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1. IL-0478 A failed GPU network adapter made NCCL silently reroute through GPU 0's adapter and slowed MoE training steps

   Amazon2026

   Symptoms

   In Amazon's large-scale foundation model training, training steps took about 0.3 seconds longer (8.7 s instead of 8.4 s) when a node had a GPU whose network adapter had failed. No hardware failure alert fired, and standard node checks such as GPU burn-in and short NCCL tests rarely catch such nodes.

   Throughput degradation

   Trigger

   The network adapter of one GPU on a node failed.

   Root cause

   Hardware failure

   Component

   NCCLNIC, cable and switch port link

   Contributing factor

   Monitoring blind spot

   Detection

   No hardware alert or standard health check showed the fault. It showed as slower training steps, in the per-node network device status and transmission rate that Guard monitors, and as elevated latency or reduced bandwidth in a 2-node sweep.

   Remediation

   • Resolved the degraded communication path, which brought training step time from 8.7 s back to 8.4 s

   Action items

   • Monitor each node's network device status continuously to find and isolate nodes whose traffic NCCL rerouted
   • Remove a node flagged by online monitoring from the healthy node pool and schedule it for offline verification
   • Run a multi-node sweep that stresses cross-node collective communication to expose degraded links and misrouted traffic
   • Trigger node sweeps after anomalies found by online monitoring and after repairs, before a node re-enters production

   Lessons

   NCCL's transparent rerouting keeps both the job and NCCL tests working while it hides a failed adapter, so checks that verify functional correctness do not find the node. Amazon relies on performance-aware monitoring during training, with step time as the main signal, and on offline verification under realistic load before nodes return to service.

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2. IL-0421 Errors, mostly GPU faults, crashed Alibaba LLM training jobs as NCCL errors; a job lost 31.19% of its time in June 2023

   Alibaba Group2025

   Symptoms

   Large LLM training jobs on Alibaba's AI clusters crashed again and again and had to restart from the last valid checkpoint. One of the largest jobs, on 4,096 GPUs in a newly deployed cluster, crashed 40 times in one month, and most crashes reached users only as "NCCL Errors".

   Training job failureNCCL timeoutGPU unavailable

   Root cause

   Hardware failure

   Component

   GPU hardware and memoryNCCLPyTorch

   Contributing factor

   Monitoring blind spotUnverified assumption

   Detection

   Before C4D, users found crashes through NCCL errors and could stay unaware of a stalled job for up to 30 minutes until the PyTorch elastic agent killed it; C4D later detects hangs and slowdowns from collective communication timings within tens of seconds.

   Remediation

   • Allocated 64 backup GPUs on 8 servers for every 1024 GPUs on 128 servers, so that parallel training on any 128 servers of the 136-server pool keeps the same communication and performance
   • Deployed C4D to detect faulty nodes automatically, isolate them and restart the job from the most recent valid checkpoint
   • Saved checkpoints more often, every 10 minutes, to cut the work lost after each crash
   • Enhanced cooling with higher fan speeds and better air conditioning to keep GPUs from overheating
   • Identified and enhanced the most vulnerable components of the system, lowering the average error rate 3.33 times

   Action items

   • Take load variation into account in C4D for more precise slow detection

   Lessons

   Most crash causes are confined to specific nodes or devices, so isolating the faulty component quickly and restarting, while leaving root cause analysis for offline work, removes most of the error-induced downtime. Because synchronous training makes many different faults look like the same communication error, diagnosis has to use the regular rhythm of the workers at synchronisation points rather than the error message, and frequent checkpoints matter because error rates in large clusters are high.

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