Invisible hardware glitches in GPUs are likely corrupting your LLM training without ever crashing the system.
April 15, 2026
Original Paper
LLM-PRISM: Characterizing Silent Data Corruption from Permanent GPU Faults in LLM Training
arXiv · 2604.10390
The Takeaway
LLM-PRISM characterizes 'Silent Data Corruption' (SDC) from permanent GPU faults. These hardware defects don't cause a crash but subtly perturb gradients, leading to catastrophic model divergence in some cases. We used to assume that if the system is running, the math is correct. This paper proves that's a dangerous assumption. For teams training at scale, this means you could waste millions of dollars on a 'ruined' training run because of an invisible hardware quirk. It highlights a critical need for 'precision-aware' training and better hardware-level verification in the AI era.
From the abstract
Large-scale LLM training is increasingly susceptible to hardware defects stemming from manufacturing escapes and silicon aging. These defects manifest as Silent Data Corruption (SDC) that perturb gradients and parameters throughout the training process. We present LLM-PRISM, a methodology to characterize LLM pre-training resilience to hardware faults. LLM-PRISM couples RTL-level GPU fault simulation with a stochastic injection engine embedded in Megatron-LM. Through 7,664 training runs across FP