Fault-Tolerant Collective Communication for LLMs
Research Paper#LLM Training and Inference, Fault Tolerance, Collective Communication🔬 Research|Analyzed: Jan 3, 2026 06:11•
Published: Dec 31, 2025 18:53
•1 min read
•ArXivAnalysis
This paper addresses a critical problem in large-scale LLM training and inference: network failures. By introducing R^2CCL, a fault-tolerant communication library, the authors aim to mitigate the significant waste of GPU hours caused by network errors. The focus on multi-NIC hardware and resilient algorithms suggests a practical and potentially impactful solution for improving the efficiency and reliability of LLM deployments.
Key Takeaways
- •Addresses the problem of network failures in large-scale LLM training and inference.
- •Introduces R^2CCL, a fault-tolerant communication library.
- •Leverages multi-NIC hardware for failover and load redistribution.
- •Demonstrates significant performance improvements over existing baselines (AdapCC and DejaVu).
- •Shows low overheads (less than 1% for training, less than 3% for inference) under NIC failures.
Reference / Citation
View Original"R$^2$CCL is highly robust to NIC failures, incurring less than 1% training and less than 3% inference overheads."