Practical guides for self-hosting AI models on your own hardware.
Ollama, Open WebUI, LM Studio, llama.cpp — set up local LLMs,
keep your data private, cut API costs, and run AI offline.
Also see: [AI Linux Admin](https://ailinuxadmin.com) for AI-powered sysadmin guides | [SecureStackOps](https://securestackops.com) for Linux security
Jan AI: Complete Guide to Self-Hosting LLMs on Your Local Machine
TL;DR Jan AI is an open-source desktop application that lets you run large language models entirely on your local machine—no cloud dependencies, no data leaving your network. Think of it as a polished alternative to Ollama with a ChatGPT-like interface built in. What makes Jan different: Unlike command-line tools like llama.cpp or Ollama, Jan provides a complete GUI experience with conversation management, model switching, and system resource monitoring. It supports GGUF model formats and runs models from Llama 3.1, Mistral, Phi-3, and other popular families. ...
GPU vs CPU Inference with Ollama: Performance Guide for Consumer Hardware
TL;DR GPU inference with Ollama delivers 5-15x faster token generation compared to CPU-only setups on consumer hardware. A mid-range NVIDIA RTX 4060 (8GB VRAM) generates ~40-60 tokens/second with Llama 3.1 8B, while a modern CPU (Ryzen 7 5800X) manages only ~8-12 tokens/second. The performance gap widens dramatically with larger models. ...
How to Set Up a Local AI Assistant That Works Offline
TL;DR This guide walks you through deploying a fully offline AI assistant using Ollama and Open WebUI on a Linux system. You’ll run models like Llama 3.1, Mistral, or Qwen locally without internet connectivity or cloud dependencies. What you’ll accomplish: Install Ollama as a systemd service, download AI models for offline use, deploy Open WebUI as your chat interface, and configure everything to work without external network access. The entire stack runs on your hardware—a laptop with 16GB RAM handles 7B models, while 32GB+ systems can run 13B or larger models. ...
Securing Your Local Ollama API: Authentication and Network Isolation
TL;DR By default, Ollama exposes its API on localhost:11434 without authentication, making it vulnerable if your network perimeter is breached or if you expose it for remote access. This guide shows you how to lock down your local Ollama deployment using reverse proxies, API keys, and network isolation techniques. Quick wins: Place Nginx or Caddy in front of Ollama with basic auth, restrict API access to specific IP ranges using firewall rules, and run Ollama in a dedicated Docker network or systemd namespace. For multi-user environments, implement token-based authentication using a lightweight auth proxy like oauth2-proxy or Authelia. ...
LM Studio vs Ollama: Complete Comparison for Local AI
TL;DR LM Studio and Ollama are both excellent tools for running LLMs locally, but they serve different use cases. LM Studio offers a polished GUI experience ideal for experimentation and interactive chat, while Ollama provides a streamlined CLI and API-first approach perfect for automation and production deployments. Choose LM Studio if you: ...
How to Run Llama 3 Locally with Ollama on Linux
TL;DR Running Llama 3 locally with Ollama on Linux takes about 5 minutes from start to finish. You’ll install Ollama, pull the model, and start chatting—all without sending data to external servers. Quick Setup: curl -fsSL https://ollama.com/install.sh | sh # Pull Llama 3 (8B parameter version) ollama pull llama3 # Start chatting ollama run llama3 The 8B model requires ~5GB disk space and 8GB RAM. For the 70B version, you’ll need 40GB disk space and 48GB RAM minimum. Ollama handles quantization automatically, so you don’t need to configure GGUF formats manually. ...
Self-Hosting Open WebUI with Docker: Installation and Configuration
TL;DR Open WebUI is a self-hosted web interface for running local LLMs through Ollama, providing a ChatGPT-like experience without cloud dependencies. This guide walks you through Docker-based deployment, configuration, and integration with local models. What you’ll accomplish: Deploy Open WebUI in under 10 minutes using Docker Compose, connect it to Ollama for model inference, configure authentication, and set up persistent storage for chat history and model configurations. ...
llama.cpp vs Ollama: Which Local LLM Runner Should You Use
TL;DR - Quick verdict: Ollama for ease-of-use and Docker integration, llama.cpp for maximum control and performance tuning Ollama wins for most self-hosters who want their local LLM running in under 5 minutes. It handles model downloads, GPU acceleration, and exposes a clean OpenAI-compatible API at localhost:11434. Perfect for Docker Compose stacks with Open WebUI, and it integrates seamlessly with tools like Continue.dev for VSCode or n8n workflows. ...
Best Local LLMs for 8GB RAM: Llama 3, Mistral, and Phi Compared
TL;DR Running local LLMs on 8GB RAM systems is entirely feasible in 2026, but requires careful model selection and quantization strategies. Llama 3.2 3B (Q4_K_M quantization) delivers the best balance of capability and efficiency, using approximately 2.3GB RAM while maintaining strong reasoning abilities. Mistral 7B (Q3_K_M) pushes boundaries at 3.8GB RAM, offering superior performance for coding tasks but requiring aggressive quantization. Phi-3 Mini (3.8B parameters, Q4_K_S) sits in the middle at 2.1GB, excelling at structured outputs and JSON generation. ...
Open WebUI vs Ollama Web UI: Which Interface is Right for You
TL;DR Open WebUI (formerly Ollama WebUI) is the actively maintained, feature-rich choice for most users, while Ollama Web UI refers to the deprecated original project that’s no longer developed. Open WebUI offers a ChatGPT-like interface with multi-user support, RAG (Retrieval-Augmented Generation) for document chat, model management, conversation history, and plugin architecture. It runs as a Docker container or Python application, connecting to your local Ollama instance on port 11434. Perfect for teams, homelab setups, or anyone wanting a polished UI with authentication and persistent storage. ...