How to deploy AI locally on a server

The Complete Developer''s Guide to Running LLMs Locally

A comprehensive guide covering the local LLM stack from hardware requirements to production deployment. Compare Ollama, LM Studio, llama.cpp and build your first local AI application.

Host and deploy ASP Core | Microsoft Learn

In general, to deploy an ASP Core app to a hosting environment: Deploy the published app to a folder on the hosting server. Set up a process manager that starts the app when

Running AI Foundry Local on Windows Server 2025 – A Fully Offline

This post walks you through how to install and run Azure AI Foundry Local on Windows Server 2025 either on physical hardware or in a Hyper-V VM and how to deploy local AI models

Deploying a Static Site | Vite

NOTE These guides provide instructions for performing a static deployment of your Vite site. Vite also supports Server-Side Rendering. SSR refers to front-end

Self-Hosted AI Models: A Practical Guide to Running LLMs Locally

Learn how to self-host AI models for better data control and lower costs. Covers hardware requirements, open-source LLMs, tools like Ollama and vLLM, and real cost breakdowns.

Deploy your app – Apps SDK | OpenAI Developers

Manufact Manufact maintains mcp-use, a community MCP framework for building MCP servers, clients, agents, and app widgets in TypeScript and Python. For ChatGPT apps, mcp-use can generate MCP

Azure Developer CLI (azd)

AI agent extension: run, invoke, monitor, and deploy agents end-to-end from your terminal to Microsoft Foundry GitHub Copilot integration in azd init for AI-assisted project setup Deploy Container App

Self-Hosting AI Models: Hardware Requirements, Model Selection,

A practical guide to self-hosting AI models on your own infrastructure. Covers hardware requirements, VRAM and quantisation, model selection for 2026, cost comparisons with cloud APIs,

From Local Dev to Production: How to Deploy AI Models in 2025

Most developers start by testing models on their local machines. Tools like Ollama, Open WebUI, or custom scripts make it easy to run models like LLaMA 3 without much setup. These tools

DeepSeek Local: How to Self-Host DeepSeek (Privacy and Control)

DeepSeek is a powerful AI model that can be self-hosted locally for faster performance, improved privacy, and flexible configuration. This guide demonstrates how to self-host DeepSeek in

Quickstart: Deploy your first hosted agent

Learn how to deploy a containerized AI agent to Foundry Agent Service using the Azure Developer CLI, Microsoft Foundry Toolkit for Visual Studio Code extension, or Microsoft Foundry Skill.

How to build a high-performance AI server locally

Network Engineer and tech enthusiast NetworkChuck has provided a fantastic tutorial on how he built an AI server to run locally and provide large language model processing for affordable AI...

How To Deploy a Local AI via Docker

Learn to deploy your own local AI service using Docker containers for maximum security and control, whether you''re running on CPU, NVIDIA GPU or AMD GPU.

Running LLMs Locally in 2026: Ollama, llama.cpp, and Self-Hosted AI

Run LLMs on local hardware for privacy, lower costs, and faster inference—this guide covers Ollama, llama.cpp, hardware, quantization, and deployment tips.

Creating a GitHub Pages site

You can also customize your own build process locally or on another server. If you use a custom build process or a static site generator other than Jekyll, you can write a GitHub Actions workflow to build