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, with model comparisons and a VRAM guide.
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 2026: GPUs and VRAM
What you need to run local LLMs in 2026: GPU picks by budget, VRAM requirements, CPU-only setups, Apple Silicon performance, and used-GPU pricing.
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.
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: Ollama Docker setup, Open WebUI compose stacks, NVIDIA Container Toolkit, and GPU scheduling.
Running Local LLMs on Linux: Ubuntu, Fedora, and Arch Guide
Complete guide to running local LLMs on Linux. 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: Ollama, MLX, LM Studio, and unified memory optimization.
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.
Choosing an Embedding Model for Local RAG
Choosing an embedding model for local RAG: Nomic Embed, MxBai Embed Large, and BGE-M3 compared on size, context length, and language support.
Enterprise Local AI: Deploying LLMs for Your Organization
Deploy local LLMs for enterprise use: architecture patterns, vLLM with NVIDIA GPUs, multi-user interfaces, and security hardening.
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.
Local Image Generation 2026: SD, FLUX, ComfyUI
Generate images locally in 2026 with Stable Diffusion, FLUX, and ComfyUI: setup, ControlNet, LoRAs, and VRAM management.
Building a Local RAG Chatbot with Embeddings
Build a fully local RAG chatbot that answers questions about your documents: architecture, chunking, embedding models, and vector databases.
Building a Local Voice Assistant: Whisper + LLM + TTS
cpp), an LLM for processing (Ollama), and text-to-speech (Piper/Kokoro). Includes latency optimization and wake word detection.
Integrations
Combine tools and frameworks.
Tool Guides
Deep dives into specific tools.
