{"ID":2826363,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.19606","arxiv_id":"2512.19606","title":"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.","short_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. i...","url_abs":"https://arxiv.org/abs/2512.19606","url_pdf":"https://arxiv.org/pdf/2512.19606v1","authors":"[\"George Karfakis\",\"Faraz Tahmasebi\",\"Binglu Chen\",\"Lime Yao\",\"Saptarshi Mitra\",\"Tianyue Pan\",\"Hyoukjun Kwon\",\"Puneet Gupta\"]","published":"2025-12-22T17:42:51Z","proceeding":"cs.PF","tasks":"[\"cs.PF\",\"cs.DC\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}