Exploring Next
Exploring Next — Ep 445 w/ Justy & Cody — Serving Multiple Users at Once: How Continuous Batching Keeps LLM Inference Efficient - MachineLearningMastery.com
Continuous batching is a scheduling technique that keeps LLM inference servers from wasting GPU cycles on padding. Instead of forcing short requests to wait for long ones in a fixed batch, continuous batching frees up slots the moment a request finishes and admits new work immediately, eliminating idle padding tokens and improving throughput.