Static vs. Dynamic vs. Continuous Batching in LLM Inference

The article contrasts static, dynamic, and continuous batching strategies for LLM inference, explaining that static batching fixes a batch size and waits for all slots before processing, dynamic batching aggregates requests up to a configurable threshold to better match traffic patterns, and continuous batching streams tokens as they arrive to minimize queue delays. It highlights that static batching can achieve peak throughput but incurs higher per‑request latency, dynamic batching offers a balance by adapting to load variations, and continuous batching further reduces latency at the cost of

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