Local LLM

Simple Definition

A local LLM is an AI language model that runs on your own computer — not on a company’s server in the cloud. You download the model, run it locally, and your data never leaves your machine.

Why Run an LLM Locally?

Privacy — Your inputs and outputs stay on your device. Nothing is sent to a cloud provider or used for training.

Offline access — Works without an internet connection.

Cost — No subscription or per-use API fees after the initial setup.

Control — You choose which model to use and how it behaves.

Speed (sometimes) — On a fast computer with a capable GPU, local inference can be quicker than waiting for a cloud API.

When Local LLMs Are Practical

Running a local LLM requires meaningful hardware — typically a modern computer with a capable GPU or enough RAM (8GB minimum, 16GB or more for larger models). This makes them:

Practical for developers, power users, and privacy-conscious professionals
Less practical for most people on average laptops (though this is improving rapidly)

Popular Tools for Running Local LLMs

Ollama — the simplest way to run models locally; handles setup automatically
LM Studio — a user-friendly desktop app for running local models
Jan — open-source desktop AI app
llama.cpp — command-line tool for running Llama-family models efficiently

Popular Local Models

Llama 3 (Meta) — widely used, available in several sizes
Mistral — efficient models that run well on consumer hardware
Phi (Microsoft) — small but capable models
Gemma (Google) — lightweight, designed for local use

LLM — what a local LLM is
On-Device AI — the broader concept of AI running on your own hardware
Open Weights — what makes local LLMs possible — publicly available model weights