Deployment Guides
Step-by-step guides for every platform, use case, and skill level. From your first local chatbot to enterprise-scale deployment.
Getting Started
New to local AI? Start here.
How to Choose the Right Local LLM for Your Use Case
A decision framework for selecting the best local LLM based on your task, hardware, and requirements. Includes model comparisons, quantization guide, and VRAM recommendations.
Your First Local AI in 5 Minutes: Ollama Quickstart
Install Ollama and start chatting with an AI model on your own machine in under 5 minutes. No GPU required. Works on Mac, Windows, and Linux.
Local AI Hardware Guide: What Do You Need to Run LLMs?
Complete hardware guide for running local LLMs. GPU recommendations by budget, VRAM requirements, CPU-only setups, Apple Silicon performance, and used GPU buying advice.
The Local AI Stack: Choosing Your Engine, UI, and Framework
Understand the three-layer local AI architecture and choose the right inference engine, user interface, and application framework for your needs. Includes 5 reference stacks for common scenarios.
What Is a Local LLM? The Complete Beginner's Guide
Learn what a local LLM is, how it works, what hardware you need, and why running AI on your own machine matters for privacy, cost, and control.
Platform Guides
Windows, macOS, Linux, Android, iOS, Docker.
Docker and Kubernetes for Local AI: Container Deployment Guide
Deploy local LLMs with Docker and Kubernetes. Covers Ollama Docker setup, Open WebUI compose stacks, NVIDIA Container Toolkit, GPU passthrough, and Kubernetes GPU scheduling.
Running Local LLMs on Linux: Ubuntu, Fedora, and Arch Guide
Complete guide to running local LLMs on Linux. Covers NVIDIA CUDA and AMD ROCm setup, Ollama installation, building llama.cpp from source, systemd services, and performance tuning for Ubuntu, Fedora, and Arch.
Running Local LLMs on macOS: Apple Silicon Optimization Guide
Complete guide to running local LLMs on macOS with Apple Silicon. Covers Ollama, MLX, LM Studio, unified memory optimization, and model recommendations for M1 through M4 chips.
Running Local LLMs on Windows: Complete Setup Guide
Step-by-step guide to running local LLMs on Windows with NVIDIA and AMD GPUs. Covers Ollama, LM Studio, WSL2, CUDA setup, and troubleshooting common issues.
Use Cases
RAG, chatbots, code assistants, voice AI, and more.
Enterprise Local AI: Deploying LLMs for Your Organization
Deploy local LLMs for enterprise use. Covers architecture patterns, vLLM with NVIDIA GPUs, multi-user interfaces with LibreChat, security hardening, compliance considerations, and cost analysis.
Fine-Tuning Your Own Local Model: From Data to Deployment
Learn when and how to fine-tune a local LLM. Covers dataset preparation, QLoRA training with Unsloth, evaluation, GGUF export, and deployment with Ollama.
Local AI Code Assistant: Setting Up Copilot Without the Cloud
Set up a fully local AI code assistant using Continue with Ollama in VS Code, Tabby for self-hosted completions, and Aider for terminal-based coding. Includes model benchmarks and configuration.
Local Image Generation: Stable Diffusion, FLUX, and ComfyUI Guide
Generate images locally with Stable Diffusion, FLUX, and ComfyUI. Covers setup, ControlNet, LoRAs, VRAM management, prompt engineering, and workflow optimization.
Building a Local RAG Chatbot: Documents, Embeddings, and Retrieval
Build a fully local RAG (Retrieval-Augmented Generation) chatbot that answers questions about your documents. Covers architecture, chunking strategies, embedding models, vector databases, and prompt engineering.
Building a Local Voice Assistant: Whisper + LLM + TTS
Build a fully local voice assistant pipeline with speech-to-text (Whisper.cpp), an LLM for processing (Ollama), and text-to-speech (Piper/Kokoro). Includes latency optimization and wake word detection.
Tool Guides
Deep dives into specific tools.