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linux-ai-serverLinuxAI 服务器

Agent Skill

linux-ai-server 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

4,455

周安装

182

GitHub Stars

公开资料未说明

下载量

1,427
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:linux-ai-server(LinuxAI 服务器)
来源仓库:https://github.com/twinsgeeks/linux-ai-server
安装命令:
openclaw skills install linux-ai-server
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install linux-ai-server

简介

将Linux服务器转换为本地AI推理集群运行环境。

  • 支持NVIDIA CUDA加速与无头系统部署,适合多机扩展。
  • 提供systemd管理服务与零GUI开销设计。
  • 需具备Linux基础环境及GPU硬件支持。linux-ai-server 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 注意驱动版本兼容性与资源分配策略,防止性能瓶颈。

SKILL.md

name
linux-ai-server
description
Linux AI Server — turn Linux servers into a local AI inference cluster. Headless Linux AI with systemd, NVIDIA CUDA, and zero GUI overhead. Linux AI server for Llama, Qwen, DeepSeek, Phi, Mistral. Run a Linux AI server cluster on Ubuntu, Debian, RHEL, Fedora. Linux AI服务器本地推理。Servidor Linux IA para inferencia local.
version
1.0.0
homepage
https://github.com/geeks-accelerator/ollama-herd
metadata
{"openclaw":{"emoji":"server","requires":{"anyBins":["curl","wget"],"optionalBins":["python3","pip","nvidia-smi","systemctl"]},"configPaths":["~/.fleet-manager/latency.db","~/.fleet-manager/logs/herd.jsonl"],"os":["linux"]}}

Linux AI Server — Headless AI Inference Cluster

Turn your Linux servers into a distributed AI inference cluster. No GUI, no Docker, no Kubernetes — just Linux + pip install. Your rack-mounted servers, cloud VMs, and spare Linux boxes all serve AI through one endpoint.

Why Linux AI server

  • Zero GUI overhead — headless Linux AI uses all resources for inference, not desktops
  • systemd native — Linux AI server starts on boot, restarts on failure, logs to journald
  • SSH management — manage your Linux AI server cluster entirely over SSH
  • Any Linux distro — Ubuntu, Debian, RHEL, Fedora, Arch, Alpine — if it runs Ollama, it joins the fleet
  • NVIDIA CUDA — Linux AI server uses NVIDIA GPUs natively. No compatibility issues.
  • Fleet routing — multiple Linux AI servers share the load. 7-signal scoring picks the best one.

Linux AI server setup

Quick install on each Linux server

# Install Ollama on Linux
curl -fsSL https://ollama.ai/install.sh | sh

# Install the Linux AI router
pip install ollama-herd

Linux AI server router (pick one server)

herd          # start Linux AI server router on port 11435
herd-node     # register this Linux AI server

Linux AI server nodes (all other servers)

herd-node     # auto-discovers the Linux AI server router
# Or explicit: herd-node --router-url http://router-ip:11435

Linux AI server systemd services

# /etc/systemd/system/herd-router.service
[Unit]
Description=Linux AI Server Router
After=network.target ollama.service

[Service]
Type=simple
ExecStart=/usr/local/bin/herd
Restart=always
RestartSec=5
User=ollama

[Install]
WantedBy=multi-user.target
# /etc/systemd/system/herd-node.service
[Unit]
Description=Linux AI Server Node
After=network.target ollama.service

[Service]
Type=simple
ExecStart=/usr/local/bin/herd-node
Restart=always
RestartSec=5
User=ollama

[Install]
WantedBy=multi-user.target
sudo systemctl enable --now herd-router    # on the Linux AI router
sudo systemctl enable --now herd-node      # on all Linux AI nodes

Linux AI server hardware guide

Linux AI ServerGPURAMBest Linux AI models
Rack server (NVIDIA A100)80GB256GBdeepseek-v3, qwen3.5:72b — frontier
Rack server (NVIDIA L40S)48GB128GBllama3.3:70b, qwen3.5:32b
Desktop server (RTX 4090)24GB64GBllama3.3:70b (Q4), deepseek-r1:32b
Mini PC / NUC (no GPU)CPU32GBphi4, gemma3:12b — CPU inference
Cloud VM (no GPU)CPU16GBphi4-mini, gemma3:4b
Raspberry Pi 5CPU8GBgemma3:1b, phi4-mini — edge AI
Linux AI server works with NVIDIA CUDA GPUs, AMD ROCm (experimental), and CPU-only inference.

Use your Linux AI server

OpenAI SDK

from openai import OpenAI

# Your Linux AI server endpoint
client = OpenAI(base_url="http://linux-ai-server:11435/v1", api_key="not-needed")

response = client.chat.completions.create(
    model="llama3.3:70b",
    messages=[{"role": "user", "content": "Write a Terraform module for AWS ECS"}],
    stream=True,
)
for chunk in response:
    print(chunk.choices[0].delta.content or "", end="")

curl from any machine

# Hit your Linux AI server from anywhere on the network
curl http://linux-ai-server:11435/api/chat -d '{
  "model": "codestral",
  "messages": [{"role": "user", "content": "Write a Dockerfile for a FastAPI app"}],
  "stream": false
}'

Linux AI server environment

# Optimize Linux AI server Ollama
sudo systemctl edit ollama
# Add under [Service]:
#   Environment="OLLAMA_KEEP_ALIVE=-1"
#   Environment="OLLAMA_MAX_LOADED_MODELS=-1"
#   Environment="OLLAMA_NUM_PARALLEL=2"
sudo systemctl restart ollama

Linux AI server firewall

# UFW (Ubuntu/Debian)
sudo ufw allow 11435/tcp

# firewalld (RHEL/Fedora)
sudo firewall-cmd --add-port=11435/tcp --permanent && sudo firewall-cmd --reload

Linux AI server monitoring

# Linux AI server fleet status
curl -s http://localhost:11435/fleet/status | python3 -m json.tool

# Linux AI server health — 15 automated checks
curl -s http://localhost:11435/dashboard/api/health | python3 -m json.tool

# Linux AI server traces — recent requests
curl -s "http://localhost:11435/dashboard/api/traces?limit=10" | python3 -m json.tool

# Linux AI server logs
journalctl -u herd-router -f
tail -f ~/.fleet-manager/logs/herd.jsonl.$(date +%Y-%m-%d)

Dashboard at http://linux-ai-server:11435/dashboard — access from any browser on the network.

Also available on Linux AI server

Image generation

curl http://localhost:11435/api/generate-image \
  -d '{"model": "z-image-turbo", "prompt": "server rack visualization", "width": 1024, "height": 1024}'

Embeddings

curl http://localhost:11435/api/embed \
  -d '{"model": "nomic-embed-text", "input": "Linux AI server headless inference"}'

Full documentation

Contribute

Ollama Herd is open source (MIT). Linux server admins welcome:

Guardrails

  • Linux AI server model downloads require explicit user confirmation.
  • Linux AI server model deletion requires explicit user confirmation.
  • Never delete or modify files in ~/.fleet-manager/.
  • No models are downloaded automatically — all pulls are user-initiated or require opt-in.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

98.99%
按下载量换算1,413

安全审计

VirusTotal

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ClawScan

通过

Static analysis

通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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