Year: 2025

Speech & Multimodal Implementation is how you give your local stack ears, a voice, and, if you want, eyes, without shipping anything to the cloud. On the speech side, you’ve…

Quantization & acceleration is how you squeeze big models onto normal hardware and make them feel fast. Quantization shrinks weights from fp16/bf16 down to 8-bit or 4-bit (sometimes even lower),…

This is the glue between your apps and a messy, ever-shifting model landscape. You point everything at one URL that speaks the OpenAI API, and the gateway translates those requests…

This guide is a practical, self-hosted “private AI stack” you can run locally or on your own servers. It includes an OpenAI-compatible proxy, a visual builder for agent and RAG…

Inference backends and servers are the engines that actually run models; on your box, your rack, or your cluster, and expose clean HTTP APIs so everything else (chat UIs, SDKs,…

If you want to run AI on your own hardware: quietly, quickly, and without paying the cloud tax, this post may be your field guide. I pulled together the local…