NVIDIA NCP-AII Questions & Answers

The suggested action targets another problem where Mechanical fit does not imply the module supports the electrical envelope of a high-power GPU chassis. It does not directly address this case, which needs Drain and power down the affected node and follow qualified chassis and ESD service instructions.

The option would apply to “A failed network adapter is replaced but the cluster job scheduler still references the prior interface and HCA identity”. Therefore Update documented adapter identity, firmware, fabric mapping and host/cluster configuration after replacement. This question instead requires “Drain and power down the affected node and follow qualified chassis and ESD service instructions”.

The related but distinct situation is “A technician replaces a GPU successfully but does not reapply the workload partition profile required for the tenant scheduler”. The appropriate intervention is Restore and validate the approved MIG or allocation configuration after the hardware replacement. For the stated problem, “Drain and power down the affected node and follow qualified chassis and ESD service instructions” addresses the central condition.

This alternative addresses the separate case “A node experiences intermittent network errors only during peak HPL and NCCL workload overlap, and the team assumes it is a faulty transceiver”. The operational evidence follows from the mechanism that Power transients and shared-resource pressure can manifest as network instability under simultaneous load. For the stated problem, “Thermal throttling can reduce compute progress and create collective stragglers without an NCCL defect” addresses the central condition.

The related but distinct situation is “A multi-GPU server repeatedly loses one NIC link when its fan tray slows under load”. The controlling explanation is that Cooling failures can indirectly destabilize network or accelerator components exposed to excess heat. The deciding requirement here is “Thermal throttling can reduce compute progress and create collective stragglers without an NCCL defect”.

This option serves the scenario “A training node reports intermittent GPU Xid faults during collective operations but its nvidia-smi inventory remains normal”. The operational evidence follows from the mechanism that A GPU can enumerate normally while intermittent compute or memory faults occur under particular workloads. This question instead requires “Thermal throttling can reduce compute progress and create collective stragglers without an NCCL defect”.

The option would apply to “A BCM operator thinks the named primary head must always be active, even after the secondary has taken over successfully”. A completed remediation must demonstrate that An HA status check identifies the secondary as active with services working after a controlled takeover. This question instead requires “A controlled failover confirms only one authorized active writer and successful fencing of the retired active node”.

The related but distinct situation is “A network partition causes a passive BCM head to see the active head as unreachable, and an operator immediately promotes it without quorum checks”. A completed remediation must demonstrate that The failover test produces consistent quorum decisions, known active ownership and no simultaneous shared-volume mounts. For the stated problem, “A controlled failover confirms only one authorized active writer and successful fencing of the retired active node” addresses the central condition.

This option serves the scenario “A backup head node has no configured remote power-fencing capability, but the team expects automatic recovery from any loss of heartbeat”. The decisive evidence is that A planned failure demonstration follows the documented automatic or manual activation and fencing sequence. The present scenario is distinguished by “A controlled failover confirms only one authorized active writer and successful fencing of the retired active node”.

The suggested action targets another problem where AI workloads combine large sequential datasets with metadata-intensive checkpoint and restart behavior. It does not directly address this case, which needs Investigate data-integrity errors and reject the storage acceptance until affected paths are corrected.

For this finding, Measure aggregate read throughput and metadata latency with the intended node concurrency is less direct than Investigate data-integrity errors and reject the storage acceptance until affected paths are corrected, because Throughput is not sufficient evidence that training data and checkpoints retain integrity.

The option would apply to “A team reports healthy storage throughput from the login node but GPU compute nodes use different mounts and client networking”. Therefore Benchmark reads and writes from the actual scheduled compute nodes and their storage interfaces. The present scenario is distinguished by “Investigate data-integrity errors and reject the storage acceptance until affected paths are corrected”.

This option serves the scenario “A new breakout cable passes continuity checks but its highest-speed lanes show repeated correctable errors under traffic”. The operational evidence follows from the mechanism that A continuity test cannot certify sufficient signal integrity at the actual configured data rate. The present scenario is distinguished by “Physical cable handling and nearby power paths can affect signal quality without changing logical configuration”.

The option would apply to “A 400G optical fabric link remains up but forward error correction counters increase rapidly during NCCL burn-in”. The key distinction is that Link-up status can hide marginal high-speed electrical or optical signal integrity. This question instead requires “Physical cable handling and nearby power paths can affect signal quality without changing logical configuration”.

This statement could explain another scenario: A technician uses an optical transceiver with unsupported insertion loss and the switch intermittently changes link speed. In the present case the more specific mechanism is that Physical cable handling and nearby power paths can affect signal quality without changing logical configuration.

This would be a meaningful test for another issue (An HPL result is announced as peak GPU FLOPS without recording matrix size, precision, GPU count or the HPL implementation), but not for the required outcome: A series of HPL runs across restarts stays within accepted throughput and correctness tolerance.

This would be a meaningful test for another issue (A cluster uses tiny HPL matrices that fit entirely in cache and reports unrealistically high efficiency for the intended training systems), but not for the required outcome: A series of HPL runs across restarts stays within accepted throughput and correctness tolerance.

This option serves the scenario “An HPL burn-in on eight GPUs has one device consistently 30 percent slower while others remain close to baseline”. A completed remediation must demonstrate that A corrected run shows the outlier device within the supported performance spread and no hardware errors. This question instead requires “A series of HPL runs across restarts stays within accepted throughput and correctness tolerance”.

The option would apply to “A network adapter shows link up but its firmware was never compared with the approved NVIDIA driver and switch stack”. The direct failure mode is that An incompatible NIC firmware combination can manifest only under collective or high-throughput load. This question instead requires “Undiagnosed PCIe faults can silently reduce bandwidth or trigger workload crashes later”.

The related but distinct situation is “An engineer updates GPU board firmware outside the planned change window while the cluster scheduler continues submitting training jobs”. Without correction, Uncoordinated firmware changes can terminate jobs and produce untraceable performance regressions. For the stated problem, “Undiagnosed PCIe faults can silently reduce bandwidth or trigger workload crashes later” addresses the central condition.

This option serves the scenario “An AI factory firmware update changes BMC inventory fields and a monitoring dashboard now reports healthy GPUs as missing”. Without correction, A broken management schema can hide real component faults under apparently successful patching. The present scenario is distinguished by “Undiagnosed PCIe faults can silently reduce bandwidth or trigger workload crashes later”.

This option addresses an alternative scenario (Two of eight GPUs report persistent PCIe replay errors but the installer replaces every accelerator in the chassis); the reported issue instead calls for Map BMC slot identifiers and physical fan positions from the qualified chassis service documentation.

This option serves the scenario “A high-speed network card vanishes from a node after a power cycle while other cards remain healthy”. Therefore Check PCI enumeration, slot power, cable seating and platform event logs for the affected adapter. This question instead requires “Map BMC slot identifiers and physical fan positions from the qualified chassis service documentation”.

This alternative addresses the separate case “A GPU is absent from the expected device inventory after maintenance, but the technician proposes reinstalling CUDA before checking hardware state”. Therefore Compare BMC inventory, lspci, physical seating and power connections to identify an actually missing device. For the stated problem, “Map BMC slot identifiers and physical fan positions from the qualified chassis service documentation” addresses the central condition.

That observation does not establish operating effectiveness of Select a supported cable type and length for the specific port, speed and installation distance. The needed evidence is that Link diagnostics show supported cable identity and stable error-free operation at target lane rate.

The proposed validation does not isolate the reported fault. An effective closure test requires evidence that Link diagnostics show supported cable identity and stable error-free operation at target lane rate.

The related but distinct situation is “A dual-rail rack has all high-speed ports connected but two cables are accidentally crossed between fabrics”. The decisive evidence is that A cable-to-port reconciliation and rail-specific path test match every documented endpoint. For the stated problem, “Link diagnostics show supported cable identity and stable error-free operation at target lane rate” addresses the central condition.

The suggested action targets another problem where CPU health testing cannot substitute for GPU memory and data-path validation under AI workloads. It does not directly address this case, which needs Analyze ECC and hardware event trends and isolate suspect memory or accelerator components before production release.

The related but distinct situation is “A node's BIOS reports a NUMA topology different from the approved design after a firmware reset”. The direct fix is Check and restore documented processor, memory and PCIe topology settings and remeasure workload affinity. The deciding requirement here is “Analyze ECC and hardware event trends and isolate suspect memory or accelerator components before production release”.

That change may help in a different context, but it would not establish that A controlled burn-in shows stable error counters under the vendor acceptance threshold and successful diagnostics. The relevant response is Analyze ECC and hardware event trends and isolate suspect memory or accelerator components before production release.

The option would apply to “A new NVIDIA GPU server boots Linux, but the operator never checks whether all expected PCIe accelerators enumerate”. A completed remediation must demonstrate that The node reports all expected GPU PCI IDs and nvidia-smi devices without initialization errors. This question instead requires “A driver health check enumerates GPUs through nvidia-smi and a representative CUDA operation completes”.

The related but distinct situation is “An operator runs an AI application on a fresh server but does not record the GPU driver version or actual compute device selection”. A completed remediation must demonstrate that A fixed smoke job reports the selected GPU UUID, driver revision and expected compute result. For the stated problem, “A driver health check enumerates GPUs through nvidia-smi and a representative CUDA operation completes” addresses the central condition.

This option serves the scenario “A technician deploys a server image that includes a newer CUDA userspace than the installed driver can support”. A completed remediation must demonstrate that A sample CUDA binary launches on each GPU and reports the intended runtime and driver versions. The present scenario is distinguished by “A driver health check enumerates GPUs through nvidia-smi and a representative CUDA operation completes”.