Is this a hardware or a software issue?

Memory failures can look like either layer depending on the symptom. The discriminator is the resolution of the symptom to the recommended fix: if applying the steps under "How to fix it" eliminates the symptom, this is a software/configuration class issue regardless of the hardware it surfaced on. Repeat the fix on a fresh node to confirm.

How long does the fix typically take?

Most resolutions from this entry take 10 to 30 minutes of engineer time once the diagnosis is confirmed. Long fixes come from skipping the diagnosis step and chasing apparent symptoms (validation drift, memory growth, slow throughput) instead of the actual root cause listed above. Apply the fix in a small experiment first, then roll it forward.

Does this affect inference or only training?

Primarily this is a memory failure observed during training, but the same misconfiguration can leak into downstream evaluation and serving. Treat the prevention checklist as a guardrail: any pipeline that touches a trained checkpoint should re-validate the failure mode before promoting the model.

What's the cheapest way to confirm this is the problem?

Cheapest signal: reproduce the symptom on a 1-GPU toy run with the same configuration. If the toy run exhibits the same behavior, the cause is the configuration rather than the cluster. If only the multi-GPU run is affected, the failure is a distributed interaction, so NVIDIA GPU Operator is the most likely layer to inspect.

Can the Denpex platform detect this automatically?

Yes. Denpex correlates the symptoms in this entry against your training logs in real time and surfaces the failure taxonomy, root cause, and exact remediation command on the console. Three full diagnoses are included free during signup, and the encyclopedia entry you are reading is generated from the same signal library that powers the platform.

What related failures should I check next?

Closely related failures cluster around #Isolation. After applying this fix, validate against the related entries below to confirm no overlapping cause is masking as this one. Engineers routinely discover that the dominant failure mode is actually a second, sibling failure compounded on top.

Will this recur after the fix?

Applied once with the prevention checklist, this failure does not recur under normal operation. The recurring variant most engineers encounter is the re-emergence of the same symptom on a different model or stack after Isolation is upgraded. Pin compatible versions and rerun the prevention smoke test on every dependency change.

Is there a fingerprint I can monitor?

Yes. A newly scheduled pod immediately crashes with an OOM error upon startup. is the highest-signal fingerprint. Wire a metric or alert on that signal (or its absence after the fix) so you can detect the recurrence automatically. The Denpex platform encodes this exact fingerprint and surfaces it directly in the dashboard.

What is the official documentation that covers this?

Reference documentation is listed in the References section at the bottom of this entry. The most authoritative URLs are the upstream framework docs and the issue trackers cited; the Denpex entry synthesizes both into the executive, engineer, and on-call perspectives so each audience can act without reading every linked source.

Does this entry include working code?

Yes. The "Code Examples" section contains copy-paste-ready snippets that target the most common stacks (PyTorch, Hugging Face Trainer, DeepSpeed, Megatron-LM, vLLM/Transformers). Each snippet is annotated with why it works so you can adapt the pattern to your own framework without losing fidelity.

Time-Slicing Cross-Pod VRAM Exhaustion

Q: What does this failure look like in production?

In production this failure typically appears during the first tens-to-hundreds of training steps: loss curves become non-monotonic, validation metrics plateau above the baseline, and downstream checkpoints either diverge or fail to improve. Engineers frequently misread it as data drift when the underlying mechanism is kubernetes gpu time-slicing multiplexes compute execution time, but it provides absolutely zero hardware-level memory isolation. if a physical gpu with 24gb vram is time-sliced into 4 logical gpus, kubernetes allows scheduling 4 pods to it. if pod a (the 'noisy neighbor') allocates 20gb of vram, pods b, c, and d will instantly encounter cuda oom errors because the physical memory pool is exhausted, despite kubernetes acting as if they each possess dedicated resources.. Validating against the symptom list in this entry is the fastest way to confirm the diagnosis.

Q: How does this scale from 1 GPU to 1000 GPUs?

At single-GPU scale the symptom usually looks like slow convergence or a single anomalous loss value. At 64+ GPU scale, distributed interactions amplify kubernetes gpu time-slicing multiplexes compute execution time, but it provides absolutely zero hardware-level memory isolation. if a physical gpu with 24gb vram is time-sliced into 4 logical gpus, kubernetes allows scheduling 4 pods to it. if pod a (the 'noisy neighbor') allocates 20gb of vram, pods b, c, and d will instantly encounter cuda oom errors because the physical memory pool is exhausted, despite kubernetes acting as if they each possess dedicated resources. through collectives, and the failure can manifest as a full job abort. Always reproduce the fix at the smallest cluster configuration that still exhibits the symptom before promoting to production scale.

Kubernetes GPU time-slicing multiplexes compute execution time, but it provides absolutely zero hardware-level memory isolation. If a physical GPU with 24GB VRAM is time-sliced into 4 logical GPUs, Kubernetes allows scheduling 4 pods to it. If Pod A (the 'noisy neighbor') allocates 20GB of VRAM, Pods B, C, and D will instantly encounter CUDA OOM errors because the physical memory pool is exhausted, despite Kubernetes acting as if they each possess dedicated resources.

Quick answer

Kubernetes GPU time-slicing multiplexes compute execution time, but it provides absolutely zero hardware-level memory isolation.

Root cause: Kubernetes GPU time-slicing multiplexes compute execution time, but it provides absolutely zero hardware-level memory isolation. If a physical GPU with 24GB VRAM is time-sliced into 4 logical GPUs, Kubernetes allows scheduling 4 pods to it. If Pod A (the 'noisy neighbor') allocates 20GB of VRAM, Pods B, C, and D will instantly encounter CUDA OOM errors because the physical memory pool is exhausted, despite Kubernetes acting as if they each possess dedicated resources.
Recommended fix: Enforce Application-Level Memory Limits For PyTorch: torch.cuda.set_per_process_memory_fraction(0.25) / For TF: set memory_growth or virtual_device memory limit. Since the infrastructure cannot isolate the memory, the applications must be configured to artificially restrict their own VRAM allocation, preventing them from stepping on each other.
How Denpex helps: Denpex matches Time-Slicing Cross-Pod VRAM Exhaustion across every rank in a distributed run and reports which rank failed first, so you act on the initiating node instead of the loudest one.

Memory#Isolation

What this failure is

Time-Slicing Cross-Pod VRAM Exhaustion is a Memory failure seen during ML training runs. Kubernetes GPU time-slicing multiplexes compute execution time, but it provides absolutely zero hardware-level memory isolation. If a physical GPU with 24GB VRAM is time-sliced into 4 logical GPUs, Kubernetes allows scheduling 4 pods to it. If Pod A (the 'noisy neighbor') allocates 20GB of VRAM, Pods B, C, and D will instantly encounter CUDA OOM errors because the physical memory pool is exhausted, despite Kubernetes acting as if they each possess dedicated resources. Common tags: Isolation.

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Why it happens (the mechanism)

The user requested 'nvidia.com/gpu: 1' in their pod spec, which conventionally implies exclusive access to a GPU. The scheduling succeeds, so the user assumes they have sufficient resources. The failure appears non-deterministic because it depends entirely on the concurrent memory usage of other tenants on the same physical card.

What you'll observe

CUDA_ERROR_OUT_OF_MEMORY during context initialization
Failed to allocate memory for the GPU context

Common symptoms and what they mean

Symptom	Why it happens
A newly scheduled pod immediately crashes with an OOM error upon startup.	Kubernetes GPU time-slicing multiplexes compute execution time, but it provides absolutely zero hardware-level memory isolation. If a physical GPU with 24GB VRAM is time-sliced into 4 logical GPUs, Kubernetes allows scheduling 4 pods to it. If Pod A (the 'noisy neighbor') allocates 20GB of VRAM, Pods B, C, and D will instantly encounter CUDA OOM errors because the physical memory pool is exhausted, despite Kubernetes acting as if they each possess dedicated resources.
Existing, previously stable pods suddenly crash with CUDA OOMs at random intervals.	Kubernetes GPU time-slicing multiplexes compute execution time, but it provides absolutely zero hardware-level memory isolation. If a physical GPU with 24GB VRAM is time-sliced into 4 logical GPUs, Kubernetes allows scheduling 4 pods to it. If Pod A (the 'noisy neighbor') allocates 20GB of VRAM, Pods B, C, and D will instantly encounter CUDA OOM errors because the physical memory pool is exhausted, despite Kubernetes acting as if they each possess dedicated resources.
Occurs exclusively on nodes where GPU time-slicing (e.g., replicas: 4) is enabled.	Kubernetes GPU time-slicing multiplexes compute execution time, but it provides absolutely zero hardware-level memory isolation. If a physical GPU with 24GB VRAM is time-sliced into 4 logical GPUs, Kubernetes allows scheduling 4 pods to it. If Pod A (the 'noisy neighbor') allocates 20GB of VRAM, Pods B, C, and D will instantly encounter CUDA OOM errors because the physical memory pool is exhausted, despite Kubernetes acting as if they each possess dedicated resources.

Which systems are affected

NVIDIA GPU Operator
Time-Slicing Device Plugin

How to confirm this is the problem

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.

✓Check node configuration to confirm time-slicing is active: kubectl get node -o yaml | grep 'nvidia.com/gpu.replicas'
✓Run 'nvidia-smi' on the node to view the aggregated memory usage of all processes sharing the GPU.

The fix and the prevention pattern

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Diagnose this failure in VS Code

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Root cause

Kubernetes GPU time-slicing multiplexes compute execution time, but it provides absolutely zero hardware-level memory isolation. If a physical GPU with 24GB VRAM is time-sliced into 4 logical GPUs, Kubernetes allows scheduling 4 pods to it. If Pod A (the 'noisy neighbor') allocates 20GB of VRAM, Pods B, C, and D will instantly encounter CUDA OOM errors because the physical memory pool is exhausted, despite Kubernetes acting as if they each possess dedicated resources.

The fix and how to prevent it

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References

↗NVIDIA GPU Operator Time-Slicing Documentation

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