cudaErrorLaunchFailure an illegal instruction was encountered sm90a

A kernel executed an instruction the GPU does not implement. Almost always an architecture mismatch: the binary contains SASS for a different compute capability, or was JIT-compiled from PTX targeting features this GPU lacks. This entry explains how to confirm the cause, apply the fix, and separate it from adjacent quant failures.

Symptom

cudaErrorLaunchFailure an illegal instruction was encountered sm90a

Root cause

A kernel executed an instruction the GPU does not implement. Almost always an architecture mismatch: the binary contains SASS for a different compute capability, or was JIT-compiled from PTX targeting features this GPU lacks. The decisive evidence is the first log line that precedes "cudaErrorLaunchFailure an illegal instruction was encountered sm90a" and differs from a healthy run.

Recommended fix

confirm the binary targets this GPU, cuobjdump --list-elf on the .so and compare against torch.cuda.get_device_capability(). Architecture-specific suffixes matter: sm_90a code will not run on sm_90.

How Denpex helps

Denpex investigates cudaErrorLaunchFailure an illegal instruction was encountered sm90a 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.

The literal signature is "cudaErrorLaunchFailure an illegal instruction was encountered sm90a". It is a environment failure associated with latency-sensitive CUDA, Triton, and OpenMP runtimes. The line identifies the failing operation or subsystem, while the surrounding evidence decides whether it is the initiating fault or a downstream symptom.

A kernel executed an instruction the GPU does not implement. Almost always an architecture mismatch: the binary contains SASS for a different compute capability, or was JIT-compiled from PTX targeting features this GPU lacks. The failure becomes visible at this call site because the operation first requires the missing resource, valid state, healthy peer, or correct result. Earlier log lines and a known-good control carry more causal value than the final wrapper exception.

• The workload stops or loses forward progress after emitting "cudaErrorLaunchFailure an illegal instruction was encountered sm90a".
• A retry on the same configuration reproduces the failure because the causal state has not changed.
• The outer framework exception can hide the rank, node, allocation, or dependency that failed first.
• Increasing timeouts or reducing workload size can suppress the symptom without correcting the cause.

• latency-sensitive CUDA, Triton, and OpenMP runtimes
• production-shaped multi-accelerator workloads
• containerized and bare-metal deployments of the same stack

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.

• ✓Find the first occurrence of "cudaErrorLaunchFailure an illegal instruction was encountered sm90a" and preserve at least 100 lines before it.
• ✓Identify which rank, node, device, or process emitted the first related warning.
• ✓this frequently follows a fleet with mixed GPU generations where a kernel was compiled on one node and cached for another. Check any compile cache keyed only by source hash. Triton and torch.compile caches must include the architecture.
• ✓Repeat the same input after the targeted change and require the signature to disappear.
• ✓Resume from latest checkpoint, on a matching architecture only after the control passes.

• A kernel executed an instruction the GPU does not implement. Almost always an architecture mismatch: the binary contains SASS for a different compute capability, or was JIT-compiled from PTX targeting features this GPU lacks.
• The decisive evidence is the first log line that precedes "cudaErrorLaunchFailure an illegal instruction was encountered sm90a" and differs from a healthy run.
• A nearby failure remains a competing hypothesis until a control separates configuration, capacity, transport, and hardware causes.

cudaErrorLaunchFailure an illegal instruction was encountered sm90a

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.

confirm the binary targets this GPU, cuobjdump --list-elf on the .so and compare against torch.cuda.get_device_capability(). Architecture-specific suffixes matter: sm_90a code will not run on sm_90. This changes the condition in the causal diagnosis instead of hiding the outer exception. The repeated control proves ownership before recovery from latest checkpoint, on a matching architecture.

# Preserve evidence before restarting
rg -n -i 'error|exception|timeout|failed' <log-file>
nvidia-smi
python -m torch.utils.collect_env

# Find the exact signature in the complete log
rg -n -F -- "cudaErrorLaunchFailure an illegal instruction was encountered sm90a" <log-file>

Adapt the snippet to your framework. The same pattern holds for PyTorch Lightning, Hugging Face Trainer, DeepSpeed, Megatron-LM, and vLLM training wrappers. Where the wrapper exposes a config flag (for examplelr_scheduler_type in Trainer), prefer the flag over the imperative API to keep the schedule declarative and reproducible.

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.

CUDA reports errors asynchronously, so the traceback usually points at whatever line synchronised next rather than the one at fault. The hub covers every common CUDA error and how to make it report honestly.