Diagnose an AI-related GPU crash in stages: preserve the failing run and logs, correlate the failure with passive temperature, power, clock and interconnect telemetry, debug the application and software stack, and only then run vendor diagnostics on an isolated, idle GPU. A diagnostic failure is evidence to interpret with its error code and context—not automatic proof that the hardware is defective.
Why an AI workload crash is difficult to classify
Training and inference exercise several layers at once: application code, framework and runtime libraries, the GPU driver, firmware, system memory, PCIe or other interconnects, power delivery, cooling and the accelerator itself. The same visible symptom—a process abort, a device reset or a hung kernel—can therefore have very different causes.
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Start with the least disruptive evidence. Passive monitoring and software debugging do not add significant load to a production accelerator. Active diagnostics deliberately consume compute, memory, interconnect, power and cooling capacity, and some tests can reset state. Running them against a busy device can interrupt unrelated jobs or make the original failure harder to interpret.
“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.” — NVIDIA GPU Debug Guidelines
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1. Stabilize the system and preserve the failed run
If the GPU is shared or serves production work, stop repeated stress runs and notify the operator before resetting devices, unloading drivers or launching an invasive test. Re-running the same workload without recording the first failure can overwrite logs, change the system state or turn an intermittent problem into an outage.
Create a short incident record while the context is still available. Record:
- Whether the job was training, inference or another AI task, including the model, dataset or input shape when relevant.
- The process identifier and GPU or device identifier, if the platform exposes them.
- The exact failure time, timezone and the first complete error text.
- Whether the failure is repeatable, intermittent, tied to a particular batch or input, or limited to one device.
- GPU model and quantity, host configuration, operating system and recent hardware or software changes.
- Driver, firmware, runtime, framework, compiler and communication-library versions.
- Complete application, runtime, kernel/driver and scheduler logs surrounding the event—not only a copied error line.
NVIDIA’s triage guidance asks for system configuration, a clear issue description, debugging steps already attempted, complete logs for key messages and the output of nvidia-bug-report.sh where applicable. AMD and Intel similarly require platform, runtime, configuration and diagnostic details before their support teams can interpret a failure.
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Put workload start, warnings, temperature or power changes, the first driver message, the application exit and any reset into one timeline. This helps distinguish a kernel that fails immediately from a job that runs for hours and then trips a thermal, memory-integrity or interconnect condition. Keep the original files unchanged and make copies for annotation.
2. Correlate the crash with passive telemetry
Compare the failure timestamp with the telemetry your platform already collects. Useful signals include GPU temperature, memory temperature, clock rates, GPU power, fan speed, inlet or room temperature, power-supply or PDU readings and interconnect counters appropriate to the hardware.
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NVIDIA notes that high GPU and memory temperatures can cause throttling and poor performance. A temperature rise that precedes a crash is therefore a useful lead, but it is not proof of causation: a workload can expose a software bug while the GPU is hot, and a thermal alert can appear after a fault has already begun. Treat telemetry as a way to choose the next check.
| Observation near the crash | What it can suggest | What it cannot establish by itself |
|---|---|---|
| Temperature or memory temperature rises with clock throttling | Cooling, airflow or sustained-load investigation is warranted | That heat caused the application fault |
| Power approaches a platform or board limit | Check power delivery, limits and cooling headroom | That the power supply is defective |
| PCIe, NVLink, XGMI or other interconnect errors align with the timestamp | Inspect the link, topology, seating and platform logs | That the GPU silicon has failed |
| No unusual passive telemetry | Prioritize application, runtime, driver and memory-access debugging | That hardware is healthy |
3. Interpret vendor error codes in context
Error codes are clues with code-specific action guidance. Do not generalize from one number or treat every device error as a hardware verdict.
NVIDIA Xid messages
Locate kernel and driver Xid messages for the affected generation and driver, then read the corresponding entry in NVIDIA’s Xid catalog. The catalog describes several materially different classes:
| Example code | Catalog meaning | Safe interpretation |
|---|---|---|
| Xid 31 | GPU memory page fault | Possible application, driver or hardware bug. Use CUDA-GDB or Compute Sanitizer memcheck to look for an illegal access and, where possible, identify the application line responsible. |
| Xid 48 | Double-bit ECC error | Evidence of a memory-integrity event that deserves hardware and system investigation; use the surrounding logs and platform state before deciding on replacement. |
| Xid 79 | GPU inaccessible over its PCI Express connection | Investigate PCIe link, host, power and platform conditions as well as the accelerator; the code alone does not identify the failed component. |
The generation, driver release and preceding messages matter. Preserve the complete Xid sequence, not just the final code, because earlier warnings can identify whether the event began in an application kernel, memory subsystem or link.
AMD ROCm and RCCL evidence
For AMD systems, use the troubleshooting documentation that matches the installed ROCm and RCCL stack. Collect the ROCm version, accelerator and platform details, topology information and relevant environment values. AMD cluster-health guidance recommends correlating reliability, availability and serviceability (RAS) data plus PCIe or XGMI counters with system logs and snapshots taken while the workload was running.
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If a runtime, kernel-mode driver or compiler changed recently, preserve the failing configuration first and then retest one prior known-good configuration. Changing several layers at once prevents you from knowing which change affected the result.
Intel oneAPI evidence
On Intel platforms, use the applicable oneAPI diagnostic utility to inspect GPU readiness, runtime and driver checks, permissions, environment settings and available metrics. Intel documentation also describes graphics-driver hangcheck behavior: depending on the platform and configuration, a long-running compute kernel can be terminated when hangcheck decides that progress has stopped.
Hangcheck handling is stack- and configuration-specific. Validate the exact Intel platform and driver documentation before changing kernel or driver settings, and record the original configuration so it can be restored.
4. Test the application and software path before stressing hardware
Once evidence is preserved, investigate the failing process with the vendor’s debugger and runtime tools. NVIDIA recommends attaching a debugger and collecting data through the failure; this can quickly show whether the fault is in application code and can also expose system-software problems.
For NVIDIA CUDA workloads
- Use CUDA-GDB when you need execution state and a breakpoint or stack around the failing kernel.
- For a memory-page fault such as Xid 31, use Compute Sanitizer
memcheckto look for illegal reads or writes and the source line associated with the access. - Preserve the debugger and sanitizer output with the original application and driver logs.
Do not apply CUDA procedures unchanged to AMD or Intel software. Use the matching vendor’s debugger, runtime diagnostics and communication-library guidance.
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Reduce the workload without changing its identity
Build a small, reproducible case only after capturing the full production failure. Keep the model, kernel path and input characteristics that trigger the error while reducing batch size, duration or device count as appropriate. A failure that follows one input, kernel or rank points toward the application path; a failure that appears only under sustained multi-device load points toward resources, topology, power, cooling or synchronization. These are prioritization clues, not proof.
When comparing configurations, change one layer at a time—such as the runtime, driver or compiler—and document the result. A prior known-good stack is useful as a controlled comparison, but do not remove the original failing stack until its logs and version information are archived.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.5. Separate passive checks from active diagnostics
Passive checks read existing state. Active diagnostics intentionally exercise GPU resources and can consume compute, memory, PCIe, NVLink or other interconnect capacity, CPU time, power and cooling. The operational risk is significant on a shared host.
When it is safe to schedule an active test
- The scheduler or system owner has taken the target GPU out of service.
- No production process, display session or unrelated diagnostic is using the selected device.
- You have captured the original logs and telemetry and can tolerate a reset or state change.
- Power, cooling and rack capacity are adequate for sustained load.
- The test is limited to explicitly selected devices and its output will be retained.
If any condition is false, continue with log, telemetry and application debugging instead of launching a stress suite.
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NVIDIA DCGM diagnostics
NVIDIA DCGM diagnostics can check software readiness, context creation, memory, PCIe, compute, power, thermal and related paths. Its diagnostic plugins apply sustained compute, memory and power load. NVIDIA warns that diagnostic suites can consume substantial GPU, CPU, interconnect, power and cooling resources; longer suites can reset state or conflict with running processes.
Coordinate with the scheduler, select the intended devices explicitly and run invasive tests only on GPUs taken out of service. Save the complete output and the specific diagnostic error code. A CUDA initialization or context-creation failure can be an execution or environment problem and does not, on its own, establish a defective GPU.
6. Use the evidence to choose the next branch
| Evidence pattern | Next action | Avoid |
|---|---|---|
| Failure reproduces at one input or kernel; sanitizer or debugger identifies an invalid access | Fix or isolate the application or kernel and retest on a controlled stack | Declaring the board defective from the first crash |
| Xid or equivalent memory-page fault with no reproducible application cause | Compare driver/runtime versions, inspect firmware and platform logs, then schedule an isolated vendor diagnostic | Repeatedly rerunning the production job without collecting new evidence |
| Double-bit ECC or recurring memory-integrity events | Correlate counters and logs, remove the device from service if policy requires, and escalate with the full record | Clearing errors and treating the next successful job as a permanent fix |
| PCIe, XGMI, NVLink or other link errors | Inspect topology, link state, host and power conditions; test the device only when isolated | Assuming the GPU core is the only possible fault |
| Crash follows sustained temperature, power or cooling anomalies | Check airflow, fans, inlet temperature, power delivery and operating limits before another load test | Adding more stress while the thermal or power condition remains |
| Only long-running Intel kernels terminate under hangcheck | Verify the exact oneAPI, driver and hangcheck configuration and follow platform-specific guidance | Changing kernel settings without recording the original configuration |
7. Escalate with a reproducible evidence package
Escalation is appropriate when the crash persists with a known-good workload and software comparison, when vendor diagnostics report recurring memory-integrity, thermal, power or interconnect errors, or when a vendor code’s action guidance calls for support.
Send one concise package containing:
- A timeline with timezone, workload start, warnings, crash and any reset.
- GPU model and device identifiers, host configuration, operating system, firmware, driver, runtime, framework and compiler versions.
- The exact workload or smallest reproducible case, including how often it fails.
- Original application, kernel/driver, scheduler and diagnostic logs, with key messages highlighted but not edited.
- Temperature, memory-temperature, clock, power, fan, inlet and interconnect telemetry covering the event.
- Every controlled change and its result, including any return to a prior known-good stack.
- For NVIDIA cases, the output of
nvidia-bug-report.shand the relevant Xid entries; for AMD or Intel cases, the equivalent vendor diagnostic collection requested by the installed stack.
Keep application evidence and system evidence together. A support engineer can then compare the fault’s timing, code and operating conditions instead of guessing from a single screenshot.
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A single crash cannot answer that question. Hardware suspicion becomes stronger when the same device produces recurring, code-specific memory-integrity or link errors across a controlled, known-good workload and software stack, especially when passive telemetry and isolated diagnostics point to the same subsystem. Suspicion becomes weaker when a debugger consistently identifies an application memory access, when only one input triggers the problem, or when changing a runtime or compiler removes the failure.
Even then, a vendor diagnostic result is not automatically a repair or replacement decision. Interpret its reported code, test conditions, device selection and surrounding logs, then follow the platform owner’s service and escalation policy.
Safety checklist before pressing “run”
- Have the original logs, timestamps, versions and telemetry been copied?
- Is the target GPU idle and explicitly reserved for the test?
- Can the host tolerate a reset, sustained power draw and increased heat?
- Are scheduler, system and application owners aware of the test window?
- Will you retain the full diagnostic output and its error code?
- Have you tried the application debugger or vendor runtime checks first?
- Is every configuration change documented so it can be reversed?
There is no universal crash-rate or hardware-failure percentage that can reliably predict what a particular AI system is experiencing. The useful result is a reproducible, time-correlated record that narrows the fault from application and software, to platform conditions, to a specific hardware path.
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