How to Install Llama 4 on Ubuntu 26.04 and 24.04 – Model Sizes, Commands and Examples

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Install Llama on Ubuntu, Without Sending a Word to the Cloud

To install Llama on Ubuntu, install Ollama first, then run ollama run llama4:16x17b to download Llama 4 and start chatting. Scout is a 67 GB download, so if your machine cannot take that, use ollama run llama3.1:8b at 4.9 GB instead.

Llama is a family of AI models built by Meta. Ollama is the free tool that downloads and runs them. Two different things with confusingly similar names: you install Ollama once, then use it to run Llama, Mistral, Qwen or anything else.

Installer: Ollama
Install methods: 3
Smallest model: 1.3 GB
Cost: Free
Before You Start
Ollama installed
Required. It is the tool that runs Llama. One command, covered below.
Account
A user that can run sudo. The installer changes system files.
Free disk space
2 GB for a small model. Far more for the large ones, so check first.
Graphics card
Optional. Speeds things up a lot, but Llama runs on the processor alone.
Connection
Needed once, to download. After that Llama works offline.
Time
Two minutes to install. The model download takes longer.

What Llama 4 Is, in Short

Llama 4 is built differently from every Llama before it. That changes what it can do, and it changes how much room it needs.

  • It is split into many small sections, and only a few switch on per question. That is what keeps it fast.
  • The whole model still sits on your drive, which is why it answers like a mid-sized model but stores like a huge one.
  • It reads images as well as text. Older Llama versions are text only.
  • It is the most capable version you can run at home, and by far the biggest download.

Download Sizes, Drawn to Scale

Below are the download sizes reported by Ollama. The bars are drawn to scale, which is the fastest way to see the problem.

Download size, drawn to scale
llama3.2:1b1.3 GB
llama3.2:3b2.0 GB
llama3.1:8b  Light fallback4.9 GB
llama3.3:70b43 GB
llama4:16x17b (Scout)67 GB
llama4:128x17b (Maverick)245 GB

Sizes as published by Ollama, September 2026. Bars are linear against Maverick at 245 GB, which is why the small models barely register.

Scout is the entry point to Llama 4 and the version this guide installs. It is a serious download, so check your free disk space before you start it. If your machine cannot take 67 GB, Llama 3.1 8B is the lighter route and the rest of this guide works the same way with that tag substituted.

Which Llama Model Should You Run?

Find the card that matches your machine. If you are between two, take the smaller one first. Downloading a bigger model later is easy. Waiting an hour for one that will not fit is not.

Current release
Workstation / multi-GPU
llama4:16x17b

67 GB download. The newest Llama. It reads images as well as text. Needs a workstation with lots of memory, or more than one graphics card.

Maverick at 245 GB is larger still.
8 GB GPU or better
llama3.1:8b

4.9 GB download. The lighter route if Llama 4 is out of reach. Handles code review, long writing tasks and general questions without feeling limited.

Text only. No image input.
24 GB GPU or more
llama3.3:70b

43 GB download. Noticeably stronger reasoning than 8B. Needs either a large GPU or enough system RAM to spill into, which slows it down.

Check your free disk space first.
No GPU / old laptop
llama3.2:3b

2.0 GB download. Runs on CPU alone. Good for summarising, rewriting and simple questions. Drop to llama3.2:1b if it still struggles.

Expect a few words per second.

The graphics card sizes above are our guidance, based on how big each model is. Ollama publishes download sizes, not graphics memory requirements, so treat these as a starting point rather than a hard rule.

Install Ollama First

Llama does not run on its own. Ollama is the program that downloads the model files and runs them, and it has to be installed before anything on this page works. One command does it:

Install Ollama

curl -fsSL https://ollama.com/install.sh | sh
Want the full picture?

That script is the quickest route. There are also a tarball install and a Docker image, plus the background service, updating and clean removal. Our full guide to installing Ollama on Ubuntu covers all of it. Everything below assumes Ollama is working.

What a Working Install Looks Like

Whichever method you picked, this is where you are heading. The first time you run a model, Ollama downloads it and shows a progress bar. After that it drops you straight into a chat.

mike@ubuntu: ~

Example output

$ ollama run llama4:16x17b
pulling manifest
pulling 4d1a2f8b9c33  100% ██████████████  67 GB
verifying sha256 digest
success
>>> Explain what a swap file does, in two sentences.
A swap file is space on your hard drive that Ubuntu uses when it runs
out of RAM. It keeps programs from crashing when memory fills up, but
it is much slower than real RAM.
>>> /bye
$  

An illustration of a normal session. Your download ID and wording will differ.

Download and Run Llama

Ollama downloads a model the first time you ask for it. You do not need a separate download step, though pulling first is useful when you want it finished before you sit down.

1
Pull the model

This fetches Llama 4 Scout, which is 67 GB. Start it when you do not need the connection for anything else. Running lighter hardware? Swap the tag for llama3.1:8b and every command below still works.

Terminal

ollama pull llama4:16x17b
2
Start a conversation

This drops you into an interactive chat. Type a question, press Enter, and the model answers in your terminal.

Terminal

ollama run llama4:16x17b

Type /bye to leave the chat. Running ollama on its own opens an interactive menu if you would rather pick from a list.

3
Manage what you have downloaded

Models add up quickly. These two commands show what is on disk and remove what is not earning its space.

Housekeeping

ollama list
ollama rm llama4:16x17b

When Llama Will Not Run

Most install problems belong to Ollama rather than Llama, and our Ollama guide covers those. This is the one you are most likely to hit with Llama itself.

Error: model requires more system memory than is available

You asked for a model that will not fit. This is the most common wall people hit with Llama 4, because Scout needs far more than a typical desktop has. Drop to a smaller model and it will work:

Use a smaller model

ollama run llama3.1:8b

Frequently Asked Questions

Is Llama free to use on Ubuntu?

Yes. The model weights are free to download and run under Meta’s community licence, and Ollama is open source. You pay nothing to run a model locally. The only cost is disk space, electricity and your own hardware.

Can I run Llama without a GPU?

Yes, but keep the model small. Llama 3.2 1B and 3B run on CPU alone on most modern machines. Expect a few words per second rather than instant replies. Anything at 8B or above without a GPU becomes slow enough to be frustrating.

Which Llama model should I run on Ubuntu?

Llama 4 Scout, tagged llama4:16x17b, is the current release and the one to run if your hardware allows it. It is a 67 GB download and expects a high-memory workstation or multiple GPUs. If that is out of reach, llama3.1:8b at 4.9 GB is the standard lighter choice, and llama3.2:3b at 2.0 GB runs without a GPU at all.

Can I run Llama 4 on a normal desktop?

Usually not. Llama 4 splits itself into many sections and only runs a few at a time, but the whole model still has to be stored, so Scout is a 67 GB download and Maverick is 245 GB. Check your free disk space first. If that is out of reach, Llama 3.1 8B gives you most of the everyday capability at 4.9 GB.

What is the difference between Llama and Ollama?

Llama is the AI model, built by Meta. Ollama is the free program that downloads models and runs them on your computer. The names look alike but they are separate projects. You install Ollama once, then use it to run Llama, Mistral, Qwen or any other model.

Next step
One command gets Llama 4 onto your machine

Install Ollama, pull Llama 4, and you have a private assistant that works with the network unplugged.

More Ubuntu guides: Install Ollama on Ubuntu  ·  Best Open Source AI Tools for Ubuntu  ·  Install Docker on Ubuntu  ·  Excellent Code Editors for Ubuntu