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 fusedadam's cuda kernels were compiled for sm80 (a100) and are not compatible with sm90 (h100) architecture. Validating against the symptom list in this entry is the fastest way to confirm the diagnosis.

Is this a hardware or a software issue?

Memory 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 memory 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 DeepSpeed with FusedAdam optimizer on NVIDIA H100 (SM90) GPUs 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 #deepspeed, #fusedadam, #h100. 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 deepspeed is upgraded. Pin compatible versions and rerun the prevention smoke test on every dependency change.

Is there a fingerprint I can monitor?

Yes. CUDA error: illegal memory access in FusedAdam kernel 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.

DeepSpeed FusedAdam Illegal Memory Access on H100

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 fusedadam's cuda kernels were compiled for sm80 (a100) and are not compatible with sm90 (h100) architecture 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.

DeepSpeed FusedAdam optimizer triggers CUDA illegal memory access errors on NVIDIA H100 GPUs. This is a compatibility issue between FusedAdam's CUDA kernels and H100's SM90 architecture. Denpex detects the H100+FusedAdam combination and recommends the fallback optimizer.

Quick answer

DeepSpeed FusedAdam optimizer triggers CUDA illegal memory access errors on NVIDIA H100 GPUs.

Symptom: CUDA error: illegal memory access in FusedAdam kernel
Root cause: FusedAdam's CUDA kernels were compiled for SM80 (A100) and are not compatible with SM90 (H100) architecture. H100's SM90 architecture has different shared memory layout and warp scheduling that causes out-of-bounds access in FusedAdam kernels. The illegal memory access occurs in the Adam update kernel when updating optimizer states (momentum, variance) for large parameter tensors.
Recommended fix: TORCH_CUDA_ARCH_LIST='9.0'
How Denpex helps: Denpex investigates DeepSpeed FusedAdam Illegal Memory Access on H100 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.

Memory#deepspeed#fusedadam#h100#sm90#illegal-memory#optimizer

What this failure is

DeepSpeed FusedAdam Illegal Memory Access on H100 is a Memory failure seen during ML training runs. DeepSpeed FusedAdam optimizer triggers CUDA illegal memory access errors on NVIDIA H100 GPUs. This is a compatibility issue between FusedAdam's CUDA kernels and H100's SM90 architecture. Denpex detects the H100+FusedAdam combination and recommends the fallback optimizer. Common tags: Deepspeed, Fusedadam, H100, Sm90.

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Why it happens (the mechanism)

FusedAdam's CUDA kernels were compiled for SM80 (A100) and are not compatible with SM90 (H100) architecture. H100's SM90 architecture has different shared memory layout and warp scheduling that causes out-of-bounds access in FusedAdam kernels. The illegal memory access occurs in the Adam update kernel when updating optimizer states (momentum, variance) for large parameter tensors. DeepSpeed's FusedAdam has not been updated to support SM90 architecture natively. 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 on H100 with DeepSpeed FusedAdam crashes with 'CUDA error: illegal memory access'
The same training works on A100 with FusedAdam
Switching to the standard PyTorch AdamW optimizer resolves the crash

Common symptoms and what they mean

Symptom	Why it happens
CUDA error: illegal memory access in FusedAdam kernel	FusedAdam's CUDA kernels were compiled for SM80 (A100) and are not compatible with SM90 (H100) architecture
Training crashes during the optimizer step (not forward or backward)	H100's SM90 architecture has different shared memory layout and warp scheduling that causes out-of-bounds access in FusedAdam kernels
nvidia-smi shows the GPU in a bad state after the crash	The illegal memory access occurs in the Adam update kernel when updating optimizer states (momentum, variance) for large parameter tensors
The error is deterministic: it happens at the same step every time	DeepSpeed's FusedAdam has not been updated to support SM90 architecture natively

Which systems are affected

DeepSpeed with FusedAdam optimizer on NVIDIA H100 (SM90) GPUs
DeepSpeed with FusedAdam on H200 GPUs
DeepSpeed versions using CUDA kernels compiled for SM80 (A100) but running on SM90
Mixed precision training with FusedAdam and bf16 on H100

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: CUDA error: illegal memory access in FusedAdam kernel
✓Verified signal present: Training crashes during the optimizer step (not forward or backward)
✓Verified signal present: nvidia-smi shows the GPU in a bad state after the crash
✓Verified signal present: The error is deterministic: it happens at the same step every time
✓A targeted fix from the "How to fix it" section eliminates or substantially reduces the symptom within one validation pass.

Root cause

FusedAdam's CUDA kernels were compiled for SM80 (A100) and are not compatible with SM90 (H100) architecture
H100's SM90 architecture has different shared memory layout and warp scheduling that causes out-of-bounds access in FusedAdam kernels
The illegal memory access occurs in the Adam update kernel when updating optimizer states (momentum, variance) for large parameter tensors
DeepSpeed's FusedAdam has not been updated to support SM90 architecture natively

The fix and how to prevent it

Searchable error signature

search key

CUDA error: illegal memory access in FusedAdam kernel
nvidia-smi shows the GPU in a bad state after the crash

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

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DeepSpeed errors in context

DeepSpeed changes when parameters, gradients and optimizer state are created, partitioned, gathered and offloaded. The hub separates ZeRO, memory, checkpoint and pipeline failures by lifecycle phase.

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