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

Abstract

RAPID-LLM is a unified performance modeling framework for large language model (LLM) training and inference on GPU clusters. It couples a DeepFlow-based frontend that generates hardware-aware, operator-level Chakra execution traces from an abstract LLM specification (model shape, batch/sequence settings, training vs. inference, and hybrid parallelism choices) with an extended Astra-Sim backend that executes those traces on explicit multi-dimensional network topologies with congestion-aware routing and support for degraded and faulty links. The frontend assigns per-operator latency using a tile-based model that accounts for SM under-utilization and multi-level memory traffic (SRAM/ L2/ HBM), and prunes memory-infeasible configurations using an activation-liveness traversal under recomputation, parallelism and ZeRO/FDSP sharding policies. Across A100-based validation cases, RAPID-LLM predicts Llama inference step latency and GPT-scale training time per batch within 10.4\% relative to published measurements, and matches ns-3 packet-level results within 8\% on representative communication workloads. Case studies demonstrate how RAPID-LLM enables fast, exhaustive sweeps over hybrid-parallel configurations, quantifies sensitivity to soft link faults under realistic routing and congestion, and evaluates hypothetical GPU design variants including HBM bandwidth throttling effects.