Cases2 cases

1 to 2 of 2

1. IL-0416 Defective GPUs silently corrupted ByteDance's LLM training, showing as loss spikes, NaN and misleading code errors

   ByteDance2026

   Symptoms

   In ByteDance's production LLM training cluster, GPUs with hardware defects silently corrupted computation without any error signal. Training jobs either crashed with errors that looked like software bugs, such as a shape mismatch or an out-of-bound access, or kept running with sudden loss spikes until engineers stopped them by hand. Of the 23 SDC-defective GPUs the paper characterises, 13 surfaced this way during active LLM training and 10 were found in offline stress tests that used online workloads and failed numerical validation checks.

   Training divergence or loss spikeTraining job failure

   Root cause

   Hardware failure

   Component

   GPU hardware and memory

   Contributing factor

   Monitoring blind spot

   Detection

   Corruptions surfaced as crashes, NaN or loss spikes in training jobs, or as failed numerical validation in offline stress tests that used online workloads; ECC, thermal and other hardware signals did not show them.

   Remediation

   • Halted the training and took the affected nodes offline to confirm the defective GPU
   • Isolated the suspicious data-parallel group and resumed the training job from a checkpoint on healthy machines
   • Confirmed the defective GPU offline by replaying the alert-triggering trace and checking it with hardware tools
   • Sent the confirmed defective GPUs to hardware repair
   • Enabled every deterministic setting in production training, including torch.use_deterministic_algorithms, so anomalies could be replayed bit for bit

   Lessons

   SDC diagnosis has to be part of the training control plane rather than a separate hardware test: the most useful step during an incident is to take suspicious machines out of the training path quickly and leave device-level confirmation to offline analysis. Replaying the exact workload works better than synthetic stress tests, because many defective GPUs fail only on the kernels, data ranges and communication schedules that triggered the original anomaly, and because occurrence rates vary so widely a fixed-duration stress test is not a reliable acceptance check; production systems should keep the alert-triggering trace. Deterministic training turns ambiguous symptoms such as loss spikes into reproducible comparisons, and SDCs appear throughout the hardware lifecycle, so GPUs need continuous monitoring rather than checks only when new.

   View case
2. IL-0479 Accelerator nodes with silent data corruption changed LLM training outputs, gradients and weights with no error signal

   Amazon Web Services2025

   Symptoms

   Machine learning accelerator nodes that a cloud computing platform's automated fleet management had removed from production produced wrong computation results without any error signal. When the same LLM training ran on one of them and on a healthy node, the training loss was identical, but submodule outputs, gradients and model weights differed.

   Training divergence or loss spike

   Root cause

   Hardware failure

   Component

   –

   Contributing factor

   Monitoring blind spot

   Detection

   The fleet management tests found the nodes: collective communication and compute unit stress tests and a small LLM training run compared with golden values, run after installation and when a node returns from customer use. Training loss showed nothing on most nodes.

   Remediation

   • Kept the nodes that failed the fleet's SDC tests out of production availability
   • Sent the nodes held for the study for repair before returning them to the production pool

   Lessons

   SDC can change training for an unknown period before any loss spike appears, so watching the training loss is not enough. ABFT did not detect it reliably; the authors' possible explanations are that mismatches were too rare and small to exceed floating-point error bounds, that ABFT lowered accelerator utilization and with it the SDC rate, and that SDC may arise outside matrix multiplication; the authors suggest recomputation, for example a shadow data-parallel replica whose gradients are compared with a target rank's during the gradient all-reduce, and point at reducing the sharpness of the loss surface to limit the damage.

   View case

By root cause1