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arXiv · 2610.37989

GAUSS: A Workload-Aware Analytical and Monte-Carlo Simulator for LLM Inference Serving

Srikant Bharadwaj·Ankur Mallick·Spyridon Mastorakis·Anjaly Parayil·Dhruv Deshmukh·Victor Ruehle
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Abstract

Large language model (LLM) inference has become an operational workload that fleets must provision, monitor, and bill against latency service-level objectives (SLOs). A single throughput number cannot answer the questions operators actually face, because generative serving couples two phases with very different service characteristics, continuous batching reshapes the effective service process, and user-visible tail latency can degrade long before aggregate throughput saturates. We present GAUSS, a workload-aware analytical and Monte-Carlo simulator that predicts the full distributions of time-to-first-token (TTFT) and time-between-tokens (TBT), together with the sustainable request rate, for a given workload, engine, and accelerator. GAUSS pairs calibrated, phase-specific latency surfaces for prefill and decode with a Lindley-recurrence model of queueing under stochastic arrivals and estimates performance by ensemble Monte-Carlo sampling rather than by replaying a single trace. This lets GAUSS reason about workloads, routing policies, and hardware that have never been deployed, and to capture the latency tails that trace-driven simulators systematically under-sample. We describe the model and show how it supports operating-point selection, traffic segmentation, and shortest-remaining-processing-time scheduling capacity questions that trace-driven simulators cannot address.

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Srikant Bharadwaj, Ankur Mallick, Spyridon Mastorakis, Anjaly Parayil, Dhruv Deshmukh, Victor Ruehle. 2026-10-27. GAUSS: A Workload-Aware Analytical and Monte-Carlo Simulator for LLM Inference Serving. https://arxiv.org/abs/2610.37989

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