What is silent data corruption and why is it particular...

Silent data corruption (SDC) is a failure mode where hardware produces numerically plausible but incorrect gradient values. Because it does not trigger explicit errors like crashes or NaNs, it is insidious; the training loss may appear normal, masking the fact that the model is being poisoned by corrupted signals over thousands of iterations. Detection requires out-of-band validation, such as canary batches or cross-replica gradient comparisons, rather than relying solely on in-band loss monitoring.