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 the user requested a combination of resources (like --mem-per-cpu combined with a high --ntasks) that exceeds the physical hardware capacity of any single node within the specified partition. often caused by confusing --mem (total memory per node) with --mem-per-cpu.. Validating against the symptom list in this entry is the fastest way to confirm the diagnosis.

Is this a hardware or a software issue?

Resource Allocation 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 resource allocation 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 SLURM 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 #Constraint Mismatch. 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 Constraint Mismatch is upgraded. Pin compatible versions and rerun the prevention smoke test on every dependency change.

Is there a fingerprint I can monitor?

Yes. The job is immediately rejected upon submission via sbatch or salloc. 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.

Requested node configuration is not available (Memory/CPU Mismatch)

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 the user requested a combination of resources (like --mem-per-cpu combined with a high --ntasks) that exceeds the physical hardware capacity of any single node within the specified partition. often caused by confusing --mem (total memory per node) with --mem-per-cpu. 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.

The user requested a combination of resources (like --mem-per-cpu combined with a high --ntasks) that exceeds the physical hardware capacity of any single node within the specified partition. Often caused by confusing --mem (total memory per node) with --mem-per-cpu.

Quick answer

The user requested a combination of resources (like --mem-per-cpu combined with a high --ntasks) that exceeds the physical hardware capacity of any single node within the specified partition.

Root cause: The user requested a combination of resources (like --mem-per-cpu combined with a high --ntasks) that exceeds the physical hardware capacity of any single node within the specified partition. Often caused by confusing --mem (total memory per node) with --mem-per-cpu.
Recommended fix: Adjust memory or CPU requirements Modify #SBATCH --mem=<value> or --mem-per-cpu=<value> in the job script to fit within the limits shown by sinfo. Ensures the requested resources match the physical capabilities of the available nodes.
How Denpex helps: Denpex investigates Requested node configuration is not available (Memory/CPU Mismatch) using the evidence you provide or your connected workload collects. Earlier rank, host or application evidence is needed to distinguish an initiating failure from a downstream report.

Resource Allocation#Constraint Mismatch

What this failure is

Requested node configuration is not available (Memory/CPU Mismatch) is a Resource Allocation failure seen during ML training runs. The user requested a combination of resources (like --mem-per-cpu combined with a high --ntasks) that exceeds the physical hardware capacity of any single node within the specified partition. Often caused by confusing --mem (total memory per node) with --mem-per-cpu. Common tags: Constraint Mismatch.

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Why it happens (the mechanism)

The error message is generic and does not specify which exact resource constraint (memory, CPU, GPUs, or walltime) caused the rejection, making users think the cluster is down or they don't have access.

What you'll observe

sbatch error: Batch job submission failed: Requested node configuration is not available
salloc: error: Requested node configuration is not available

Common symptoms and what they mean

Symptom	Why it happens
The job is immediately rejected upon submission via sbatch or salloc.	The user requested a combination of resources (like --mem-per-cpu combined with a high --ntasks) that exceeds the physical hardware capacity of any single node within the specified partition. Often caused by confusing --mem (total memory per node) with --mem-per-cpu.
No job ID is returned, and the job never enters the queue.	The user requested a combination of resources (like --mem-per-cpu combined with a high --ntasks) that exceeds the physical hardware capacity of any single node within the specified partition. Often caused by confusing --mem (total memory per node) with --mem-per-cpu.

Which systems are affected

SLURM

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.

✓Run `sinfo -O partition,memory,cpus` to see the actual hardware limits of the target partition.
✓Run `scontrol show node <nodename>` to check specific node memory and core counts.
✓Check the submission script for conflicting `-n` (tasks) and `-N` (nodes) flags.

Root cause

The user requested a combination of resources (like --mem-per-cpu combined with a high --ntasks) that exceeds the physical hardware capacity of any single node within the specified partition. Often caused by confusing --mem (total memory per node) with --mem-per-cpu.

The fix and how to prevent it

The fix and the prevention pattern

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Diagnose this failure in VS Code

Select the traceback or open the failed terminal, then run Denpex locally to see the initiating rank, collateral failures, exact fix, and verification command without uploading the log.

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References

↗Slurm Documentation - Troubleshooting

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