Continuous Batching in LLM Inference Servers (EXPLAINED)
Hard Llama/inference question on continuous batching and GPU efficiency.
TL;DR — Quick Answer
Continuous batching lets the scheduler admit new requests as others finish tokens, instead of waiting for an entire static batch to complete. This raises GPU utilization and throughput under mixed prompt lengths and streaming workloads.
The Interview Question
Explain continuous batching in servers like vLLM. Why does it improve GPU utilization compared to static batching?
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