In this blog, you'll learn best practices for running a self-hosted AI server with FileMaker 2025, including: Hardware and GPU recommendations. Key pitfalls to avoid when deploying production-ready large language models (LLMs). Covers hardware requirements, model selection, Open WebUI setup, and VS Code integration. Let me be direct about something: I'm not neutral on this topic. I use AI tools for coding, debugging, writing, and research. Last updated: March 2026 Running AI models on your own hardware keeps your data private, eliminates recurring API costs, and. Running AI models on a local AI server is one of the most empowering steps you can take in your AI journey. Whether you're integrating text generation, text embedding, or image embedding models, self-hosting gives you full control over performance, cost, and data security—no cloud dependency required. Your All-in-One Complete AI Stack - Run powerful language models, autonomous agents, and document intelligence locally on your hardware. No cloud, no limits, no compromise. GPU: NVIDIA RTX PRO Blackwell (96 GB VRAM, 5th-gen Tensor Cores) for training/inference; rack-ready for 2U–4U servers. CPU/RAM/Storage: High single-thread CPU, 128–512 GB RAM; NVMe SSDs for.