RAPID-LLM: Resilience-Aware Performance analysis of Infrastructure for Distributed LLM Training and Inference

Abstract

RAPID-LLM is a unified performance modeling framework for distributed large language model (LLM) training and inference on GPU clusters, without relying on deployment-specific traces or expensive cycle-level simulation for exploration. From a workload and hardware specification, it builds hardware-aware operator-level execution models that capture tiling, memory-hierarchy effects, communication, and memory feasibility under hybrid parallelism. Its backend simulates explicit multidimensional interconnects with congestion-aware routing and support for degraded and failed links, enabling scalable what-if analysis across topology, mapping, and hardware design choices. Across 124 evaluation cases spanning inference and dense, fully sharded, and mixture-of-experts training on A100 and H100 GPUs, RAPID-LLM achieves an overall mean absolute percentage error (MAPE) of 10.0\%. Its network predictions stay within 8\% of ns-3 on representative communication patterns. Case studies demonstrate how RAPID-LLM enables fast, systematic sweeps over hybrid-parallel configurations, quantifies sensitivity to link faults under realistic routing and congestion, and evaluates hypothetical GPU design variants including 3D-stacked HBM-on-GPU scenarios.