LLM Checkpoint/Restore I/O Optimization
Published:Dec 30, 2025 23:21
•1 min read
•ArXiv
Analysis
This paper addresses the critical I/O bottleneck in large language model (LLM) training and inference, specifically focusing on checkpoint/restore operations. It highlights the challenges of managing the volume, variety, and velocity of data movement across the storage stack. The research investigates the use of kernel-accelerated I/O libraries like liburing to improve performance and provides microbenchmarks to quantify the trade-offs of different I/O strategies. The findings are significant because they demonstrate the potential for substantial performance gains in LLM checkpointing, leading to faster training and inference times.
Key Takeaways
- •Checkpoint/restore is a major I/O bottleneck in LLM training and inference.
- •Kernel-accelerated I/O libraries like liburing can improve performance.
- •Aggregation and coalescing strategies are crucial for optimizing I/O.
- •The proposed approach significantly outperforms existing LLM checkpointing engines.
Reference
“The paper finds that uncoalesced small-buffer operations significantly reduce throughput, while file system-aware aggregation restores bandwidth and reduces metadata overhead. Their approach achieves up to 3.9x and 7.6x higher write throughput compared to existing LLM checkpointing engines.”