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

Authors: 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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Artificial Intelligence (cs.AI)

Cite as:

arXiv:2610.37989 [cs.AI]

(or

arXiv:2610.37989v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.37989

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From: Ankur Mallick [ view email ]

[v1]

Tue, 27 Oct 2026 21:36:03 UTC (261 KB)

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