How to Run Large Language Models Locally: A Practical Guide

By Ivana Tilca · September 17, 2026 · 5 min read

Running a capable AI model on your own laptop is no longer science fiction. Here's why you'd want to — privacy, cost, control — the one thing that decides whether a model runs (memory), how to pick one, and how to go from zero to chatting with a private model using Ollama.

A few years ago, running a capable AI model on your own laptop sounded like science fiction. Today it's a Tuesday afternoon project. Open-weight models have gotten good — genuinely good — and the tools to run them have gotten simple enough that you don't need a PhD or a data center. In this guide I'll walk you through running large language models locally: why you'd want to, what hardware you actually need, and how to go from zero to chatting with a private model on your own machine.

Why run a model locally at all?

The cloud is convenient, so why bother? A few reasons that matter more than people expect:

Privacy. Your prompts and data never leave your machine. For anything sensitive — client work, personal notes, proprietary code — that's not a nice-to-have, it's the whole point.

Cost. No per-token bill. Once the model is on your disk, you can run it as much as you want for the price of electricity.

Offline and reliable. No internet, no outage, no rate limit. The model is just there.

Control and learning. You decide the model, the settings, the system prompt. And nothing teaches you how these systems actually work faster than running one yourself.

The trade-off, of course, is that a model running on your laptop won't match the absolute frontier models in the cloud. But the gap has narrowed dramatically, and for a huge range of everyday tasks, a good local model is more than enough.

The one thing that decides everything: memory

If you remember one concept from this article, make it this: the limiting factor for running a model locally is memory, not raw processor speed. The model's weights have to fit in memory to run well — ideally in your GPU's VRAM, or, on Apple Silicon Macs, in the unified memory shared between CPU and GPU.

A rough rule of thumb: a model needs a little more memory than its size on disk. And model size depends on two things — the number of parameters and the quantization.

Parameters are the model's "size" — 7B (7 billion), 14B, 70B, and so on. More parameters generally means more capable, and more memory.

Quantization is compression. Instead of storing each weight in full precision, quantized models store them in fewer bits (4-bit is the popular sweet spot). A 4-bit quantized model uses roughly a quarter of the memory of the full version, with only a small quality loss. This is the trick that makes local models practical.

As a practical guide: a 7–8B model at 4-bit needs roughly 5–6 GB of memory and runs comfortably on most modern laptops. A 14B model wants around 10–12 GB. A 70B model needs a serious machine — 40+ GB — which is exactly why Apple's high-memory unified-memory Macs have become such a big deal for local AI.

Picking a model

The open-weight world is rich and moves fast, but the families worth knowing are Llama (Meta), Qwen (Alibaba), Mistral, Gemma (Google), Phi (Microsoft), and newer standouts like the GLM and DeepSeek families. Most are available in several sizes.

My advice: start small. Download a 7–8B model first. It'll run fast, teach you the workflow, and handle a surprising amount — summarizing, drafting, answering questions, light coding. Move up to a bigger model only once you've hit a real limitation, and your hardware can handle it.

Getting started: the easy path with Ollama

The simplest way in is a tool called Ollama. It handles downloading, quantization, and running, and gives you both a chat interface and a local API. Here's the whole flow:

Install Ollama from its website, then in your terminal, pull and run a model:

That's it. The first run downloads the model (a few gigabytes); after that it's instant and offline. You're now chatting with a model that runs entirely on your machine.

Want to use it from your own code? Ollama exposes a local API on port 11434:

If you prefer a friendly graphical app over the terminal, LM Studio is an excellent alternative — it gives you a polished chat UI, a model browser, and the same kind of local API, all without touching a command line. And if you're in the Microsoft ecosystem, Foundry Local brings the same on-device idea to Azure-flavored workflows.

Setting realistic expectations

Local models are wonderful, but let's be honest about the limits so you're not disappointed:

They're smaller than frontier cloud models, so on the hardest reasoning or the most obscure knowledge, they'll sometimes fall short.

Speed depends on your hardware. On a good GPU or a modern Apple Silicon Mac, responses are snappy; on an older machine, larger models can feel slow.

Very long contexts and huge documents push memory hard — another place where "start small" pays off.

For summarizing, drafting, brainstorming, coding help, private Q&A, and learning, a local model shines. For the absolute cutting edge, the cloud still has its place. The smart move is knowing which tool fits which task — and increasingly, that's a real choice you get to make.

Why this matters

Here's what genuinely excites me about all of this, beyond the technical fun. Running a capable model on your own machine, without sending your data to anyone, without paying per thought, is a quiet form of independence. It puts a powerful tool directly in your hands and keeps it there. As these models keep improving and our everyday machines keep getting more capable, that independence only grows.

So if you've been curious, don't overthink it. Install Ollama, pull a small model, and ask it something this afternoon. The distance between "science fiction" and "running on my laptop" turned out to be a single command.