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NVIDIA Xid 48. ECC Uncorrectable Memory Error

Xid 48 is NVIDIA's code for uncorrectable GPU memory errors. It crashes training and corrupts model weights. Denpex correlates Xid 48 events with NCCL failures to identify the originating GPU and trigger automated draining.

Quick answer

Xid 48 is NVIDIA's code for uncorrectable GPU memory errors.

Hardware#xid#ecc#gpu#hardware#nvidia#memory

What this failure is

NVIDIA Xid 48. ECC Uncorrectable Memory Error is a Hardware failure seen during ML training runs. Xid 48 is NVIDIA's code for uncorrectable GPU memory errors. It crashes training and corrupts model weights. Denpex correlates Xid 48 events with NCCL failures to identify the originating GPU and trigger automated draining. Common tags: Xid, Ecc, Gpu, Hardware.

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Why it happens (the mechanism)

Single-bit ECC errors accumulate silently until a double-bit ECC error triggers Xid 48. CUDA kernels read corrupted data and propagate errors through the computation. Xid 48 can corrupt model weights mid-training, producing silent data corruption. The failing GPU must be identified and drained immediately. Taken together, these mechanisms explain why the failure is reproducible, why it tends to surface on specific workloads or scales, and why generic mitigation attempts often fall short without addressing the underlying cause.

What you'll observe

  • Training job terminates with Xid 48 in dmesg or nvidia-smi
  • The same training config works on some GPUs but not others
  • Silent data corruption: training continues but produces garbage output

Common symptoms and what they mean

SymptomWhy it happens
dmesg: Xid 48 (hardware damage) on GPU XSingle-bit ECC errors accumulate silently until a double-bit ECC error triggers Xid 48
nvidia-smi -q -d ECC shows non-zero uncorrectable error countCUDA kernels read corrupted data and propagate errors through the computation
Loss degrades silently without an explicit crash as Xid 48 corrupts weights during trainingXid 48 can corrupt model weights mid-training, producing silent data corruption
ECC error count increases over time on specific GPUsThe failing GPU must be identified and drained immediately

Which systems are affected

  • Multi-node GPU training with 8+ GPUs per node
  • Aging GPU hardware (V100, A100 after heavy use
  • Training on PCIe-attached GPUs (more ECC-prone than SXM/NVLink-connected GPUs
  • High-utilization clusters running 24/7 training

How to confirm this is the problem

Use this checklist to test the hypothesis against a small reproduction. No single line proves the root cause, so preserve the preceding events and compare one variable at a time.

  • Reproduce the failure from a clean checkpoint/seed: the symptom must appear without warm-up state from a previous run.
  • Verified signal present: dmesg: Xid 48 (hardware damage) on GPU X
  • Verified signal present: nvidia-smi -q -d ECC shows non-zero uncorrectable error count
  • Verified signal present: Loss degrades silently without an explicit crash as Xid 48 corrupts weights during training
  • Verified signal present: ECC error count increases over time on specific GPUs
  • A targeted fix from the "How to fix it" section eliminates or substantially reduces the symptom within one validation pass.

The fix and the prevention pattern

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Xid 48 in context

Xid 48 is one of a small set of codes the NVIDIA driver uses to report GPU faults, and the number is most of the diagnosis: it tells you whether you are looking at your own code, the driver, or a board that needs replacing.

Compare every Xid code side by side

Diagnose this failure in VS Code

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Root cause

  • Single-bit ECC errors accumulate silently until a double-bit ECC error triggers Xid 48
  • CUDA kernels read corrupted data and propagate errors through the computation
  • Xid 48 can corrupt model weights mid-training, producing silent data corruption
  • The failing GPU must be identified and drained immediately

The fix and how to prevent it

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