NVIDIA NCA-AIIO Bundle

Not the most direct the action-and-evidence pair for these facts. Compare supported vGPU or time-sliced sharing, passthrough and MIG-backed virtualization according to VM isolation, licensing, performance and hardware support requirements. This choice concerns virtual gpus and gpu sharing approaches and may be valid in another design. But this case reports 'Distributed ranks are scattered across oversubscribed network paths without topology review', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.

Not the most direct the action-and-evidence pair for these facts. Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices. This choice concerns Kubernetes gpu device scheduling and discovery and may be valid in another design. But this case reports 'Distributed ranks are scattered across oversubscribed network paths without topology review', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.

Not the most direct the action-and-evidence pair for these facts. A sampled node's actual firmware, GPU, driver and fabric settings match the approved inventory or show a tracked exception. This choice concerns cluster inventory firmware and configuration baseline and may be valid in another design. But this case reports 'Distributed ranks are scattered across oversubscribed network paths without topology review', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.

Not the most direct the corrective action for these facts. Compare supported vGPU or time-sliced sharing, passthrough and MIG-backed virtualization according to VM isolation, licensing, performance and hardware support requirements. This choice concerns virtual gpus and gpu sharing approaches and may be valid in another design. But this case reports 'A job reserves one GPU per node even though its communication favors local GPU peers', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.

Not the most direct the corrective action for these facts. Use a workload scheduler such as Slurm with explicit job GPU resource requests, queue policies and fair-share controls for shared training clusters. This choice concerns slurm batch queues fair-share and reservations and may be valid in another design. But this case reports 'A job reserves one GPU per node even though its communication favors local GPU peers', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.

Not the most direct the corrective action for these facts. Use nvidia-smi or supported NVIDIA management interfaces to inspect GPU identification, utilization, memory, temperatures and running processes as an initial node-level diagnostic. This choice concerns nvidia-smi point-in-time gpu operational checks and may be valid in another design. But this case reports 'A job reserves one GPU per node even though its communication favors local GPU peers', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.

Not the most direct the acceptance evidence for these facts. A test pod requests a supported accelerator resource, schedules to an eligible node and performs a successful device computation. This choice concerns Kubernetes gpu device scheduling and discovery and may be valid in another design. But this case reports 'A job reserves one GPU per node even though its communication favors local GPU peers', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.

Not the most direct the acceptance evidence for these facts. A point-in-time inspection identifies the correct GPU and active process, memory and temperature state corresponding to the reported issue. This choice concerns nvidia-smi point-in-time gpu operational checks and may be valid in another design. But this case reports 'A job reserves one GPU per node even though its communication favors local GPU peers', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.

Not the most direct the acceptance evidence for these facts. A sampled node's actual firmware, GPU, driver and fabric settings match the approved inventory or show a tracked exception. This choice concerns cluster inventory firmware and configuration baseline and may be valid in another design. But this case reports 'A job reserves one GPU per node even though its communication favors local GPU peers', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.

Not the most direct the corrective action for these facts. Maintain an inventory of GPU systems, drivers, firmware, network devices and approved configuration versions with controlled rollout and reconciliation. This choice concerns cluster inventory firmware and configuration baseline and may be valid in another design. But this case reports 'Partial resource allocation holds devices idle while the final worker waits indefinitely', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.

Not the most direct the corrective action for these facts. Correlate supported GPU error counters and Xid or ECC events with driver logs, diagnostics and affected workloads before deciding on remediation or hardware replacement. This choice concerns gpu reliability events and diagnostic triage and may be valid in another design. But this case reports 'Partial resource allocation holds devices idle while the final worker waits indefinitely', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.

Not the most direct the corrective action for these facts. Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices. This choice concerns Kubernetes gpu device scheduling and discovery and may be valid in another design. But this case reports 'Partial resource allocation holds devices idle while the final worker waits indefinitely', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.

Not the most direct the acceptance evidence for these facts. A time-series review connects kernel activity and memory use to actual completed training or inference work while checking limit events. This choice concerns gpu utilization memory and power telemetry interpretation and may be valid in another design. But this case reports 'Partial resource allocation holds devices idle while the final worker waits indefinitely', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.

Not the most direct the acceptance evidence for these facts. A test pod requests a supported accelerator resource, schedules to an eligible node and performs a successful device computation. This choice concerns Kubernetes gpu device scheduling and discovery and may be valid in another design. But this case reports 'Partial resource allocation holds devices idle while the final worker waits indefinitely', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.

Not the most direct the acceptance evidence for these facts. A point-in-time inspection identifies the correct GPU and active process, memory and temperature state corresponding to the reported issue. This choice concerns nvidia-smi point-in-time gpu operational checks and may be valid in another design. But this case reports 'Partial resource allocation holds devices idle while the final worker waits indefinitely', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.

Not the most direct the corrective action for these facts. Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices. This choice concerns Kubernetes gpu device scheduling and discovery and may be valid in another design. But this case reports 'A scheduler satisfies GPU count but ignores incompatible accelerator models across hosts', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.

Not the most direct the corrective action for these facts. Maintain an inventory of GPU systems, drivers, firmware, network devices and approved configuration versions with controlled rollout and reconciliation. This choice concerns cluster inventory firmware and configuration baseline and may be valid in another design. But this case reports 'A scheduler satisfies GPU count but ignores incompatible accelerator models across hosts', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.

Not the most direct the corrective action for these facts. Correlate supported GPU error counters and Xid or ECC events with driver logs, diagnostics and affected workloads before deciding on remediation or hardware replacement. This choice concerns gpu reliability events and diagnostic triage and may be valid in another design. But this case reports 'A scheduler satisfies GPU count but ignores incompatible accelerator models across hosts', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.

Not the most direct the acceptance evidence for these facts. A reproducible fault is linked to device identifiers, event history and a documented diagnostic action with a verified recovery result. This choice concerns gpu reliability events and diagnostic triage and may be valid in another design. But this case reports 'A scheduler satisfies GPU count but ignores incompatible accelerator models across hosts', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.

Not the most direct the acceptance evidence for these facts. Several representative users submit jobs and the scheduler allocates only authorized GPU resources under the published priority and quota policy. This choice concerns slurm batch queues fair-share and reservations and may be valid in another design. But this case reports 'A scheduler satisfies GPU count but ignores incompatible accelerator models across hosts', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.

Not the most direct the acceptance evidence for these facts. An authorized operator retrieves board-level health and remote console information over the protected management network while the host OS is unavailable. This choice concerns bmc and out-of-band server health administration and may be valid in another design. But this case reports 'A scheduler satisfies GPU count but ignores incompatible accelerator models across hosts', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.

Not the most direct the corrective action for these facts. Interpret GPU compute utilization, device-memory occupancy, power draw and temperature together over time instead of using one metric as proof of efficient execution. This choice concerns gpu utilization memory and power telemetry interpretation and may be valid in another design. But this case reports 'A tightly coupled training job cannot progress until all its ranks launch together', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.

Not the most direct the corrective action for these facts. Use a workload scheduler such as Slurm with explicit job GPU resource requests, queue policies and fair-share controls for shared training clusters. This choice concerns slurm batch queues fair-share and reservations and may be valid in another design. But this case reports 'A tightly coupled training job cannot progress until all its ranks launch together', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.

Not the most direct the corrective action for these facts. Use nvidia-smi or supported NVIDIA management interfaces to inspect GPU identification, utilization, memory, temperatures and running processes as an initial node-level diagnostic. This choice concerns nvidia-smi point-in-time gpu operational checks and may be valid in another design. But this case reports 'A tightly coupled training job cannot progress until all its ranks launch together', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.

Not the most direct the primary risk for these facts. Uncontrolled first-come submissions and missing resource limits can starve teams or schedule more accelerator work than exists. This choice concerns slurm batch queues fair-share and reservations and may be valid in another design. But this case reports 'A tightly coupled training job cannot progress until all its ranks launch together', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.

Not the most direct the primary risk for these facts. Without a partitioning plan, tenants can contend for full-GPU resources or receive partitions too small for their model memory needs. This choice concerns nvidia mig hardware partitioning and isolation and may be valid in another design. But this case reports 'A tightly coupled training job cannot progress until all its ranks launch together', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.

Not the most direct the primary risk for these facts. A healthy host does not prove its accelerator is recognized or free of memory pressure and unexpected competing processes. This choice concerns nvidia-smi point-in-time gpu operational checks and may be valid in another design. But this case reports 'A tightly coupled training job cannot progress until all its ranks launch together', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.

Not the most direct the corrective action for these facts. Correlate supported GPU error counters and Xid or ECC events with driver logs, diagnostics and affected workloads before deciding on remediation or hardware replacement. This choice concerns gpu reliability events and diagnostic triage and may be valid in another design. But this case reports 'Operators scale a cluster but job placement yields worse collective performance than before', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.

Not the most direct the corrective action for these facts. Use appropriate NVIDIA GPU drivers, device discovery or GPU Operator components and Kubernetes resource requests so eligible workloads receive supported accelerator devices. This choice concerns Kubernetes gpu device scheduling and discovery and may be valid in another design. But this case reports 'Operators scale a cluster but job placement yields worse collective performance than before', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.

Not the most direct the corrective action for these facts. Maintain an inventory of GPU systems, drivers, firmware, network devices and approved configuration versions with controlled rollout and reconciliation. This choice concerns cluster inventory firmware and configuration baseline and may be valid in another design. But this case reports 'Operators scale a cluster but job placement yields worse collective performance than before', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.

Not the most direct the action-and-evidence pair for these facts. Use a workload scheduler such as Slurm with explicit job GPU resource requests, queue policies and fair-share controls for shared training clusters. This choice concerns slurm batch queues fair-share and reservations and may be valid in another design. But this case reports 'Operators scale a cluster but job placement yields worse collective performance than before', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.

Not the most direct the action-and-evidence pair for these facts. Use a secured baseboard management controller and out-of-band path to inspect system sensors, power and remote console status independently of the host operating system. This choice concerns bmc and out-of-band server health administration and may be valid in another design. But this case reports 'Operators scale a cluster but job placement yields worse collective performance than before', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.

Not the most direct the action-and-evidence pair for these facts. A sampled node's actual firmware, GPU, driver and fabric settings match the approved inventory or show a tracked exception. This choice concerns cluster inventory firmware and configuration baseline and may be valid in another design. But this case reports 'Operators scale a cluster but job placement yields worse collective performance than before', which calls for reasoning about distributed job placement and topology-aware orchestration. The targeted response is: Place tightly coupled distributed training processes with awareness of accelerator counts, network topology and collective communication demands, and validate gang-scheduling needs.