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VMware-PrivateAI

VMware-PrivateAI

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VMware-PrivateAI

Inspect GPU readiness and VMware Private AI resources before deployment.

English · 简体中文

Community-maintained · MIT · Not an official VMware/Broadcom/NVIDIA product.

24-second illustrative investigation demo

Synthetic-data GIF; video includes redacted historical screenshots. Download the AI-narrated family introduction (~3 min)

Why use it?

  • Inspect GPU/vGPU inventory, profiles and utilization.

  • Query supported PAIS catalogs and model resources.

  • Use local sizing and air-gap bundle inspection.

Related MCP server: mcp-proxmox

Get started

Install the published CLI and inspect its commands:

uv tool install vmware-privateai==2.5.3
vmware-privateai --help

Requires uv and Python >=3.11. Configure targets and credentials locally before remote queries. Setup guide.

For agent-host installation, use the setup guide and Cursor, Codex, Gemini CLI, Cline and local-model examples.

Choose a model with the shared model-fit workflow: compare the same tasks, review quality and safety, then select the smallest tested passing configuration per task group; this is not a production guarantee.

Know the boundaries

Includes GPU assignment and related sensitive VM operations. Unknown API shapes stay unknown; PAIS live-validation limits and required Python 3.11+ are documented.

Go deeper

Part of VMware Skills: 15 skills, 14 MCP servers and one shared Policy library. If it helps your work, star the family portal.

Issues and feedback · MIT

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

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    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    Maintenance

    ActivityActive
    ResponsivenessNo recent community issues

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