Learn About Local AI
Everything you need to understand before deploying AI on your own hardware. Start from the basics and build up to advanced concepts.
What Is Local AI? A Complete Guide
Local AI means running AI models on your own hardware, with local data processing, no per-token API fees, and the ability to work offline.
Why Run AI Locally? 8 Reasons to Deploy Your Own AI
Running AI locally can keep data on hardware you control, removes per-token fees, avoids network latency, and gives you full control over models.
Local AI vs Cloud AI in 2026: Key Tradeoffs
Local AI vs cloud AI compared in 2026: privacy, cost, performance, model selection, customization, and compliance.
Local AI Hardware Guide: GPU, CPU, RAM, and Storage Requirements
A complete guide to the hardware you need to run AI locally: GPU VRAM requirements, CPU-only inference, RAM sizing, and Apple Silicon.
How to Choose the Right Local LLM in 2026: A Decision Framework
Pick the right local LLM in 2026: decision framework for Llama, Mistral, Gemma, Qwen, DeepSeek, and Phi by task, hardware, VRAM budget, and quality bar.
Understanding LLM Quantization: GGUF, GPTQ, AWQ, EXL2 Explained
A complete guide to LLM quantization: what it is, how it works, and how to choose between GGUF, GPTQ, AWQ, and EXL2 formats.
Local AI Terms Explained: The Complete Glossary
A comprehensive glossary of local AI terminology — from attention mechanisms to zero-shot learning. The definitive reference for anyone deploying AI locally.
Context Windows and KV Cache Explained
How context windows and KV cache work in local LLMs: why a bigger context window costs more VRAM at runtime than the model's own weights do.