Transform your Linux hardware into a high-performance, headless AI inference cluster using native systemd and NVIDIA CUDA.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install linux-ai-server
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install linux-ai-server using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Linux AI Server?
The Linux AI Server skill enables developers and sysadmins to convert standard Linux machines into a powerful, distributed inference fleet. By leveraging Openclaw Skills, users can bypass the overhead of heavy container orchestration like Kubernetes or Docker, instead utilizing native systemd services to manage LLMs like Llama 3.3, DeepSeek-V3, and Mistral. This architecture ensures maximum resource allocation for inference, providing a streamlined way to scale AI capabilities across rack-mounted servers, cloud VMs, and edge devices.
This specific implementation focuses on low-latency performance and high availability. Openclaw Skills allow for a unified endpoint that intelligently routes traffic across your entire fleet. Whether you are running high-end NVIDIA A100 GPUs or simple CPU-based Mini PCs, this skill provides a standardized way to consume AI models via an OpenAI-compatible API without the need for a GUI.
Linux AI Server Use Cases
- Managing a headless AI inference cluster across multiple rack-mounted data center servers.
- Deploying local AI endpoints on cloud VMs to minimize GUI overhead and maximize token throughput.
- Creating a distributed AI fleet using spare local hardware or edge devices like Raspberry Pi 5.
- Scaling inference for large frontier models like DeepSeek-R1 using multi-GPU Linux environments.
- Providing a unified, self-hosted OpenAI-compatible API for internal development teams and automated workflows.
How Linux AI Server Works
- Install the Ollama inference engine on all participating Linux machines to provide the base model execution environment.
- Install the Openclaw Skills management layer via pip to enable the router and node communication protocols.
- Designate a primary Linux server as the herd router to act as the central gateway and load balancer for the fleet.
- Initialize herd-node services on all other Linux hardware to allow them to automatically discover and register with the router.
- Configure systemd units for both the router and nodes to ensure the AI cluster starts automatically on boot and recovers from failures.
- Access the unified API endpoint through the router, which monitors node health and distributes inference requests based on model availability and performance scoring.
Linux AI Server Setup
Quick Installation
# Install Ollama on your Linux environment
curl -fsSL https://ollama.ai/install.sh | sh
# Install the Openclaw Skills router and node components
pip install ollama-herd
Running the Fleet
# On the primary server (Router)
herd
# On worker servers (Nodes)
herd-node --router-url http://<router-ip>:11435
Persistence with systemd
# Enable the router service
sudo systemctl enable --now herd-router
# Enable the node service on all workers
sudo systemctl enable --now herd-node
Linux AI Server Data Schema & Taxonomy
The skill manages its state and performance data through specific local file structures and structured logging:
| Component | Path | Description |
|---|---|---|
| Latency DB | ~/.fleet-manager/latency.db |
SQLite database tracking node response times and performance metrics. |
| JSON Logs | ~/.fleet-manager/logs/herd.jsonl |
Structured log files for auditing requests, traces, and cluster events. |
| Systemd Units | /etc/systemd/system/ |
Configuration files for managing persistent background processes. |
| Health Checks | /dashboard/api/health |
API endpoint returning a JSON object containing 15 automated system health checks. |
Linux AI Server Advanced Features
- Intelligent fleet routing using 7-signal scoring to prioritize the fastest available nodes.
- Native NVIDIA CUDA support for high-performance GPU acceleration without container overhead.
- Real-time observability through a web-based dashboard for monitoring traces and request logs.
- Support for advanced Ollama environment optimizations like OLLAMA_KEEP_ALIVE and OLLAMA_NUM_PARALLEL.
- Versatile inference capabilities including text generation, image generation, and text embeddings via Openclaw Skills.
- Automated model discovery and node synchronization across complex network topologies.
SKILL.md
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