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A hot GPU does not, by itself, prove heat caused an AI job to fail. Check the exact error and timing, GPU temperature alongside its hardware-throttling state, PyTorch’s live and reserved memory, and system logs for NVIDIA Xid or ECC errors. Those signals help distinguish thermal slowdown from a CUDA memory problem, an application bug, or a driver, hardware, or infrastructure fault—and point to a safer next step than changing cooling at random.
What evidence should I preserve before troubleshooting?
Start by recording the failure as it happened. Keep the exact exception or error text, job ID, host, GPU model, driver, CUDA and PyTorch versions, and timestamps. These details let you compare the failure with temperature and throttle readings, memory behavior, and system events instead of relying on a general impression such as “the GPU was hot.”
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Preserve the same information for a successful run, if one is available. A comparison is useful only when you can tell which job and host produced each observation. Avoid changing several variables before you have captured the failed run’s evidence: doing so can make a one-off failure harder to reproduce or classify.
- Job: exact exception or error text, job ID, and failure time.
- Machine: host and GPU model.
- Software: driver, CUDA, and PyTorch versions.
- State around failure: temperature, throttle reason, memory readings, and relevant kernel or event-log entries.
How do I tell whether GPU heat caused the failure?
Look for temperature and hardware-throttle evidence at the same time as the slowdown or failure. A high temperature alone is not proof of causation; a recorded hardware-slowdown state is more directly relevant to thermal throttling, especially when its timing matches the job’s symptoms.
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Google Cloud recommends querying the GPU timestamp, name, PCI bus ID, temperature, and hardware-slowdown throttle reason together:
nvidia-smi --query-gpu=timestamp,name,pci.bus_id,temperature.gpu,clocks_throttle_reasons.hw_slowdown --format=csvIn the output, an Active value for clocks_throttle_reasons.hw_slowdown is evidence of hardware slowdown. Compare its timestamp with the job’s recorded failure or slowdown; a reading from another time does not establish what was happening during the failure. The query provides a way to collect related observations, not a verdict about root cause.
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Is a CUDA out-of-memory error the same as a thermal problem?
No. A CUDA out-of-memory error points to an allocation or memory-capacity problem, while temperature and throttle readings address a different hypothesis. Check what PyTorch reports as live tensor memory as well as what its allocator has reserved before treating a high memory reading in nvidia-smi as proof that live tensors occupy all of it.
PyTorch distinguishes memory held by live tensors from memory managed by its caching allocator:
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memory_allocated()reports memory occupied by tensors.max_memory_allocated()reports the peak memory occupied by tensors.memory_reserved()reports memory managed by the caching allocator.max_memory_reserved()reports the peak memory managed by that allocator.
Because the allocator can retain cached memory, nvidia-smi can show memory as used even when that memory is not held by live tensors. That does not mean every OOM is merely a display artifact: compare allocator readings with the exact exception and the job’s memory behavior. Keep “live tensor usage,” “allocator-reserved memory,” and “memory shown as used by the system” conceptually separate.
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Review workload settings and process behavior before changing cooling. Batch size, sequence length, and model precision affect the workload’s memory demands; concurrent processes and subprocesses that remain after Python exits can also contribute to what is using the GPU. Check these conditions against the failure rather than assuming the GPU’s temperature explains a memory allocation error.
Inspect tensor lifetime and gradient tracking
Look for tensors retained longer than intended, particularly tensors that require gradients. PyTorch warns that accumulating tensors requiring gradients can rapidly consume GPU memory. If memory rises across iterations or jobs, inspect whether the application is retaining such tensors instead of allowing them to be released when they are no longer needed.
Check processes, not just the Python process you expected
Account for concurrent workloads and subprocesses that may remain after the main Python process exits. A process that outlives the script can leave GPU use that is easy to misattribute to the failed job. Record which workload was running at the relevant time before concluding that the failed model alone exhausted available memory.
Use repeatability as a diagnostic clue
Compare whether the same job fails with different batch sizes, sequence lengths, or model precision, and whether the issue follows a particular host or GPU. These comparisons help narrow the investigation, but a change that makes a failure disappear does not by itself prove which mechanism caused it. Preserve the changed conditions and the original error so the comparison remains interpretable.
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What does an NVIDIA Xid error mean?
An Xid is a useful clue, not a root-cause verdict. NVIDIA describes Xid messages as “debugging guides” because they can reflect hardware, NVIDIA software, or user-application problems. Read the code together with the surrounding log entries, the CUDA exception, and the host and job details.
Google Cloud’s guidance associates these codes with different investigation paths:
| Xid | What the guidance associates it with | Initial direction |
|---|---|---|
| 13, 31, or 137 | Illegal memory access | Debug the application with cuda-gdb or Compute Sanitizer. |
| 48 | Double-bit ECC error | Investigate as an ECC or hardware/infrastructure issue. |
| 79 | GPU has fallen off the PCI bus | Investigate as a bus, hardware, or infrastructure issue. |
The code narrows the next step but cannot, on its own, tell you whether the ultimate cause is in the application, NVIDIA software, or hardware. For example, an illegal-memory-access Xid points first toward application debugging; it is not evidence that a GPU overheated. ECC and bus errors call for a different response from a CUDA kernel bug.
How do I investigate logs and compare likely causes?
Review kernel and event logs around the failure for Xid messages, ECC errors, driver faults, and reports that the GPU has fallen off the bus. Match their timestamps to the recorded job failure and the temperature and throttle observations. A nearby event is relevant evidence, but the event’s meaning and the application’s exact error still matter.
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Use the same set of questions to compare competing explanations:
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- Symptom: What is the exact error text, and did the job slow down, fail an allocation, or terminate in another way?
- Thermal state: What were the temperature and hardware-throttle state at the time?
- Memory state: What did PyTorch report for allocated and reserved memory, and what did the system show as used?
- Log classification: Was there an Xid, ECC error, driver fault, or bus event?
- Scope: Does the failure reproduce across models or hosts, or remain tied to one environment?
- Recovery response: Does a permitted reset or a driver change alter the result?
These comparisons organize evidence; none makes one observation conclusive in isolation. A reproducible failure tied to an application workload suggests a different path from a recurring ECC or bus event. Likewise, a reset that changes the symptom is useful information, not a complete diagnosis.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What recovery step is appropriate for each failure?
Classify the error before choosing recovery. Stop workloads and use reset or reboot only where the documented error class permits it; recurring ECC, bus, or driver failures should be escalated rather than treated as routine cooling problems.
For illegal-memory-access Xids
Start with application debugging. Google Cloud recommends cuda-gdb or Compute Sanitizer for Xids 13, 31, and 137. Preserve the exact failing job and error context so debugging is aimed at the workload that produced the event, rather than at unrelated temperature readings.
For ECC, bus, or driver events
Review the event and its recurrence, then involve the relevant hardware or infrastructure support path when the error points there. An Xid 48 is associated with double-bit ECC, while Xid 79 indicates that the GPU has fallen off the PCI bus. Recurring errors of these kinds warrant escalation; changing a model’s batch size does not address the classification itself.
Before attempting a GPU reset
NVIDIA’s debugging guidance says a GPU reset can clear GPU hardware and software state when a reboot would otherwise be required. No applications may be using the devices during the reset. Stop workloads and verify that condition before attempting it; do not assume a reset is appropriate for every Xid or that it replaces investigation of recurring faults.
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How should I reach a conclusion without overcalling the evidence?
State the strongest supported finding, and keep it distinct from the suspected root cause. For example, an active hardware-slowdown reading at the time of a slowdown supports thermal throttling; it does not alone explain every subsequent job failure. An OOM exception with allocator readings supports a memory investigation; a high system memory figure alone does not prove that live tensors consumed that amount.
Similarly, an Xid gives you a debugging direction rather than a complete explanation. Application-associated illegal-memory-access codes call for kernel or application debugging, while ECC and bus events point toward hardware or infrastructure handling. If readings do not align in time or the failure does not reproduce, record that uncertainty rather than turning a correlation into a diagnosis.
Frequently Asked Questions
Does an Xid error prove my GPU is broken?
No. NVIDIA says Xid messages can reflect hardware, NVIDIA software, or user-application problems, and describes them as debugging guides rather than standalone diagnoses. Interpret the code with the surrounding logs and the failed job’s exact error.
Why does nvidia-smi show GPU memory in use after a job finishes?
PyTorch’s caching allocator can retain memory it manages even when that memory is no longer occupied by live tensors. Compare PyTorch’s allocated and reserved readings instead of treating a system-level used-memory figure as a direct count of live tensors.
Which tool should I use for an illegal memory access in CUDA?
Google Cloud recommends cuda-gdb or Compute Sanitizer for Xids 13, 31, and 137, which its guidance associates with illegal memory access. Keep the failing job’s exact context available while debugging.
Can a GPU reset replace a reboot?
NVIDIA’s guidance says a reset can clear GPU hardware and software state when a reboot would otherwise be required. It is only possible when no applications are using the devices, and it should not be treated as a universal fix for recurring errors.
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