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 […]

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