Dynamic batching: a practical how-to guide

Dynamic batching is a server-side technique that optimizes GPU utilization by grouping individual inference requests into batches at runtime, enhancing throughput but potentially increasing latency. This method is crucial for inference servers like Triton, TorchServe, and Ray Serve, which are designed to efficiently handle batch requests in machine learning and deep learning frameworks. The process trades latency for throughput by leveraging the GPU's memory bandwidth, allowing weight-loading operations to be shared across multiple inputs. Static batching relies on client-side batch assembly, while dynamic batching waits briefly to form larger batches on the server, balancing latency and throughput with a configurable timeout window. Continuous batching addresses limitations with autoregressive large language models (LLMs) by scheduling at the iteration level, reducing wait times for short requests. Semantic caching complements batching by intercepting repeated requests before they reach the inference queue, notably improving performance and cost efficiency for repetitive tasks. Redis provides a real-time platform for semantic caching, integrating vector search to recognize equivalent queries, which further reduces unnecessary GPU workload and enhances overall system efficiency.