Deferred prefill for throughput maximization in LLM inference
Moonmoon Mohanty, Gautham Bolar, Preetam Patil, UmaMaheswari C. Devi, Felix George, Pratibha Moogi, Parimal Parag · 2025
Large language models (LLMs) have led to ground-breaking improvements in the capabilities of generative AI (Gen-AI) applications, leading to their increased adoption, which in turn is leading to increasing volumes of user requests at LLM inference deployments. The existing common implementations of LLM inference engines perform a new prefill every time there is a prompt departure. We analytically model the inference system for a fixed batch size with large rate of prompt arrivals and scheduling prefills after a fixed number of prompt departures. We characterize the throughput of the system as number of prompts departing per unit time for different thresholds. We observe that to maximize throughput, there exists an optimal threshold on the number of prompt departures. We verify this observation with vLLM experiments, and compare the optimal threshold predicted theoretically to the experimentally observed ones.