We deployed Ollama and Open WebUI on three real units — a QNAP TS-464 (Celeron), a QNAP TVS-h674 (Core i5), and a TrueNAS SCALE box with a Ryzen 5 and an RTX 3060 12 GB — and have honest numbers and step-by-step recipes for each. To simplify it, Large Language Model is a type of AI (artificial intelligence) that is trained on a lot of text data, and is designed to generate human-like responses back to the user. The most significant would be ChatGPT from OpenAI. But, interacting with those models requires the information to. In this guide, we'll walk you through setting up DeepSeek AI on a Synology NAS using Docker. Why Run DeepSeek AI on a NAS? Hosting AI locally on a NAS (Network Attached Storage) has several advantages: Data Security – Keep all your queries and interactions private. Offline Access – No need for an. AI agents are automated systems that perform tasks using AI workflows. Who benefits most from this setup? Founders, creators, and businesses that want control over data and automation. Is this hard to build? It requires some setup, but tools today make it much easier than before. Since everything's web-based, I can even access it from my iPad or iPhone—perfect for quick model checks or kicking off longer-running tasks when I'm away from my desk. Here's what I put together: I started with. Running AI models on a local AI server is one of the most empowering steps you can take in your AI journey. Instead of depending on cloud APIs, you can bring the intelligence directly onto your own hardware, which unlocks: Improved privacy and security: With locally hosted AI, your data never. Voice Assistant for Home Assistant locally (so Text-to-Speech model like Piper, a Speech-to-Text model like Whisper and a LLM for the conversation pipeline).
