Reliable and Resilient Collective Communication Library for LLM Training and Serving
In brief:
Modern ML training and inference now span tens to tens of thousands of GPUs, where network faults can waste 10--15\% of GPU hours due to slow recovery.
Common network errors and link fluctuations trigger timeouts that often terminate entire jobs, forcing expensive checkpoint rollback during training and request reprocessing during inference.
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About this source
Source
arXiv cs.LG
Type
Research Preprint
Published
Credibility
Peer-submitted research paper on arXiv
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arXiv cs.LG·Research Preprint·Primary Source·
Reliable and Resilient Collective Communication Library for LLM Training and Serving
TL;DR
Modern ML training and inference now span tens to tens of thousands of GPUs, where network faults can waste 10--15\% of GPU hours due to slow recovery.
Scan abstract → experiments → limitations. Also: check API docs for breaking changes; verify benchmark methodology.
Full Analysis
May affect how AI can be used.
Common network errors and link fluctuations trigger timeouts that often terminate entire jobs, forcing expensive checkpoint rollback during training and request reprocessing during inference.
