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GPU heat can cause performance loss, but a high temperature alone does not prove it caused an AI job to fail. Before changing cooling or restarting the machine, preserve the error and job details, compare temperature with hardware-throttle state, distinguish live tensor memory from allocator reservations, and inspect Xid and system logs. That evidence helps separate thermal throttling from out-of-memory errors, application bugs, ECC faults, and driver or hardware problems.
What should I capture before restarting or changing anything?
Record the failure as it happened. A restart, reset, or new run can change the evidence you need to compare symptoms with GPU state. Capture the exact exception text, job ID, host, GPU model, driver, CUDA and PyTorch versions, and timestamps for both the job and the failure.
- Exception: Copy the complete error, not just a phrase such as “CUDA error.” Include the traceback if available.
- Identity: Note the job ID, machine or host, GPU model, and which GPU the process used.
- Software versions: Record the driver, CUDA and PyTorch versions. These are useful context when reviewing a driver fault or reproducing an application error.
- Timing: Preserve timestamps for job start, last progress, exception, and any temperature, throttle, or Xid event. Correlation is much more informative than an isolated peak reading.
If you have multiple failed runs, keep their records separate. A failure that consistently follows one model or input is different evidence from a failure that follows one host or device, even when both report a CUDA exception.
Does a hot GPU prove heat caused the job failure?
No. Temperature is one part of the diagnosis; pair it with the GPU’s throttle state and the failure timeline. High temperature without a matching hardware-slowdown indication does not establish that heat caused the failure.
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Google Cloud’s guidance for A3-series virtual machines says performance degradation can occur when temperatures consistently exceed 87 °C. That figure applies to the A3 platform guidance; it is not a universal failure or shutdown threshold for every GPU. Do not use it as a standalone pass/fail test for another machine or GPU.
Query temperature and hardware-slowdown state together
Google Cloud recommends this nvidia-smi query:
nvidia-smi --query-gpu=timestamp,name,pci.bus_id,temperature.gpu,clocks_throttle_reasons.hw_slowdown --format=csvReview the timestamp, GPU name and bus ID, temperature, and clocks_throttle_reasons.hw_slowdown value as a set. An Active hardware-slowdown value is evidence of thermal throttling. Compare its timing with the job’s slowdown or failure rather than assuming that a temperature reading taken later describes the conditions at the moment of failure.
What points toward a thermal explanation?
A plausible thermal case has a time relationship between sustained elevated temperature, an active hardware-slowdown indication, and degraded job performance. Even that relationship identifies throttling, not necessarily the direct cause of a crash: the exception, memory state, and logs still matter. If the job failed but the available telemetry shows no corresponding throttle state, heat is not established as the cause by temperature alone.
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Is a CUDA out-of-memory error the same as a GPU heat problem?
No. CUDA out-of-memory errors concern memory capacity or allocation state; a thermal problem concerns GPU operating conditions. The symptoms can occur during the same workload, so inspect memory figures and thermal telemetry independently instead of treating one as proof of the other.
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Separate live tensor memory from allocator reservations
PyTorch distinguishes memory held by live tensors from memory managed by its caching allocator. memory_allocated() and max_memory_allocated() report allocated memory associated with tensors, while memory_reserved() and max_memory_reserved() report memory managed by the allocator. The “max” versions are useful when the question is how high those respective measures rose during a run.
This distinction explains why nvidia-smi may show GPU memory as used even when some of it is cached by the allocator rather than held by live tensors. A device-level “used” figure is not, by itself, a count of active model tensors or proof that a leak occurred. Compare the PyTorch measures with the device view and the exact exception before deciding what ran out.
Check the workload and process state
Before attributing an allocation failure to cooling, check the workload’s batch size, sequence length, and model precision, along with concurrent processes using the GPU. Also check whether the program retains autograd history or whether a subprocess remains after Python exits. These conditions can affect memory demand or keep device resources in use without a thermal fault.
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PyTorch specifically warns that accumulating tensors that require gradients can rapidly consume GPU memory. When a job appears to use progressively more memory, inspect how tensors are retained across iterations rather than assuming that a temperature change explains the increase.
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What does an NVIDIA Xid error mean?
An Xid message narrows the investigation but does not independently identify root cause. NVIDIA says Xid messages can point to hardware, NVIDIA software, or user-application problems, and describes them as debugging guides—not definitive diagnoses.
Review kernel and event logs around the failure for Xid codes, ECC errors, driver faults, and messages that the GPU has “fallen off the bus.” Record the full message and timestamp rather than relying only on the numeric code. Google Cloud’s guidance associates several codes with particular investigation paths:
| Log evidence | What it suggests | Useful next step |
|---|---|---|
| Xid 13, 31, or 137 | Google Cloud associates these codes with illegal memory access. | Investigate the application with cuda-gdb or Compute Sanitizer. |
| Xid 48 | Associated with a double-bit ECC error. | Treat it as an ECC or hardware/infrastructure issue for investigation and escalation, rather than assuming a workload-only fix. |
| Xid 79 | Means the GPU has fallen off the PCI bus. | Investigate the device, driver, and infrastructure context; escalate if the event recurs. |
| Other Xid or no Xid found | The code alone may not identify the cause; no captured Xid does not classify an unrelated exception. | Preserve the exact error and logs, then compare memory, thermal, and reproducibility evidence. |
The recommended tools for illegal-memory-access codes point toward debugging the CUDA application path. By contrast, a double-bit ECC event or a bus-loss event calls for hardware or infrastructure handling. These associations help choose what to investigate first; they do not prove the complete causal chain in a particular failure.
How can I distinguish heat, memory pressure, and an application or hardware fault?
Compare evidence across the same time window instead of choosing a cause from one symptom. The table below gives a working classification: it identifies the next investigation, not a guaranteed diagnosis.
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| Evidence to compare | Thermal hypothesis | Memory hypothesis | Application, driver, or hardware hypothesis |
|---|---|---|---|
| Symptom and exact error text | Look for performance degradation that coincides with throttling; heat is not proved by a generic crash. | A CUDA out-of-memory exception points toward capacity or allocation state. | Preserve the full exception and traceback; an Xid may arise from hardware, NVIDIA software, or the user application. |
| Temperature and throttle state | Correlated elevated temperature and active hardware slowdown support throttling as a factor. | Temperature alone does not establish memory exhaustion. | A fault or Xid should be evaluated alongside—not replaced by—the telemetry. |
| Allocated versus reserved memory | These measures do not establish thermal cause. | Compare live tensor allocations with allocator reservations and device-level usage. | Retained tensors, concurrent processes, or leftover subprocesses may explain resource pressure. |
| Xid and ECC classification | A temperature reading does not classify an Xid. | Illegal-memory-access codes call for application debugging; do not confuse them with proof of exhausted capacity. | ECC and bus-loss events require hardware or infrastructure investigation; driver faults merit driver review. |
| Reproducibility across models or hosts | Compare whether the failure follows the same operating conditions. | Compare the workload and process state across runs. | Note whether it follows a model or input, or a particular host or GPU. |
| Effect of reset or driver changes | A changed outcome is evidence to record, not proof that cooling caused the original event. | Check process and allocator state rather than presuming a reset fixes the underlying allocation pattern. | Use the outcome as one diagnostic clue; recurring ECC, bus, or driver failures warrant escalation. |
When is it safe to reset the GPU or reboot?
Choose recovery only after classifying the evidence as far as possible. NVIDIA’s debugging guidance says a GPU reset can clear GPU hardware and software state when a reboot would otherwise be required, but no applications may be using the devices. A reset is therefore not a first diagnostic step while workloads may still be active.
- Preserve evidence: Save the exception, job and host details, version information, telemetry, and relevant kernel or event log entries.
- Stop workloads: Confirm that applications are no longer using the affected GPU before attempting a reset. Do not assume the Python process exiting means all subprocesses have ended.
- Match the action to the error: Use application debugging for illegal-memory-access codes; review driver context for driver faults; treat ECC and bus errors as hardware or infrastructure concerns.
- Reset or reboot only where appropriate: A reset may clear device state under NVIDIA’s stated condition. Rebooting or resetting can restore service in some cases, but does not establish why the failure happened.
- Escalate recurring faults: Repeated ECC, bus, or driver failures need hardware, provider, or driver investigation rather than repeated recovery alone.
What should I change after the first diagnosis?
Make the change that addresses the evidence you found, and preserve the before-and-after details. A broad cooling change is not a substitute for diagnosing allocation state or an illegal memory access, just as reducing memory demand does not resolve a recurring bus-loss event.
- If throttling aligns with degraded performance: Investigate the platform and operating conditions that coincide with the slowdown. Apply the relevant platform guidance rather than treating Google Cloud’s A3-specific 87 °C figure as a universal GPU limit.
- If memory pressure is supported: Review batch size, sequence length, precision, concurrent GPU users, retained autograd history, and subprocesses. Compare live allocations with allocator reservations before interpreting
nvidia-smi. - If an illegal-memory-access Xid appears: Focus on application debugging with
cuda-gdbor Compute Sanitizer rather than changing cooling as the first response. - If ECC, bus, or driver errors recur: Keep the timestamps and full log messages, and involve the relevant hardware, cloud-provider, or driver support path.
For the next run, use the same recordkeeping: exact error, versions, GPU identity, timestamped thermal and throttle information, memory measures, and logs. Changing one relevant factor at a time makes it easier to tell whether a fix addressed the failure or merely changed the conditions under which it appeared.
Frequently Asked Questions
Can I find out what caused a CUDA job to fail from the exception alone?
Usually not. A message such as a generic CUDA failure needs to be interpreted with the traceback, timestamped device state, and kernel or event logs; NVIDIA describes Xid messages as debugging guides rather than final diagnoses.
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Why does nvidia-smi show GPU memory in use after my Python process exits?
A subprocess may still be running, or another concurrent process may be using the device. PyTorch allocator reservations can also explain why device-level memory usage differs from live tensor allocation, so check process state as well as allocator figures.
Does an Xid 79 error mean my GPU is permanently damaged?
The code means the GPU has fallen off the PCI bus, but the code alone does not establish permanent damage or the underlying cause. Preserve the event context and escalate if it recurs.
Should I lower batch size whenever an AI job fails?
Only when the error and memory evidence support allocation pressure. A batch-size change does not diagnose a throttle event, an illegal memory access, an ECC error, or a GPU bus failure.
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