Individual skill
gpu-server-management
Set up and manage NVIDIA GPU servers for AI workloads — driver installation, CUDA toolkit, container toolkit, MIG partitioning, GPU health monitoring, and multi-GPU configuration for LLM inference and training.
infrastructure/servers/gpu-server-management/SKILL.md
Source commit 0365f57a079b1332f95cf26e31dd2d5332a8399f
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- Yes
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Network or broad shell
- Last checked
- Nov 3, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 1,197
- Repository last updated
- May 22, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- agent-skills Repository topic
- agentic-ai Repository topic
- ai-agents Repository topic
- cheatsheets Repository topic
- devops Repository topic
- infrastructure-as-code Repository topic
- kubernetes Repository topic
- security Repository topic
- sre Repository topic
- terraform Repository topic
- engineering Derived category
- devops Derived category
- devops-containers Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
Read excerpt
# GPU Server Management
Provision, configure, and monitor NVIDIA GPU servers for AI inference and training workloads.
## When to Use This Skill
Use this skill when:
- Setting up a new GPU server for LLM inference or model training
- Installing or upgrading NVIDIA drivers and CUDA toolkit
- Configuring Docker with NVIDIA Container Toolkit for GPU workloads
- Partitioning A100/H100 GPUs with MIG for multi-tenant workloads
- Troubleshooting GPU errors, driver issues, or thermal throttling
## Prerequisites