model outputs. Batching groups multiple inputs into one process, while parallel decoding generates parts of the output simultaneously instead of one token at a time.
Batching pools several requests to be processed together, improving hardware utilization. Parallel decoding splits the output generation into multiple streams, running them at the same time. This reduces overall latency by avoiding sequential bottlenecks.
For product managers, these methods lower response times and reduce computing costs. This means faster user interactions, better scalability for large workloads, and improved efficiency, making AI features more practical and cost-effective to deploy in real-world products.