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Nvidia ai infrastructure operations

Skill Raishin/vanguard-frontier-agentic/skills/nvidia/nvidia-ai-infrastructure-operations

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npx -y skills add Raishin/vanguard-frontier-agentic --skill nvidia-ai-infrastructure-operations

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  • 18 stars18 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

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Use this skill when reviewing NVIDIA AI infrastructure deployments — DGX, HGX, MGX systems, GPU server install posture, BMC and out-of-band exposure, BIOS/firmware levels, vGPU host configuration, and rack-scale power/cooling/networking readiness. Trigger when the user asks whether a GPU host is provisioned per NVIDIA reference architecture, whether the BMC is segmented, whether driver/firmware versions match the AI Enterprise support matrix, or whether the deployment is in scope for NCA-AIIO or NCP-AII certification expectations.

SKILL.md

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NVIDIA AI Infrastructure Operations Review

Purpose

Review NVIDIA GPU infrastructure deployments (DGX, HGX, MGX, certified OEM systems) against NVIDIA reference architectures and the NCA-AIIO / NCP-AII certification body of knowledge. Anchor judgments on driver + firmware + CUDA toolkit + AI Enterprise support matrix alignment, BMC/iDRAC/iLO segmentation, and host-level GPU configuration (persistence mode, ECC, MIG capability, vGPU).

Lean operating rules

  • Prefer live evidence (nvidia-smi, nvidia-smi -q, dmidecode, ipmitool lan print, dcgmi diag) when the active client exposes it; otherwise fall back to NVIDIA Enterprise Support documentation, sanitized topology diagrams, and the AI Enterprise compatibility matrix.
  • Separate confirmed facts from inference. If BMC network segmentation, firmware level, or driver-toolkit match was not directly queried, say so.
  • Treat a BMC / iDRAC / iLO interface reachable from a tenant or workload network as a critical finding. GPU hosts hold model weights and tenant data; OOB compromise is total compromise.
  • Treat driver / CUDA / cuDNN versions outside the published NVIDIA AI Enterprise support matrix as a high finding — silent ABI breakage and unsupported workloads.
  • Treat ECC disabled on production GPUs as a high finding for training workloads (silent corruption of weights or gradients).
  • Treat persistence mode disabled on long-running inference hosts as a medium finding (driver re-init latency at first call).
  • Treat MIG-capable GPUs running in default whole-GPU mode in a multi-tenant cluster as a medium finding — partitioning is the isolation primitive.
  • Treat absent or unverified firmware bundle (HGX baseboard, NVSwitch, BMC) as a high finding for any deployment with regulated or high-value workloads.

References

Load these only when needed:

  • NVIDIA AI Enterprise support matrix
  • DGX/HGX system user guides for the deployed generation
  • NCA-AIIO and NCP-AII exam blueprints

Response minimum

Return, at minimum:

  • the scoped target (host class, generation, AI Enterprise version) and evidence level,
  • driver / CUDA / cuDNN / firmware posture vs the support matrix,
  • BMC / OOB segmentation posture,
  • ECC / persistence / MIG posture per GPU,
  • the safest next actions and any assumptions or blockers.

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