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The safe way to diagnose an AI-related GPU crash is to preserve evidence first, correlate the failure with passive telemetry, test the application and software stack, and only then run active hardware diagnostics on an isolated, idle GPU. Repeatedly restarting a production job or launching a stress test can erase the clues you need and can disrupt other users.
A crash is not automatically a bad GPU. Illegal memory access, a runtime or driver regression, ECC errors, overheating, unstable power, PCIe or interconnect faults, permissions, and kernel hangcheck can produce similar symptoms. The workflow below separates those possibilities while limiting operational risk.
What an AI GPU crash can mean
“Crash” may describe several different events: a training process exits with an exception, an inference server loses its device, a kernel hangs until the driver resets the GPU, the operating system logs a machine-check or driver error, or the GPU disappears from the bus. AI workloads expose these paths because they combine long-running kernels, large allocations, high memory traffic, sustained power draw, and communication between accelerators.
Start by classifying the event rather than assuming a component failed. A reproducible illegal read in one model points toward the application or a kernel. A device that vanishes from PCI Express after a period of heat or load points toward a platform, power, firmware, or interconnect path. A failure that follows a driver or compiler change needs a controlled software comparison. These are hypotheses, not verdicts.
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Before touching the system: stabilize and preserve evidence
On a shared or production host, stop repeated stress runs and notify the operator or scheduler owner. Do not reset a device that another process may be using until you have recorded what you can and obtained approval. A reset can remove the context needed to explain the original failure.
Capture the incident timeline
- Record the workload type (training, inference, data preparation, or another GPU task), model or job identifier, process ID, and device identifier if available.
- Write down the exact failure time with timezone, the last successful step, whether the failure is repeatable, and whether it affects one device, one host, or multiple hosts.
- Copy the complete application, framework, runtime, driver, kernel, and system logs around the event. Preserve the original files; do not keep only a pasted error line.
- Note recent changes such as a new model, batch size, compiler, container image, driver, firmware, kernel, cooling configuration, or scheduler policy.
Record the software and platform inventory
- GPU vendor, exact model, memory capacity, device count, and topology.
- Operating-system distribution and kernel version.
- GPU driver, firmware, accelerator runtime, framework, compiler, and container versions.
- Host CPU, system memory, power-supply or PDU information when available, and cooling or rack location.
- Environment variables, permissions, device visibility settings, and scheduler allocation.
NVIDIA’s triage guidance requests system configuration, a clear issue description, debugging steps already attempted, complete logs for key messages, and output from nvidia-bug-report.sh where relevant. AMD and Intel troubleshooting guidance likewise depends on the exact platform, runtime, configuration, and diagnostic output. Generate a bug report only after checking your organization’s policy about sensitive paths, environment variables, and workload data.
Correlate the crash with passive telemetry
Passive observation does not deliberately load the GPU, so it is the safest next step. Align telemetry timestamps with the crash timeline and look for a change immediately before the event rather than relying on a single peak value.
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Telemetry to collect
- GPU temperature and memory temperature, if the hardware exposes both.
- GPU and memory clocks, throttle or power-limit reasons, fan speed, and utilization.
- GPU power draw, host power-supply or PDU readings, and inlet or rack temperature.
- PCIe link state and error counters, plus NVLink, XGMI, or other accelerator-interconnect counters appropriate to the platform.
- ECC or RAS events, page retirement, remapping, and driver reset messages.
- Host memory pressure, CPU errors, filesystem failures, and container or scheduler events at the same time.
NVIDIA notes that high GPU or memory temperature can cause throttling and poor performance. A temperature rise that precedes a crash is useful evidence, but correlation alone does not prove that heat caused the failure. A power transient, cooling restriction, or software timeout can occur in the same interval.
Use time-aligned snapshots
For a recurring job, collect a short, low-impact telemetry interval before launch, during the workload, and immediately after failure. Keep the sampling interval and clock source consistent. If the failure occurs only after many hours, a single manual reading taken afterward is not representative. In a cluster, capture the affected host and its peers so you can distinguish a node-local event from a workload-wide software problem.
Interpret vendor error messages in context
Error codes narrow the search, but most are not one-to-one hardware diagnoses. Use the catalog for the installed driver and GPU generation, then follow the action guidance for that exact code.
NVIDIA: Xid messages
Look for Xid messages in the kernel or driver log and retain the surrounding lines. The NVIDIA Xid catalog includes several classes of causes:
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| Example | What it indicates | How to treat it |
|---|---|---|
| Xid 31 | GPU memory page fault | May involve an application, driver, or hardware bug. NVIDIA recommends CUDA-GDB or Compute Sanitizer memcheck to investigate an illegal access and identify the responsible application line where possible. |
| Xid 48 | Double-bit ECC error | Evidence of a memory-integrity event that requires the code-specific recovery and escalation guidance. Do not infer the full repair decision from the number alone. |
| Xid 79 | GPU is inaccessible over its PCI Express connection | Investigate PCIe link, slot, power, firmware, and platform conditions, then check whether the device returns after an approved reset or reboot. |
The same Xid can have more than one possible cause. Preserve the complete message, device identity, preceding errors, and what happened after the driver response. A single Xid number is not proof that the GPU is defective.
AMD: ROCm and RCCL
For AMD accelerators, use the ROCm and RCCL documentation matching the installed release. Collect the ROCm version, accelerator and platform information, topology, and relevant environment values. RCCL problems can be workload- or topology-specific, so record whether the failure occurs only in multi-GPU communication or also in a single-device run.
AMD cluster-health guidance recommends correlating RAS and PCIe or XGMI counters with system logs and workload-time snapshots. Compare a failing run with a controlled single-GPU or reduced-topology run only after preserving the original configuration. If a recent runtime, kernel-mode driver, or compiler change coincides with the crash, retest the previous known-good configuration one layer at a time.
Intel: oneAPI and hangcheck
On Intel GPUs, use the applicable oneAPI diagnostic utility to check GPU readiness, driver and runtime state, environment values, permissions, and available metrics. Intel documentation describes graphics-driver hangcheck behavior that can terminate a long-running kernel when the relevant configuration is not handled correctly.
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Test the application and software path first
NVIDIA’s GPU Debug Guidelines state: “The issue causing the application crash could be related to the system software, but debugging the process is the fastest way to understand where the crash is and if it can be resolved from the application side.” The same principle applies on other vendors’ stacks: reproduce the smallest failing case with the matching vendor debugger and runtime tools.
Reduce the workload without changing its meaning
- Keep the same model, kernels, precision mode, and communication pattern while reducing batch size, sequence length, or data volume.
- Run the failing operation on one GPU to separate an application fault from a collective-communication or topology fault.
- Use a deterministic seed and a fixed input slice when the framework supports it.
- Record whether the reduced case fails at the same operation and with the same message.
A smaller workload that succeeds does not prove the hardware is healthy; it may simply avoid the allocation size, execution time, or communication path that triggers the problem. Conversely, a minimal reproducer that fails consistently is valuable evidence for an application or runtime defect.
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Use vendor debugging tools
For NVIDIA Xid 31 or an illegal memory access, CUDA-GDB and Compute Sanitizer memcheck can instrument the process and report the application line associated with an invalid read or write. Instrumented execution can be slower and can alter timing, so run it on an isolated device and retain both the instrumented and normal results.
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Change one layer at a time
After preserving the failing evidence, compare a known-good configuration: an earlier driver or runtime, prior compiler, previous container, or reduced workload. Change only one layer, rerun the same reproducer, and record the result. Changing the driver, framework, model, batch size, and firmware simultaneously makes the result uninterpretable and can hide a regression.
Check thermal, power, memory, and interconnect causes
Thermal path
Inspect temperature trends, fan behavior, inlet temperature, airflow obstructions, and whether the card is throttling before the crash. Verify that the reading is from the affected GPU and memory domain, not a neighboring device. A cooling issue may present first as reduced clocks and longer runtimes, then as a timeout or reset. Do not remove airflow limits or operate outside the manufacturer’s environmental range to make a test pass.
Power path
Compare GPU power telemetry with host power-supply or PDU data when available. Look for a repeatable failure during high-power phases, but remember that a sampled reading may miss a short transient. Check approved cabling, connector seating, firmware policy, and rack-level power events with the operator. Never reseat energized hardware.
Memory and ECC path
Record correctable and uncorrectable ECC or RAS events, page retirement, and remapping. A double-bit ECC event deserves the vendor’s code-specific procedure, but it still needs context: device identity, recurrence, workload, and any companion errors. Avoid clearing counters before exporting them.
PCIe and accelerator interconnect
For Xid 79 or similar device-inaccessible errors, inspect PCIe link state and error counters, slot or riser events, firmware compatibility, and interconnect health. In multi-GPU jobs, compare a single-device run with the original topology. AMD users should include XGMI information where applicable; NVIDIA users should include NVLink information where applicable. A topology-specific failure can be distinct from a compute-kernel fault.
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Run active diagnostics only on isolated resources
Active diagnostics deliberately exercise GPU resources and can consume compute, memory, PCIe, NVLink, CPU, power, and cooling capacity. They can also reset state or conflict with running processes. Coordinate with the scheduler, take the GPU out of service, and announce the maintenance window before starting.
NVIDIA DCGM diagnostics
DCGM diagnostics can test software readiness, context creation, memory, PCIe, compute, power, thermal behavior, and other paths. Select the intended device explicitly and save the complete output, including the diagnostic level and error code. The diagnostic plugin applies sustained compute, memory, and power load; NVIDIA recommends running it on GPUs that are not serving production workloads.
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Scheduling and isolation checklist
- Drain or cordon the GPU through the scheduler and verify that no user process owns it.
- Record the device inventory and baseline telemetry before starting.
- Run the least invasive readiness checks first, then increase diagnostic scope only if needed.
- Monitor temperature, power, and host events during the test.
- Stop if the device approaches an operational limit, affects another resource, or produces a new system error.
- Restore the scheduler state, verify device visibility, and document any reset or reboot.
Do not run a long diagnostic suite on a GPU that is still serving inference, participating in training, or sharing a power and cooling envelope with production devices that cannot tolerate the load.
Choose the next diagnostic path
| Observed pattern | Most useful next step | What would strengthen the hypothesis |
|---|---|---|
| Same model and kernel fail consistently; memory-access message appears | Reduce to a reproducer and use the vendor debugger or memory checker | The failure follows the operation or input across known-good GPUs |
| Failure follows a driver, runtime, or compiler change | Retest the prior known-good layer while holding everything else constant | The crash disappears without changing hardware or workload |
| Temperature or power rises immediately before reset | Review cooling, power, throttle reasons, and rack events | Repeats under the same thermal or power condition and is absent at lower load |
| ECC, RAS, or page-retirement events recur on one device | Follow the vendor’s memory-integrity procedure and isolate the device | Events persist across workloads and approved diagnostics |
| Device becomes inaccessible; PCIe, NVLink, or XGMI errors appear | Inspect the platform and interconnect path, firmware, and topology | The issue follows a slot, link, or host rather than a job |
| Initialization, permission, or readiness check fails | Verify device visibility, permissions, runtime installation, and environment | A clean environment or corrected allocation resolves the failure |
This is a practical decision aid, not a vendor scoring system. A pattern can have multiple causes, so retain the evidence that supports and contradicts each hypothesis.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Production and shared-system precautions
- Use the scheduler to reserve and drain devices rather than relying on an informal announcement.
- Do not reset a GPU while another process may be using it.
- Separate diagnostic output from application logs but keep timestamps aligned.
- Protect model data, credentials, and environment variables when sharing bug reports.
- Record every reset, reboot, driver reload, firmware change, and cable or slot inspection.
- Return the device to service only after a readiness check and a small, representative workload succeed.
When and how to escalate
Escalate when the crash persists with a known-good workload and software configuration, when vendor diagnostics report recurring memory-integrity, thermal, power, or interconnect errors, or when the error catalog gives code-specific escalation advice. A failed diagnostic is evidence to interpret, not an automatic repair or replacement decision.
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Build a concise escalation package
- One-paragraph issue description and a timestamped timeline.
- Exact GPU model, device identifier, host, topology, operating system, kernel, driver, firmware, runtime, framework, and compiler versions.
- Minimal reproducer or precise workload steps, including batch size, precision, device count, and whether the failure is deterministic.
- Complete application, kernel, driver, scheduler, and diagnostic logs with the relevant time window.
- Telemetry snapshots or graphs for temperature, memory temperature, clocks, power, throttle reasons, ECC or RAS, and interconnect counters.
- All controlled changes and their results, including any known-good configuration.
- For NVIDIA cases, the output of
nvidia-bug-report.shwhen requested and permitted by your data-handling policy.
Keep application evidence and system evidence together. Reporting only “the GPU crashed” forces the recipient to repeat safe triage and often loses the distinction between an illegal access, a driver reset, and a platform fault.
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Common diagnostic mistakes
Rerunning until it fails again
Repeated production reruns can overwrite logs, increase thermal stress, and create secondary faults. Capture first, then reproduce on an isolated resource.
Calling any Xid or diagnostic failure a dead GPU
Xid codes and DCGM results can reflect application, environment, driver, firmware, memory, power, or interconnect conditions. Follow the code-specific guidance and compare controlled configurations.
Changing several variables at once
A simultaneous driver upgrade, model change, and firmware update prevents a meaningful comparison. Use a one-layer-at-a-time plan.
Ignoring topology and scheduler context
A multi-GPU collective failure may disappear on one device, while a permission problem may affect every workload on one host. Record allocation, topology, and device visibility.
Disabling protections casually
Hangcheck, thermal limits, and power protections exist to contain faults. Any change must match the exact vendor platform and be approved for a controlled maintenance window.
Safe return-to-service criteria
Before returning a device to production, verify that the original error is no longer present, the device is visible and healthy to the vendor readiness tool, no new ECC, RAS, PCIe, or interconnect errors are accumulating, and temperatures and power remain within the platform’s operating limits under a representative workload. Record the test, duration, device, software versions, and operator. If the original failure returns, stop escalating load and attach the new evidence to the existing timeline rather than starting over.
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