Is this a hardware or a software issue?

Software failures can look like either layer depending on the symptom. The discriminator is the resolution of the symptom to the recommended fix: if applying the steps under "How to fix it" eliminates the symptom, this is a software/configuration class issue regardless of the hardware it surfaced on. Repeat the fix on a fresh node to confirm.

How long does the fix typically take?

Most resolutions from this entry take 10 to 30 minutes of engineer time once the diagnosis is confirmed. Long fixes come from skipping the diagnosis step and chasing apparent symptoms (validation drift, memory growth, slow throughput) instead of the actual root cause listed above. Apply the fix in a small experiment first, then roll it forward.

Does this affect inference or only training?

Primarily this is a software failure observed during training, but the same misconfiguration can leak into downstream evaluation and serving. Treat the prevention checklist as a guardrail: any pipeline that touches a trained checkpoint should re-validate the failure mode before promoting the model.

What's the cheapest way to confirm this is the problem?

Cheapest signal: reproduce the symptom on a 1-GPU toy run with the same configuration. If the toy run exhibits the same behavior, the cause is the configuration rather than the cluster. If only the multi-GPU run is affected, the failure is a distributed interaction, so PyTorch is the most likely layer to inspect.

Can the Denpex platform detect this automatically?

Yes. Denpex correlates the symptoms in this entry against your training logs in real time and surfaces the failure taxonomy, root cause, and exact remediation command on the console. Three full diagnoses are included free during signup, and the encyclopedia entry you are reading is generated from the same signal library that powers the platform.

What related failures should I check next?

Closely related failures cluster around #Xid Error. After applying this fix, validate against the related entries below to confirm no overlapping cause is masking as this one. Engineers routinely discover that the dominant failure mode is actually a second, sibling failure compounded on top.

Will this recur after the fix?

Applied once with the prevention checklist, this failure does not recur under normal operation. The recurring variant most engineers encounter is the re-emergence of the same symptom on a different model or stack after Xid Error is upgraded. Pin compatible versions and rerun the prevention smoke test on every dependency change.

Is there a fingerprint I can monitor?

Yes. Training script crashes abruptly with a CUDA illegal memory access error. is the highest-signal fingerprint. Wire a metric or alert on that signal (or its absence after the fix) so you can detect the recurrence automatically. The Denpex platform encodes this exact fingerprint and surfaces it directly in the dashboard.

What is the official documentation that covers this?

Reference documentation is listed in the References section at the bottom of this entry. The most authoritative URLs are the upstream framework docs and the issue trackers cited; the Denpex entry synthesizes both into the executive, engineer, and on-call perspectives so each audience can act without reading every linked source.

Does this entry include working code?

Yes. The "Code Examples" section contains copy-paste-ready snippets that target the most common stacks (PyTorch, Hugging Face Trainer, DeepSpeed, Megatron-LM, vLLM/Transformers). Each snippet is annotated with why it works so you can adapt the pattern to your own framework without losing fidelity.

Xid 13: Graphics Engine Exception due to Kernel Out-of-Bounds Access

Q: What does this failure look like in production?

In production this failure typically appears during the first tens-to-hundreds of training steps: loss curves become non-monotonic, validation metrics plateau above the baseline, and downstream checkpoints either diverge or fail to improve. Engineers frequently misread it as data drift when the underlying mechanism is a custom pytorch c++ / cuda kernel (or a bug in a framework like tensorrt/deepspeed) calculates an incorrect thread index or memory offset, resulting in a read or write operation past the allocated bounds of a tensor. the nvidia hardware traps this memory violation (warp illegal address) and halts the gpu context, logging an xid 13 error.. Validating against the symptom list in this entry is the fastest way to confirm the diagnosis.

Q: How does this scale from 1 GPU to 1000 GPUs?

At single-GPU scale the symptom usually looks like slow convergence or a single anomalous loss value. At 64+ GPU scale, distributed interactions amplify a custom pytorch c++ / cuda kernel (or a bug in a framework like tensorrt/deepspeed) calculates an incorrect thread index or memory offset, resulting in a read or write operation past the allocated bounds of a tensor. the nvidia hardware traps this memory violation (warp illegal address) and halts the gpu context, logging an xid 13 error. through collectives, and the failure can manifest as a full job abort. Always reproduce the fix at the smallest cluster configuration that still exhibits the symptom before promoting to production scale.

A custom PyTorch C++ / CUDA kernel (or a bug in a framework like TensorRT/DeepSpeed) calculates an incorrect thread index or memory offset, resulting in a read or write operation past the allocated bounds of a tensor. The NVIDIA hardware traps this memory violation (Warp Illegal Address) and halts the GPU context, logging an Xid 13 error.

Quick answer

Symptom: dmesg shows NVRM Xid 13 Graphics Engine Exception.
Root cause: A custom PyTorch C++ / CUDA kernel (or a bug in a framework like TensorRT/DeepSpeed) calculates an incorrect thread index or memory offset, resulting in a read or write operation past the allocated bounds of a tensor. The NVIDIA hardware traps this memory violation (Warp Illegal Address) and halts the GPU context, logging an Xid 13 error.
Recommended fix: Fix the CUDA Kernel Bounds Checking if (idx < target_size) { out[idx] = in[idx]; } Ensures the CUDA kernel threads do not attempt to access memory beyond the allocated array size.
How Denpex helps: Denpex matches Xid 13: Graphics Engine Exception due to Kernel Out-of-Bounds Access across every rank in a distributed run and reports which rank failed first, so you act on the initiating node instead of the loudest one.

Software#Xid Error

What this failure is

Xid 13: Graphics Engine Exception due to Kernel Out-of-Bounds Access is a Software failure seen during ML training runs. A custom PyTorch C++ / CUDA kernel (or a bug in a framework like TensorRT/DeepSpeed) calculates an incorrect thread index or memory offset, resulting in a read or write operation past the allocated bounds of a tensor. The NVIDIA hardware traps this memory violation (Warp Illegal Address) and halts the GPU context, logging an Xid 13 error. Common tags: Xid Error.

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Why it happens (the mechanism)

Because Xid 13 is a driver-level fault (Graphics Engine Exception), users often mistake this for failing GPU hardware or a driver bug, replacing GPUs unnecessarily rather than debugging their custom CUDA code.

What you'll observe

NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: Warp Illegal Address
NVRM: Xid (PCI:0000:01:00): 13, Graphics Exception: ESR 0x404490=0x80000000
RuntimeError: CUDA error: an illegal memory access was encountered

Common symptoms and what they mean

Symptom	Why it happens
Training script crashes abruptly with a CUDA illegal memory access error.	A custom PyTorch C++ / CUDA kernel (or a bug in a framework like TensorRT/DeepSpeed) calculates an incorrect thread index or memory offset, resulting in a read or write operation past the allocated bounds of a tensor. The NVIDIA hardware traps this memory violation (Warp Illegal Address) and halts the GPU context, logging an Xid 13 error.
dmesg shows NVRM Xid 13 Graphics Engine Exception.	A custom PyTorch C++ / CUDA kernel (or a bug in a framework like TensorRT/DeepSpeed) calculates an incorrect thread index or memory offset, resulting in a read or write operation past the allocated bounds of a tensor. The NVIDIA hardware traps this memory violation (Warp Illegal Address) and halts the GPU context, logging an Xid 13 error.
Subsequent PyTorch operations fail until the process is restarted.	A custom PyTorch C++ / CUDA kernel (or a bug in a framework like TensorRT/DeepSpeed) calculates an incorrect thread index or memory offset, resulting in a read or write operation past the allocated bounds of a tensor. The NVIDIA hardware traps this memory violation (Warp Illegal Address) and halts the GPU context, logging an Xid 13 error.

Which systems are affected

PyTorch
CUDA
NVIDIA Driver

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.

✓Run the training script wrapped in compute-sanitizer: compute-sanitizer --tool memcheck python train.py
✓Look for 'Invalid __global__ read of size X' or 'Invalid __global__ write of size X' in the sanitizer output.
✓Set export CUDA_LAUNCH_BLOCKING=1 to get an accurate Python stack trace pointing to the failing operation.

Searchable error signature

search key

dmesg shows NVRM Xid 13 Graphics Engine Exception.
RuntimeError: CUDA error: an illegal memory access was encountered

Use this text as a lookup key in logs and upstream issue trackers. It is not presented as a captured customer log. Confirm the cause from your own preceding events, versions, configuration and the cited references.

The fix and the prevention pattern

The root cause is on this page and stays free. A free account adds the exact remediation steps, saved history, and the fix on every entry in the encyclopedia.

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Xid 13 in context

Xid 13 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.

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Root cause

A custom PyTorch C++ / CUDA kernel (or a bug in a framework like TensorRT/DeepSpeed) calculates an incorrect thread index or memory offset, resulting in a read or write operation past the allocated bounds of a tensor. The NVIDIA hardware traps this memory violation (Warp Illegal Address) and halts the GPU context, logging an Xid 13 error.

The fix and how to prevent it

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References

Don't just read the fix, diagnose your run

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