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

[2512.19606] RAPID-LLM: Resilience-Aware Performance analysis of Infrastructure for Distributed LLM Training and Inference Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Performance arXiv:2512.19606 (cs) [Submitted on 22 Dec 2025 ( v1 ), last revised 15 Aug 2026 (this version, v2)] Title: RAPID-LLM: Resilience-Aware Performance analysis of Infrastructure for Distributed LLM Training and Inference Authors: George Karfakis , Lime Yao , Binglu Chen , Faraz Tahmasebi , Saptarshi Mitra , Tianyue Pan , Hyoukjun Kwon , Puneet Gupta View a PDF of the paper titled RAPID-LLM: Resilience-Aware Performance analysis of Infrastructure for Distributed LLM Training and Inference, by George Karfakis and 7 other authors View PDF HTML (experimental) 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. Subjects: Performance (cs.PF) ; Distributed, Parallel, and Cluster Computing (cs.DC) Cite as: arXiv:2512.19606 [cs.PF] (or arXiv:2512.19606v2 [cs.PF] for this version) https://doi.org/10.48550/arXiv.2512.19606 Focus to learn more arXiv-issued DOI via DataCite Submission history From: George Karfakis [ view email ] [v1] Mon, 22 Dec 2025 17:42:51 UTC (1,438 KB) [v2] Sat, 15 Aug 2026 02:49:59 UTC (1,715 KB) Full-text links: Access Paper: View a PDF of the paper titled RAPID-LLM: Resilience-Aware Performance analysis of Infrastructure for Distributed LLM Training and Inference, by George Karfakis and 7 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.PF < prev | next > new | recent | 2025-12 Change to browse by: cs cs.DC References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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