About Persistent AI
We believe artificial intelligence belongs on your hardware, running under your control, available whenever you need it.
Our Mission
Persistent AI was founded on a simple conviction: the most valuable AI systems are the ones you own. Cloud APIs are convenient, but they introduce latency, privacy risks, recurring costs, and dependency on vendors who can change terms or deprecate models overnight. We document, test, and advocate for self-hosted AI infrastructure that delivers continuous intelligence without compromise.
Building an always-on AI server is not merely a technical exercise — it represents a philosophical commitment to data sovereignty. Every engineer who migrates inference workloads from the cloud to local hardware strengthens the ecosystem of open, accessible AI. Our guides cover everything from selecting dedicated AI hardware to configuring long-running private AI pipelines that serve predictions around the clock.
What We Cover
The landscape of self-hosted AI is broad and rapidly evolving. We focus on the practical intersection of hardware, software, and operational discipline required to keep inference servers running 24/7. Our content spans edge devices like the persistent Jetson AI platform, mid-range GPU workstations, and multi-node rack deployments.
Software coverage includes model serving frameworks (vLLM, llama.cpp, Ollama), container orchestration for AI workloads, vector database integration, embedding pipeline automation, and monitoring stacks purpose-built for inference nodes. We place special emphasis on reliable always-on computing practices — because an AI server that crashes at 3 AM isn't serving anyone.
Who We Serve
Our audience includes software engineers building internal AI tools, researchers who need reproducible local environments, privacy-conscious organizations that cannot send data to external APIs, and hobbyists exploring the frontier of edge inference. Whether you're configuring a single Jetson Orin for ClawBox 24/7 operation or managing a cluster of inference servers, our guides meet you at your level.
We draw heavily from the community expertise shared by the STH always-on lab community, whose decades of experience running production servers inform our approach to hardware selection, cooling, and reliability engineering. Similarly, the Linux uptime guide principles embedded in the kernel itself serve as the operating system foundation for everything we build.
Our Approach
We prioritize reproducibility above all. Every configuration we publish has been tested on real hardware in continuous operation — not just fired up for a benchmark screenshot and torn down. We document failure modes, recovery procedures, and the unglamorous operational details that make the difference between a weekend project and a production system.
The compute landscape keeps shifting — new accelerators, new quantization methods, new model architectures — but the principles of persistent computing remain constant. Redundancy, monitoring, graceful degradation, and automated recovery never go out of style. That's what we teach, and that's what we practice with every always-on AI server we deploy.