TierCheck: Tiered Checkpointing for Fault Tolerance in Large Language Model Training
Large Language Model (LLM) training is frequently interrupted by a heterogeneous spectrum of failures, from common GPU crashes to catastrophic cluster-wide outages. Existing checkpointing systems rely on monolithic, single-tier storage backend, forcing a trade-off between state-saving overhead and recovery speed. We propose TierCheck, a cluster-aware tiered checkpointing system that aligns storage placement with failure heterogeneity. TierCheck adopts a three-tier design that maintains lightweight differential checkpoints in local and peer memory for fast localized recovery, while asynchronously migrating heavyweight base checkpoints to remote persistent storage. It also ensures strict gl...
| Authors | Shujie Han, Feng Jiang, Patrick P. C. Lee et al. |
|---|---|
| Primary category | cs.DC |
| Published | 18 May 2026 |
| Last updated at source | 18 May 2026 |
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