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 ucx registers each gpu buffer on every reachable hca (all 8 nics per node) creating firmware page duplication factor of n_nics. Validating against the symptom list in this entry is the fastest way to confirm the diagnosis.

Is this a hardware or a software issue?

Reliability 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 reliability 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 HGX H200 and HGX B200 with 8 NICs per node 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 #crusoe, #nixl, #ucx. 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 crusoe is upgraded. Pin compatible versions and rerun the prevention smoke test on every dependency change.

Is there a fingerprint I can monitor?

Yes. /proc/meminfo shows MemAvailable = 0 with buffers/cache fully reclaimed during NIXL-heavy workloads 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.

NIXL Firmware Page Registration Fan-Out Triggers Host OOM Kills on HGX H200 and B200

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 ucx registers each gpu buffer on every reachable hca (all 8 nics per node) creating firmware page duplication factor of n_nics 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.

NVIDIA Inference Xfer Library workloads on HGX H200 and HGX B200 instances triggered host OOM kills because UCX registered GPU memory as firmware pages across all 8 NICs simultaneously, consuming ~34 GiB of host memory per GPU in firmware-level registrations. The root cause was traced to UCX registering every reachable HCA for each buffer, creating firmware page fan-out proportional to NIC count.

Quick answer

Symptom: Kernel dmesg: "Out of memory: Killed process <PID> (python)" with no GPU-side errors
Root cause: UCX registers each GPU buffer on every reachable HCA (all 8 NICs per node) creating firmware page duplication factor of N_nics. Each firmware page consumes ~35 KB of locked host memory per registration entry. With 75 GB registered per GPU * 8 NICs, firmware overhead reaches ~34 GiB of host memory before any workload memory.
Recommended fix: UCX_MAX_HCA_PER_GPU=auto
How Denpex helps: Denpex investigates NIXL Firmware Page Registration Fan-Out Triggers Host OOM Kills on HGX H200 and B200 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.

Reliability#crusoe#nixl#ucx#firmware-page#host-oom#h200

What this failure is

NIXL Firmware Page Registration Fan-Out Triggers Host OOM Kills on HGX H200 and B200 is a Reliability failure seen during ML training runs. NVIDIA Inference Xfer Library workloads on HGX H200 and HGX B200 instances triggered host OOM kills because UCX registered GPU memory as firmware pages across all 8 NICs simultaneously, consuming ~34 GiB of host memory per GPU in firmware-level registrations. The root cause was traced to UCX registering every reachable HCA for each buffer, creating firmware page fan-out proportional to NIC count. Common tags: Crusoe, Nixl, Ucx, Firmware Page.

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Why it happens (the mechanism)

UCX registers each GPU buffer on every reachable HCA (all 8 NICs per node) creating firmware page duplication factor of N_nics. Each firmware page consumes ~35 KB of locked host memory per registration entry. With 75 GB registered per GPU * 8 NICs, firmware overhead reaches ~34 GiB of host memory before any workload memory. Taken together, these mechanisms explain why the failure is reproducible, why it tends to surface on specific workloads or scales, and why generic mitigation attempts often fall short without addressing the underlying cause.

What you'll observe

Host processes killed by OOM killer with GPUs at normal HBM utilization levels
MemAvailable in /proc/meminfo drops to zero despite adequate host RAM capacity
Swap fills up and kernel OOM killer activates without GPU memory pressure

Common symptoms and what they mean

Symptom	Why it happens
/proc/meminfo shows MemAvailable = 0 with buffers/cache fully reclaimed during NIXL-heavy workloads	UCX registers each GPU buffer on every reachable HCA (all 8 NICs per node) creating firmware page duplication factor of N_nics
fw_pages_total in debug counters shows >1 million firmware pages per NIC for ~75 GB registered per GPU	Each firmware page consumes ~35 KB of locked host memory per registration entry
Kernel dmesg: "Out of memory: Killed process <PID> (python)" with no GPU-side errors	With 75 GB registered per GPU * 8 NICs, firmware overhead reaches ~34 GiB of host memory before any workload memory

Which systems are affected

NVIDIA HGX H200 and HGX B200 with 8 NICs per node
NIXL workloads with GPU memory registration exceeding ~75 GB per GPU
UCX-based communication (UCX 1.20 and earlier)
KVM-virtualized GPU instances with PCIe passthrough

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.

✓Reproduce the failure from a clean checkpoint/seed: the symptom must appear without warm-up state from a previous run.
✓Verified signal present: /proc/meminfo shows MemAvailable = 0 with buffers/cache fully reclaimed during NIXL-heavy workloads
✓Verified signal present: fw_pages_total in debug counters shows >1 million firmware pages per NIC for ~75 GB registered per GPU
✓Verified signal present: Kernel dmesg: "Out of memory: Killed process <PID> (python)" with no GPU-side errors
✓A targeted fix from the "How to fix it" section eliminates or substantially reduces the symptom within one validation pass.

Root cause

UCX registers each GPU buffer on every reachable HCA (all 8 NICs per node) creating firmware page duplication factor of N_nics
Each firmware page consumes ~35 KB of locked host memory per registration entry
With 75 GB registered per GPU * 8 NICs, firmware overhead reaches ~34 GiB of host memory before any workload memory

The fix and how to prevent it

Searchable error signature

search key

Kernel dmesg: "Out of memory: Killed process <PID> (python)" with no GPU-side errors
/proc/meminfo shows MemAvailable = 0 with buffers/cache fully reclaimed during NIXL-heavy workloads
MemAvailable in /proc/meminfo drops to zero despite adequate host RAM capacity

Use this text as a lookup key in logs and upstream issue trackers. It is not presented as a captured customer log. Confirm the cause from your own preceding events, versions, configuration and the cited references.

The fix and the prevention pattern

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