Learn · Local AI path

From concept
to a running model.

Start with what local AI is, understand the hardware constraints, then move through memory, model formats and a reproducible Windows setup.

Step 01

What is Local AI?

Understand what runs locally and what changes when inference moves to your own hardware.

Step 02

Hardware for Local AI

Learn which CPU, RAM, GPU and storage constraints matter.

Step 03

LLM Model Size

Understand parameters, precision and why parameter count is not a memory figure.

Step 04

VRAM Explained

Understand why model file size is not the same thing as runtime GPU memory.

Step 05

Quantization

See how lower-precision model weights change memory requirements and trade-offs.

Step 06

GGUF

Learn the model format commonly used by llama.cpp-based local workflows.

Step 07

Context Length

Separate model context limits from active context and understand the memory trade-off.

Step 08

CPU vs GPU

Understand CPU inference, full GPU residency and partial GPU offload.

Step 09

Ollama

Understand the runtime, model management and local API.

Step 10

Install Ollama on Windows

Follow the tested tutorial from installation through a local API request.

Step 11

Inspect real measurements

See Gemma 3 1B and Qwen3 4B tested on a GTX 1060 3GB.