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 driver-level bug in the nvidia 535 series causes the mma.sp ptx instruction on hopper architecture to silently corrupt data during matrix multiplication.. Validating against the symptom list in this entry is the fastest way to confirm the diagnosis.

Is this a hardware or a software issue?

CUDA 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 cuda 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 CUDA 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 #hopper, #sdc, #user-report. 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 hopper is upgraded. Pin compatible versions and rerun the prevention smoke test on every dependency change.

Is there a fingerprint I can monitor?

Yes. Training loss spikes or inference outputs degrade with no logged errors. 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.

Hopper mma.sp Silent Data Corruption (SDC)

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 driver-level bug in the nvidia 535 series causes the mma.sp ptx instruction on hopper architecture to silently corrupt data during matrix multiplication. 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.

Using custom PTX kernels that leverage the mma.sp sparse tensor core instruction on Hopper GPUs causes silent calculation errors on the 535-series NVIDIA drivers.

Quick answer

Using custom PTX kernels that leverage the mma.

Root cause: A driver-level bug in the NVIDIA 535 series causes the mma.sp PTX instruction on Hopper architecture to silently corrupt data during matrix multiplication.
Recommended fix: Run nvidia-smi -r to clear the invalid operational state.
How Denpex helps: Denpex matches Hopper mma.sp Silent Data Corruption (SDC) 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.

CUDA#hopper#sdc#user-report

What this failure is

Hopper mma.sp Silent Data Corruption (SDC) is a CUDA failure seen during ML training runs. Using custom PTX kernels that leverage the mma.sp sparse tensor core instruction on Hopper GPUs causes silent calculation errors on the 535-series NVIDIA drivers. Common tags: Hopper, Sdc, User Report.

Live diagnosis, no signup

Is this what broke your run? Paste your log.

You're reading about Hopper mma.sp Silent Data Corruption (SDC). Paste your own crash log or traceback below and get the real root cause for YOUR run, not this generic entry. No account, no card. Logs are masked at ingress and never saved to account history.

training_logs.txt

No log to hand? Try one:

3 free diagnoses/day

Want 14 days on the Scale plan?

Request an evaluation code. A verified workplace organization activates up to 50 diagnoses a day, alerts, history, and follow-up questions. No credit card or automatic subscription.

Evaluate one incident

Why it happens (the mechanism)

A driver-level bug in the NVIDIA 535 series causes the mma.sp PTX instruction on Hopper architecture to silently corrupt data during matrix multiplication. 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

Mathematical outputs from custom CUDA kernels utilizing sparse tensor cores are incorrect without throwing any errors.

Common symptoms and what they mean

Symptom	Why it happens
Training loss spikes or inference outputs degrade with no logged errors.	A driver-level bug in the NVIDIA 535 series causes the mma.sp PTX instruction on Hopper architecture to silently corrupt data during matrix multiplication.
None. Silent data corruption.	A driver-level bug in the NVIDIA 535 series causes the mma.sp PTX instruction on Hopper architecture to silently corrupt data during matrix multiplication.

Which systems are affected

CUDA
Drivers
Hardware

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: Training loss spikes or inference outputs degrade with no logged errors
✓Verified signal present: None. Silent data corruption
✓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

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.

No credit card. Daily allowance follows verified trust tier. Instant access.

Diagnose this failure in VS Code

Select the traceback or open the failed terminal, then run Denpex locally to see the initiating rank, collateral failures, exact fix, and verification command without uploading the log.

Install the free VS Code extension

Root cause

A driver-level bug in the NVIDIA 535 series causes the mma.sp PTX instruction on Hopper architecture to silently corrupt data during matrix multiplication.

The fix and how to prevent it

Evaluate Denpex on your own logs

Request a Scale evaluation code. A verified workplace organization activates 14 days with up to 50 diagnoses a day. Every account keeps its current diagnosis allowance and gets a verification path. No card or automatic subscription.

References

Don't just read the fix, diagnose your run

The encyclopedia tells you what went wrong. Denpex tells you what went wrong in YOUR training run. With your logs, your config, and your stack.

Diagnose your logs free Browse all failures

Related CUDA errors

Multi-Allocator Zero-Copy MMU Fault

CUDA · critical

Xid 43 (GPU Stopped Processing)

CUDA · high