SEC.01

The playbook

The run starts read-only: the recon probes decide what happens next. The steps that follow are adapted to the distribution and state found — a real machine is rarely clean.

1. 01recondistro, version, kernel and architecture (x86_64, or aarch64 → sbsa in NVIDIA URLs)
2. 02reconis an NVIDIA GPU present? none → stop, nothing to install
3. 03recondriver already installed and working? which version / max CUDA?
4. 04reconnouveau or nvidia module loaded?
5. 05reconSecure Boot enabled? (an unsigned module will not load)
6. 06reconheaders for the running kernel installed (needed to build the module)?
7. 07reconDocker present? (then the Container Toolkit is useful)
8. 08actInstall the kernel headersverify
9. 09actAdd the official NVIDIA CUDA repositoryverify
10. 10actInstall the NVIDIA driverdestructiveverify
11. 11actInstall the CUDA toolkitverify
12. 12actInstall the NVIDIA Container Toolkit (only if Docker is present)verify
13. 13actVerify the GPUverify

SEC.02

What it verifies

Each step ships its own check. The run only moves on when the check passes, so what the recipe reports as done is what is actually on the server.

1. 01verifyInstall the kernel headersVerified bytest -d "/lib/modules/$(uname -r)/build" && echo ok
2. 02verifyAdd the official NVIDIA CUDA repositoryVerified byapt-cache policy cuda-toolkit 2>/dev/null | head -3; dnf -q list cuda-toolkit 2>/dev/null | tail -1; true
3. 03verifyInstall the NVIDIA driverVerified bynvidia-smi -L 2>&1 || modinfo -F version nvidia
4. 04verifyInstall the CUDA toolkitVerified by/usr/local/cuda/bin/nvcc --version | tail -2
5. 05verifyInstall the NVIDIA Container Toolkit (only if Docker is present)Verified bydocker info --format '{{json .Runtimes}}' | grep -c nvidia
6. 06verifyVerify the GPUVerified bynvidia-smi -L | grep -c GPU

SEC.03

Guardrails

Non-negotiable instructions the run must respect, whatever it finds on the server.

SEC.04

How a recipe runs in Servor

Servor is zero-knowledge: the platform cannot execute anything on its own. When you launch this recipe, a run reads the reconnaissance, plans the steps and asks for your approval. Each approved command is signed by your browser — with a key the server never sees — then relayed verbatim to the agent, which verifies the signature locally before executing.

SEC.05

Deploy on Servor button

Maintain NVIDIA drivers + CUDA or a project that needs it? Put this button in your README or on your site: one click and your users install it on their own server.

SEC.06

More ai / ml recipes

1. 01OllamaOllama to run local LLMs (NVIDIA GPU if present, CPU otherwise), API on 127.0.0.1, optional model pull.
2. 02JupyterLabJupyterLab via Docker, on 127.0.0.1 with a generated access token (access through an SSH tunnel).
3. 03Open WebUIOpen WebUI, a web interface for LLMs (Ollama), via Docker on 127.0.0.1, wired to the local Ollama.
4. 04ComfyUI (Stable Diffusion)ComfyUI for image generation, installed from the official repository as a systemd service (NVIDIA GPU recommended, CPU possible).
5. 05vLLM inference servervLLM to serve an LLM through an OpenAI-compatible API on 127.0.0.1, protected by a generated key. NVIDIA GPU required.