Serving Multiple Users at Once: How Continuous Batching Keeps LLM Inference Efficient

Static batching wastes GPU cycles by padding short sequences to match the longest in a batch and delaying results until the entire batch finishes. Continuous batching solves this with two techniques: dynamic scheduling, which frees slots and admits new requests the moment a sequence finishes, and ragged batching, which concatenates variable-length sequences into a single tensor using a block-diagonal causal attention mask to prevent cross-sequence attention. A full Python implementation using GPT-2 and Hugging Face Transformers demonstrates both approaches, showing identical outputs but significantly better throughput with continuous batching.