How a Single Slow GPU Can Halt Thousands During Large-Scale AI Training

In synchronous distributed LLM training, all GPUs must complete each step before the next begins, meaning the entire job stalls until the slowest worker finishes. This 'straggler problem' means total step time is determined by the maximum worker time, not the average, making even a small slowdown on one GPU disproportionately costly. At scales of thousands of GPUs, the issue compounds statistically — more workers mean more chances for at least one to lag. Research from real training clusters shows that sequence-length imbalance, workload skew, and garbage-collection pauses are common culprits, not just hardware failures. The economic impact is significant, as large training runs can waste substantial compute time simply waiting at synchronization barriers.