VMware Private AI (Foundation with NVIDIA) — GPU & Model-Serving Ops

The GPU / AI-infrastructure lens of the VMware skill family — GPU host and device inventory, vGPU consumers, live GPU utilization, the vGPU / DirectPath profile catalog, vGPU assignment, and the models and knowledge bases served by Private AI Service (PAIS) — over the vSphere 9.x / VCF 9.1 Web Services API (pyVmomi) and the PAIS REST API. MCP server + CLI.

Disclaimer: This is a community-maintained open-source project and is not affiliated with, endorsed by, or sponsored by VMware, Inc., Broadcom Inc., or NVIDIA Corporation. "VMware", "vSphere", and "VCF" are trademarks of Broadcom; "NVIDIA" and "vGPU" are trademarks of NVIDIA. Source is auditable at github.com/vmware-skills/VMware-PrivateAI under the MIT license.

Status: beta. Skill #15 of the family, on its own version line. Every API path is verified against official Broadcom/NVIDIA docs before use (see tests/eval/spec/privateai_endpoints.py) — no endpoints written from memory. GET response field names and the exact PAIS paths are defensive and await validation on real 9.x hardware (see the beta caveats in skills/vmware-privateai/SKILL.md). Governed by the family harness (audit + policy + teaching errors); read/write authorization is left to the RBAC role of the vCenter service account.

Upgrading to 2.0

2.0 renamed every MCP tool it had so every name starts with a verb (e.g. directpath_profile_list → list_directpath_profiles, gpu_consumer_list → list_gpu_consumers, gpu_device_list → list_gpu_devices). The full old → new table is in RELEASE_NOTES.md. Nothing else changes: same tools, parameters, safety gates and CLI commands. If an agent prompt, script or ~/.vmware/rules.yaml names a tool, update it — though deny rules written with the old names keep working with vmware-policy ≥ 1.18 (vmware-audit policy lists any rule the renames widened), and vmware-pilot ≥ 1.13 translates old names in saved workflows. If an install older than 2.0 shares that rules.yaml, give it vmware-policy ≥ 1.18.2 too: it matches each rule against both the old and the new name, so one file serves both.

Capabilities (18 MCP tools — 17 read / 1 write)

Reads are strictly non-destructive. The one write (assign_vgpu) previews its blast radius, refuses a powered-on VM, never powers a VM off itself, double-confirms on the CLI, and is audited to ~/.vmware/audit.db; pre-check with validate_vgpu_profile first. assign_vgpu takes confirm (default false). Without it the call returns blast_radius — the VM's name and id, current and target profile, whether its vGPU device is edited or a new one added, power state, blockers and unmeasured — and changes nothing. confirm=true is refused for a powered-on or suspended VM, a name shared by several VMs, and a VM whose power state or devices cannot be read. get_pais_model_catalog / list_pais_data_sources use unconfirmed PAIS control-plane paths (pitfall #36: a 404 returns a base-URL teaching hint and is not a bug); advise_pais_sizing / verify_pais_bundle need no connection (pure computation / local file parsing).

Quick Start

uv tool install vmware-privateai
vmware-privateai version
vmware-privateai gpu host-list        # first read command — lists the hosts that have GPUs

Configuration lives in ~/.vmware-privateai/config.yaml (targets + an optional pais: section); passwords and the PAIS bearer token live in ~/.vmware-privateai/.env (chmod 600). See skills/vmware-privateai/references/setup-guide.md.

Common Workflows

1. Find an idle GPU and change a VM's vGPU profile

vmware-privateai gpu device-list --vendor NVIDIA   # vm_count 0 = idle
vmware-privateai gpu consumer-list                 # who holds a vGPU, with which profile
vmware-privateai vgpu profile-list --host esx-07   # profiles this host can assign
vmware-privateai gpu vgpu-assign fin-train-01 grid_a100-4c --dry-run   # preview
# power the VM off with vmware-aiops first, then:
vmware-privateai gpu vgpu-assign fin-train-01 grid_a100-4c            # double-confirm + audit

Failure branch: on "VM is powered on", power it off first with vmware-aiops power_off_vm (CLI: vmware-aiops vm power-off fin-train-01); if the host does not offer the profile or memory is short, check the valid profiles with vmware-privateai gpu host-get <that host>.

2. Fleet-wide GPU utilization sweep

vmware-privateai gpu utilization --top 10

Failure branch: metrics unavailable is not an error — the NVIDIA host GPU driver does not expose that VM's counters. Deeper per-SM / MIG-slice telemetry is not provided by vSphere (it needs NVIDIA DCGM).

3. See what PAIS is serving

vmware-privateai pais model-list
vmware-privateai pais kb-list

Failure branch: HTTP 404 is usually a base-URL mismatch (the /api/v1 prefix varies by deployment and is unconfirmed in beta) — check pais.endpoint; HTTP 401/403 means VMWARE_PRIVATEAI_PAIS_TOKEN expired or lacks permission — fetch a new token.

Companion Skills

• vmware-aiops — the vCenter VMs behind AI workloads (power/snapshot/clone/migrate)
• vmware-vks — GPU-enabled Tanzu Kubernetes
• vmware-monitor — read-only vSphere health

This skill is the GPU lens over all three.

Security

See SECURITY.md at the repo root. Highlights: credentials only in .env (chmod 600, b64: obfuscation); TLS verification on by default; all vSphere/PAIS text passes vmware_policy.sanitize() against prompt injection; read/write authorization is left to vCenter RBAC; assign_vgpu is power-off gated + double-confirmed + audited.

Remote access (HTTPS) and limiting tools

By default vmware-privateai mcp speaks stdio to one local client and offers every tool. Two options change that (vmware-policy ≥ 1.21.0).

Serve over HTTPS so clients on other machines can connect. Every caller needs a bearer token:

vmware-audit token create alice          # prints the token once; give it to alice
vmware-audit token list                  # names only
vmware-audit token revoke alice          # refused from its next request, no restart
vmware-privateai mcp --transport https --host 0.0.0.0 --port 8443 \
    --cert /etc/vmware-mcp/server.pem --key /etc/vmware-mcp/server-key.pem \
    --public-name mcp.example.com

Connect a client (Claude Code shown; any MCP client that supports streamable HTTP and custom headers works):

claude mcp add --transport http vmware-privateai https://mcp.example.com:8443/mcp \
    --header "Authorization: Bearer $VMWARE_MCP_TOKEN"

{
  "mcpServers": {
    "vmware-privateai": {
      "type": "http",
      "url": "https://mcp.example.com:8443/mcp",
      "headers": { "Authorization": "Bearer ${VMWARE_MCP_TOKEN}" }
    }
  }
}

• The server will not start without a token, refuses Host/Origin values outside --public-name, and checks before it binds that unauthenticated requests get 401. Use a certificate your clients trust for those names.
• Behind a TLS-terminating reverse proxy on the same machine use --transport http --host 127.0.0.1 --public-name mcp.example.com and pass the Authorization header through.
• The audit log records each caller as token:<name>. Calls run side by side, also on the same target: the server keeps up to VMWARE_MCP_POOL_SIZE (default 2) sessions per target, each one a login on that target, and a call waits at most 300 s for a free session (vmware-policy ≥ 1.22.0).
• Everyone who can reach the endpoint acts with this server's account. Tokens identify callers; they grant nothing on the platform. Run one endpoint per privilege level. vmware-privateai access-info shows which account that is and what it may do.

Offer only read tools, plus the writes you choose:

vmware-privateai mcp --expose read --allow-write assign_vgpu
# or: VMWARE_PRIVATEAI_MCP_EXPOSE=read VMWARE_PRIVATEAI_MCP_ALLOW_WRITE=assign_vgpu vmware-privateai mcp

Tools not offered do not appear in the client's tool list. A bad value or an unknown write tool stops the server (exit 2). This limits what clients are offered; it is not authorization, so the account itself should be least-privilege.

Details: skills/vmware-privateai/references/setup-guide.md ("Serving MCP over HTTPS", "Limiting which tools are offered").

License

MIT