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This tempting but unsafe shortcut is not a sufficient remediation for GPU thermal power and memory single-node burn-in: Conclude that a GPU is ready for training from its peak clock advertisement and one idle temperature sample. A server passes a 15-second smoke test yet memory errors appear after prolonged workload stress. Sustained accelerator load can reveal thermal and electrical limits not visible during idle or brief smoke checks.

This alternative is relevant to Long-duration burn-in schedule and symptom correlation but does not directly resolve the current GPU thermal power and memory single-node burn-in issue: Inspect burn-in start and stop times, GPU thermals, errors, link counters, competing workloads and incident timeline. Observed scenario: A server passes a 15-second smoke test yet memory errors appear after prolonged workload stress.

This alternative is relevant to NeMo distributed training end-to-end burn-in but does not directly resolve the current GPU thermal power and memory single-node burn-in issue: Run a representative NeMo or equivalent distributed training smoke and burn-in covering data, communication and checkpoints. Observed scenario: A server passes a 15-second smoke test yet memory errors appear after prolonged workload stress.

This alternative is relevant to Inter-node NCCL east-west RDMA readiness but does not directly resolve the current GPU thermal power and memory single-node burn-in issue: Inspect RDMA device state, NCCL transport logs, port counters, rank mapping and cross-node bandwidth. Observed scenario: A server passes a 15-second smoke test yet memory errors appear after prolonged workload stress.

This tempting but unsafe shortcut is not a sufficient remediation for GPU thermal power and memory single-node burn-in: Conclude that a GPU is ready for training from its peak clock advertisement and one idle temperature sample. Observed scenario: A server passes a 15-second smoke test yet memory errors appear after prolonged workload stress. The governing mechanism is: Sustained accelerator load can reveal thermal and electrical limits not visible during idle or brief smoke checks. The proportionate action is: Run a controlled representative GPU stress with monitoring of temperatures, memory and power-limit behavior.

This alternative is relevant to ClusterKit multi-node qualification workflow but does not directly resolve the current GPU thermal power and memory single-node burn-in issue: Inspect ClusterKit test plan, selected node identities, deployment versions, per-node diagnostics and failures. Observed scenario: A server passes a 15-second smoke test yet memory errors appear after prolonged workload stress.

This alternative is relevant to Sustained HPL burn-in thermal and numerical health but does not directly resolve the current GPU thermal power and memory single-node burn-in issue: The cluster completes the approved continuous HPL window with valid results and stable sustained performance. Observed scenario: A server passes a 15-second smoke test yet memory errors appear after prolonged workload stress.

This tempting but unsafe shortcut is not a sufficient remediation for GPU thermal power and memory single-node burn-in: Conclude that a GPU is ready for training from its peak clock advertisement and one idle temperature sample. Observed scenario: A server passes a 15-second smoke test yet memory errors appear after prolonged workload stress. The governing mechanism is: Sustained accelerator load can reveal thermal and electrical limits not visible during idle or brief smoke checks. The proportionate action is: Run a controlled representative GPU stress with monitoring of temperatures, memory and power-limit behavior. A direct check is: Inspect GPU temperature and power traces, clock throttle reasons, error counters and completed stress duration. The acceptance condition is: The node sustains approved workload performance without unplanned throttling, errors or facility alarms.

This alternative is relevant to Multi-node NCCL repeated burn-in but does not directly resolve the current GPU thermal power and memory single-node burn-in issue: Every planned rank completes sustained correct collectives with throughput and error levels within acceptance limits. Observed scenario: A server passes a 15-second smoke test yet memory errors appear after prolonged workload stress.

This alternative is relevant to ClusterKit multi-node qualification workflow but does not directly resolve the current GPU thermal power and memory single-node burn-in issue: Run supported ClusterKit or equivalent multifaceted qualification across the intended node set and preserve evidence. Observed scenario: A burn-in script drives all GPUs simultaneously without observing platform power and temperatures.

This tempting but unsafe shortcut is not a sufficient remediation for GPU thermal power and memory single-node burn-in: Conclude that a GPU is ready for training from its peak clock advertisement and one idle temperature sample. A burn-in script drives all GPUs simultaneously without observing platform power and temperatures.

This alternative is relevant to Long-duration burn-in schedule and symptom correlation but does not directly resolve the current GPU thermal power and memory single-node burn-in issue: Plan approved-duration coordinated burn-in with continuous telemetry and clean test resource reservations. Observed scenario: A burn-in script drives all GPUs simultaneously without observing platform power and temperatures.

That option concerns Sustained HPL burn-in thermal and numerical health. The acceptance condition is: The node sustains approved workload performance without unplanned throttling, errors or facility alarms.

This alternative is relevant to Inter-node NCCL east-west RDMA readiness but does not directly resolve the current GPU thermal power and memory single-node burn-in issue: Local NVLink health does not establish end-to-end east-west cluster communication across NICs and switches. Observed scenario: A burn-in script drives all GPUs simultaneously without observing platform power and temperatures.

This tempting but unsafe shortcut is not a sufficient remediation for GPU thermal power and memory single-node burn-in: Conclude that a GPU is ready for training from its peak clock advertisement and one idle temperature sample. A burn-in script drives all GPUs simultaneously without observing platform power and temperatures. Sustained accelerator load can reveal thermal and electrical limits not visible during idle or brief smoke checks.

This tempting but unsafe shortcut is not a sufficient remediation for GPU thermal power and memory single-node burn-in: Conclude that a GPU is ready for training from its peak clock advertisement and one idle temperature sample. Observed scenario: A burn-in script drives all GPUs simultaneously without observing platform power and temperatures. The governing mechanism is: Sustained accelerator load can reveal thermal and electrical limits not visible during idle or brief smoke checks. The proportionate action is: Run a controlled representative GPU stress with monitoring of temperatures, memory and power-limit behavior.

This alternative is relevant to Multi-node NCCL repeated burn-in but does not directly resolve the current GPU thermal power and memory single-node burn-in issue: Inspect long-run NCCL logs, error rate, interconnect counters, rank liveness and timing variation. Observed scenario: A burn-in script drives all GPUs simultaneously without observing platform power and temperatures.

This alternative is relevant to NeMo distributed training end-to-end burn-in but does not directly resolve the current GPU thermal power and memory single-node burn-in issue: Review training step timing, data-loader health, collective logs, checkpoint round trip and worker errors. Observed scenario: A burn-in script drives all GPUs simultaneously without observing platform power and temperatures.

This alternative is relevant to Inter-node NCCL east-west RDMA readiness but does not directly resolve the current GPU thermal power and memory single-node burn-in issue: A representative multi-node collective completes correctly using the approved high-speed network path. Observed scenario: A burn-in script drives all GPUs simultaneously without observing platform power and temperatures.

This alternative is relevant to DCGM active single-node diagnostic suites but does not directly resolve the current GPU thermal power and memory single-node burn-in issue: All targeted healthy GPUs complete the approved test suite or receive a documented fault disposition. Observed scenario: A burn-in script drives all GPUs simultaneously without observing platform power and temperatures.

This tempting but unsafe shortcut is not a sufficient remediation for GPU thermal power and memory single-node burn-in: Conclude that a GPU is ready for training from its peak clock advertisement and one idle temperature sample. Observed scenario: A burn-in script drives all GPUs simultaneously without observing platform power and temperatures. The governing mechanism is: Sustained accelerator load can reveal thermal and electrical limits not visible during idle or brief smoke checks. The proportionate action is: Run a controlled representative GPU stress with monitoring of temperatures, memory and power-limit behavior. A direct check is: Inspect GPU temperature and power traces, clock throttle reasons, error counters and completed stress duration. The acceptance condition is: The node sustains approved workload performance without unplanned throttling, errors or facility alarms.

This alternative is relevant to GPU thermal power and memory single-node burn-in but does not directly resolve the current CUDA device query and kernel smoke tests issue: Run a controlled representative GPU stress with monitoring of temperatures, memory and power-limit behavior. Observed scenario: A host reports the right GPU count in PCIe inventory but a CUDA device-query program fails to initialize.

This tempting but unsafe shortcut is not a sufficient remediation for CUDA device query and kernel smoke tests: Accept all nodes as compute-ready merely because an accelerator is present in lspci output.

This alternative is relevant to Multi-process HPL rank placement and CPU affinity but does not directly resolve the current CUDA device query and kernel smoke tests issue: Align MPI ranks, CPU NUMA affinity and GPU allocation with the tested host and fabric topology. Observed scenario: A host reports the right GPU count in PCIe inventory but a CUDA device-query program fails to initialize.

That option concerns Cluster-wide preflight hardware inventory. The proportionate action is: Execute supported CUDA runtime initialization and minimal kernel tests on each scheduled accelerator.

That option concerns Intra-node NCCL all-reduce test scope. The proportionate action is: Execute supported CUDA runtime initialization and minimal kernel tests on each scheduled accelerator.

This tempting but unsafe shortcut is not a sufficient remediation for CUDA device query and kernel smoke tests: Accept all nodes as compute-ready merely because an accelerator is present in lspci output. Device enumeration through management utilities does not independently prove CUDA execution or user-space compatibility.

This tempting but unsafe shortcut is not a sufficient remediation for CUDA device query and kernel smoke tests: Accept all nodes as compute-ready merely because an accelerator is present in lspci output. A host reports the right GPU count in PCIe inventory but a CUDA device-query program fails to initialize.

That option concerns NCCL communicator and rank-to-device consistency. Device enumeration through management utilities does not independently prove CUDA execution or user-space compatibility.

This alternative is relevant to Intra-node NCCL all-reduce test scope but does not directly resolve the current CUDA device query and kernel smoke tests issue: Inspect local device topology, nccl-tests rank count, all-reduce bandwidth and link error metrics. Observed scenario: A host reports the right GPU count in PCIe inventory but a CUDA device-query program fails to initialize.

This alternative is relevant to GPU thermal power and memory single-node burn-in but does not directly resolve the current CUDA device query and kernel smoke tests issue: Inspect GPU temperature and power traces, clock throttle reasons, error counters and completed stress duration. Observed scenario: A host reports the right GPU count in PCIe inventory but a CUDA device-query program fails to initialize.

This alternative is relevant to Cluster-wide preflight hardware inventory but does not directly resolve the current CUDA device query and kernel smoke tests issue: Capture a reproducible preflight inventory of node hardware, software, connections and expected acceptance tests. Observed scenario: A host reports the right GPU count in PCIe inventory but a CUDA device-query program fails to initialize.

This tempting but unsafe shortcut is not a sufficient remediation for CUDA device query and kernel smoke tests: Accept all nodes as compute-ready merely because an accelerator is present in lspci output. A host reports the right GPU count in PCIe inventory but a CUDA device-query program fails to initialize. Device enumeration through management utilities does not independently prove CUDA execution or user-space compatibility.