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

Short summary

Static batching processes fixed-size request groups inefficiently, leaving GPUs idle. Continuous batching uses dynamic scheduling and ragged batches so requests can exit independently, significantly improving throughput and resource utilization in multi-user LLM serving.

• •Static batching wastes GPU cycles waiting for slow requests within fixed-size groups
• •Continuous batching allows flexible request completion without blocking peers
• •Measurable efficiency gains for production LLM inference systems

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