gpu-server-setup

An agent skill that prepares a Linux server with NVIDIA or Intel Arc GPUs for neural-network and LLM workloads — from a bare Debian/Ubuntu install to a verified Docker + GPU inference stack. It is a portable skill: install it in Claude Code, Cursor, or OpenAI Codex, or drop the folder into any agent that reads SKILL.md.

Based on two field-tested sources by the author: the Dzen article “Как подготовить Linux к запуску и обучению нейросетей? (+ Docker)” and a production setup runbook for multi-GPU inference servers. The Intel Arc part comes from bringing up a dual Arc A770 server on Debian 13 in 2026.

What it does

• NVIDIA driver + CUDA — from the official CUDA apt repo (cuda-keyring), with the open-vs-proprietary kernel-module choice, driver-only vs toolkit decision, multi-CUDA via update-alternatives, and version pinning.
• Mandatory kernel headers — exact running-kernel headers plus the tracking metapackage (linux-headers-amd64 on standard Debian; match the kernel flavour elsewhere), with headers and NVIDIA modules verified for installed kernels before a reboot. A kernel upgrade must not silently disable GPUs on the next boot.
• Intel Arc (Alchemist and Battlemage) — the in-tree i915/xe driver with distro firmware, Resizable BAR and real PCIe link checks, the trixie-backports kernel when the host must see GPU temperatures, Docker access through /dev/dri render nodes with no toolkit, and a field-tested map of what serves on Arc today: llama.cpp SYCL/Vulkan, Ollama, OpenVINO Model Server, vLLM on Battlemage and a no-build vLLM recipe for the A770, plus measured i915 against xe on a pair of A770: what the force-probed xe driver gives (up to 5x for vLLM, 2x for llama.cpp) and what it costs (a card that can wedge until a reboot, runtime PM to switch off).
• Docker + NVIDIA Container Toolkit — official Docker Engine install, nvidia-ctk runtime configure, and explicit GPU passthrough (--gpus, compose deploy blocks, per-service device_ids pinning).
• Staged verification — hard gates between layers (lspci → nvidia-smi or i915/xe + render nodes + GuC → GPU-in-Docker → service health); the skill never proceeds past a failing gate. scripts/check.sh detects the vendor and runs the gates automatically, then fails any running vLLM, Infinity or OpenWebUI container that still reports to its vendor.
• Inference presets (optional) — the mandatory deliverable is the verified Docker + GPU base; serving stacks go on top only when asked for. Ready docker-compose files for vLLM (per-GPU model services), Infinity (embeddings), OpenWebUI (web UI) and Ollama (llama.cpp runs the same way via its official image), with the flags that matter explained and vendor telemetry switched off in every one of them.
• GPU power limit — on request: query the card’s allowed range, cap with