Serving Multiple Users at Once: How Continuous Batching Keeps LLM Inference Efficient https://machinelearningmastery.com/serving-multiple-users-at-once-how-continuous-batching-keeps-llm-inference-efficient/ This article is divided into four parts; they are: • The Problem with Static Batching • Code Example of Static Batching • Continuous Batching: Dynamic Scheduling and Ragged Batching • Full Implementation The simplest way to serve multiple requests together is to use static ...
Serving Multiple Users at Once: How Continuous Batching Keeps LLM Inference Efficient - MachineLearningMastery.com
In the previous article, we saw how a language model processes a prompt during prefill, then generates tokens one at a time during decode, and uses KV cache to avoid repeated computation. In the real world, inference servers handle hundreds or thousands of requests at the same time. How a server schedules those requests determines […]
