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
Featured Guides
Running Local LLMs on AMD GPUs with ROCm and Ollama
Complete guide to running local LLMs on AMD GPUs using ROCm 6.x and Ollama. Covers supported GPUs, installation, performance benchmarks, and cost comparison with NVIDIA.
Building a Local RAG Pipeline with Ollama and Open WebUI
Step-by-step guide to building a retrieval-augmented generation pipeline locally using Ollama, Open WebUI, embedding models, and vector databases.
Running Local LLMs with Ollama and llama.cpp
Guide to installing, configuring, and optimizing local AI models using Ollama and llama.cpp with parameter tuning, quantization, and GPU acceleration
RTX 3090 for AI: Best Value GPU for Local LLM Hosting
Why the NVIDIA RTX 3090 is the best value GPU for local AI inference and fine-tuning. Benchmarks, pricing, power costs, and capacity analysis.
Self-Hosting Open WebUI with Docker: Setup Guide
Learn to deploy Open WebUI with Docker for private ChatGPT-like access, including Ollama integration, GPU setup, and production security configs.
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Qwen 3.5 Local Setup Guide: Ollama vs LM Studio Performance
TL;DR Running Qwen 3.5 locally requires choosing between Ollama’s CLI-first approach and LM Studio’s GUI-driven workflow. Both tools serve the same GGUF model files but differ significantly in performance characteristics and operational overhead. Ollama excels at automated deployments and scripting. Install with curl -fsSL https://ollama.com/install.sh | sh, pull the model using ollama pull qwen2.5-coder:7b, and start serving on port 11434. Memory usage stays consistent across inference requests, making it predictable for containerized environments. The CLI interface integrates cleanly with shell scripts and CI/CD pipelines. ...
Complete Guide to Open WebUI Tools for Local AI Models
TL;DR Open WebUI’s Tools feature transforms your local LLM into an AI agent capable of executing real-world tasks through function calling. Instead of just chatting with your model, you can build custom tools that let it query APIs, run system commands, process files, or integrate with external services – all while keeping your data local. ...
LM Studio API Key Setup Guide for Local AI Models 2026
TL;DR LM Studio provides an OpenAI-compatible API server that runs entirely on your local machine, eliminating the need to send data to external services. The API key system in LM Studio serves as an authentication layer for applications connecting to your local inference server, preventing unauthorized access from other processes or network clients. ...
Running Image Generation Models Locally with Ollama in 2026
TL;DR Ollama now supports image generation models through its standard API on port 11434, letting you run Stable Diffusion and similar models entirely offline. Install Ollama with curl -fsSL https://ollama.com/install.sh | sh, then pull an image model like ollama pull stable-diffusion. Generate images by sending prompts to the same REST endpoint you use for text models – no separate services required. ...
How to Install LM Studio on Ubuntu 2026: Complete Setup
TL;DR LM Studio is a desktop GUI application for running large language models locally on Ubuntu 2026. Unlike command-line tools, it provides a graphical interface for downloading models from Hugging Face and running them without sending data to external servers. The application includes a local OpenAI-compatible API server, making it useful for developers who want to test AI integrations privately. ...
Turn Idle GPUs Into P2P AI Grid With Go Binary Tools
TL;DR This guide shows you how to build a peer-to-peer GPU sharing network using Go-based tools that let idle machines serve AI inference requests across your local network or homelab. Instead of leaving GPUs idle on workstations overnight, you can pool them into a distributed inference cluster that routes requests to available hardware. ...
GAIA Framework: Build AI Agents on Your Local Hardware
TL;DR GAIA (Generative AI Integration Architecture) is an open-source framework that lets you build autonomous AI agents running entirely on your local hardware using Ollama, LM Studio, or llama.cpp as the inference backend. Unlike cloud-based agent frameworks, GAIA keeps your data on-premises and gives you full control over model selection, resource allocation, and execution policies. ...
Docker Pull Issues in Spain: Self-Hosting AI with Ollama
TL;DR Docker Hub rate limits and regional connectivity issues in Spain can block container pulls, disrupting self-hosted AI deployments. The primary workaround is switching to mirror registries or running Ollama natively without Docker. For immediate relief, configure Docker to use alternative registries. Edit /etc/docker/daemon.json to add registry mirrors: { "registry-mirrors": [ "https://mirror.gcr.io" ] } Restart Docker with sudo systemctl restart docker and retry your pull. This routes requests through Google’s mirror, bypassing Docker Hub entirely. ...
RTX 3090 Used Market 2026: Best Bang for Buck Local AI Setup
TL;DR The RTX 3090 remains a compelling choice for local AI workloads in 2026, particularly on the used market where prices have stabilized considerably below launch MSRP. With 24GB of VRAM, this card handles most local LLM deployments that would otherwise require multiple newer cards or expensive cloud instances. On the secondary market, expect to find RTX 3090s from mining operations, workstation upgrades, and gamers moving to newer architectures. The key advantage is VRAM capacity – running a 70B parameter model quantized to 4-bit requires roughly 40GB, making dual RTX 3090s viable where a single RTX 4090 (24GB) falls short. For 13B to 34B models, a single card provides comfortable headroom. ...
Running Claude-Style Models in LM Studio: Complete 2026
TL;DR LM Studio provides a GUI-first approach to running Claude-style coding models locally without command-line complexity. Download the application from lmstudio.ai, install it on your Linux, macOS, or Windows system, and you gain immediate access to Hugging Face’s model repository through an integrated browser. The workflow centers on three steps: discover models through LM Studio’s search interface, download your chosen quantization format (Q4_K_M for balanced performance, Q8_0 for accuracy), and launch the built-in OpenAI-compatible API server. Models like DeepSeek Coder V2, Qwen2.5-Coder, and CodeLlama variants work particularly well for development tasks. ...
