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 bug in the cutlass epilogue generation for specific sm configurations causes sub-optimal register allocation and warp scheduling. Validating against the symptom list in this entry is the fastest way to confirm the diagnosis.

Is this a hardware or a software issue?

Hardware/Vendor 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 hardware/vendor 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 NVIDIA Hopper/Ada architectures (SM90/SM120) 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 #cutlass, #nvidia, #moe. 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 cutlass is upgraded. Pin compatible versions and rerun the prevention smoke test on every dependency change.

Is there a fingerprint I can monitor?

Yes. VLLM or PyTorch throughput metrics show a 50% regression compared to previous baseline 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.

CUTLASS SM120 Kernel Regression Halving MoE Throughput

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 bug in the cutlass epilogue generation for specific sm configurations causes sub-optimal register allocation and warp scheduling 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.

NVIDIA's CUTLASS kernels for SM120 (and some Hopper architectures) suffer from a silent regression that halves throughput on Mixture of Experts (MoE) inference, commonly seen when utilizing certain PyTorch extensions or vLLM.

Quick answer

Root cause: A bug in the CUTLASS epilogue generation for specific SM configurations causes sub-optimal register allocation and warp scheduling. Silent vendor regression introduced in recent CUDA toolkit updates impacting specialized grouped GEMMs used in MoE.
Recommended fix: Downgrade CUTLASS/CUDA toolkit to known-good version prior to the regression
How Denpex helps: Denpex investigates CUTLASS SM120 Kernel Regression Halving MoE Throughput using the evidence you provide or your connected workload collects. Earlier rank, host or application evidence is needed to distinguish an initiating failure from a downstream report.

Hardware/Vendor#cutlass#nvidia#moe#throughput#regression#vllm

What this failure is

CUTLASS SM120 Kernel Regression Halving MoE Throughput is a Hardware/Vendor failure seen during ML training runs. NVIDIA's CUTLASS kernels for SM120 (and some Hopper architectures) suffer from a silent regression that halves throughput on Mixture of Experts (MoE) inference, commonly seen when utilizing certain PyTorch extensions or vLLM. Common tags: Cutlass, Nvidia, Moe, Throughput.

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Why it happens (the mechanism)

A bug in the CUTLASS epilogue generation for specific SM configurations causes sub-optimal register allocation and warp scheduling. Silent vendor regression introduced in recent CUDA toolkit updates impacting specialized grouped GEMMs used in MoE. 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

Throughput on MoE inference is roughly half of expected theoretical limits
No hard errors or exceptions are thrown during execution
GPU utilization appears artificially low (e.g., <50% utilization on $20K GPUs)

Common symptoms and what they mean

Symptom	Why it happens
vLLM or PyTorch throughput metrics show a 50% regression compared to previous baseline	A bug in the CUTLASS epilogue generation for specific SM configurations causes sub-optimal register allocation and warp scheduling
NVIDIA Nsight Compute shows significant stall times in GEMM kernels	Silent vendor regression introduced in recent CUDA toolkit updates impacting specialized grouped GEMMs used in MoE
GPU SM active time is unusually low despite high request concurrency	A bug in the CUTLASS epilogue generation for specific SM configurations causes sub-optimal register allocation and warp scheduling

Which systems are affected

NVIDIA Hopper/Ada architectures (SM90/SM120)
MoE models (Mixtral, DeepSeek) running on vLLM or specialized PyTorch kernels

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: vLLM or PyTorch throughput metrics show a 50% regression compared to previous baseline
✓Verified signal present: NVIDIA Nsight Compute shows significant stall times in GEMM kernels
✓Verified signal present: GPU SM active time is unusually low despite high request concurrency
✓A targeted fix from the "How to fix it" section eliminates or substantially reduces the symptom within one validation pass.

Root cause

A bug in the CUTLASS epilogue generation for specific SM configurations causes sub-optimal register allocation and warp scheduling
Silent vendor regression introduced in recent CUDA toolkit updates impacting specialized grouped GEMMs used in MoE

The fix and how to prevent it

The fix and the prevention pattern

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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.

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vLLM errors in context

vLLM failures cross request, model, KV cache, worker, kernel and host boundaries. The hub compares common literal signatures and the first control that separates their causal owners.

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Related failures to investigate next

Nccl P2p Level Topology Debug often co-occurs with this failure because both share underlying assumptions about hardware/vendor; if you fix this entry but still see drift, validate the related primary cause next. nccl p2p level topology debug →

Mellanox Ofed Driver Mismatch often co-occurs with this failure because both share underlying assumptions about hardware/vendor; if you fix this entry but still see drift, validate the related neighboring cause next. mellanox ofed driver mismatch →

References

Don't just read the fix, diagnose your run

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Related Hardware/Vendor errors

NCCL P2P Level and Topology Routing Limits

Hardware/Vendor · high

Mellanox OFED Driver Mismatch

Hardware/Vendor · critical