Yes. It is free to use in the browser and the source code is available on GitHub.

Do I need to install anything?

No. The main app runs directly in the browser. You only need a modern web browser to use it.

Who is AnimatedLLM for?

It is for students, developers, and anyone who wants an intuitive visual explanation of LLM text generation and training mechanics.

Does AnimatedLLM run a real LLM?

No. It is an educational visualization. It does not perform live model inference or provide an API for building applications.

How does the deep-dive visualization work?

The model view walks through token-level Transformer processing during generation and training, showing how individual tokens are handled and how mistakes are learned from.

Can I use my own prompts or models?

The app is built around sample prompts and educational examples. It does not support custom model uploads or arbitrary production inference.

Directory/Search/AI Education/AnimatedLLM

AnimatedLLM

AnimatedLLM is an open-source educational tool that provides interactive visualizations to explain how large language models (LLMs) work. It covers text generation, training processes, and the inner workings of Transformer models. Users can explore step-by-step simulations of token generation, see how models learn from mistakes during training, and access a downloadable LLM cheatsheet. The app is designed for developers, students, and AI enthusiasts who want a hands-on understanding of LLM mechanics. Built by Zdeněk Kasner at ÚFAL MFF UK, it's free and open-source, making complex concepts accessible without requiring a background in machine learning.

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Added on

Nov 4, 2026

Transformer internalsInteractive visualizationsToken-level demosTraining walkthroughFree PDF cheatsheetNo signup

Product information

Everything worth knowing about AnimatedLLM

The complete picture — from everyday features to the technical detail builders and IT folks dig for.

Core features

What it actually does

Text Generation Basics

A simplified visualization of how a language model produces text, walked through on sample prompts so you can watch tokens appear one at a time.

Training Basics

A simplified visualization of the training process on sample documents, showing how a model learns from raw text before you touch the deeper material.

How-to Guide

Explains how the app was built and how to navigate it, so you are not left guessing which view shows what.

LLM Cheatsheet (PDF)

A downloadable one-page overview of how language models work — useful when you need the mental model without re-watching the animations.

Technical capabilities

Transformer Text Generation, Token by Token

Deeper track that shows how a Transformer actually processes each individual token during generation, rather than hiding it behind a black box.

Transformer Training Walkthrough

Deeper track that traces how the Transformer processes individual tokens during training and updates from its mistakes.

Must watch videos

How LLMs Actually Work — Tokens, Context Windows & Next-Word Prediction

9:52

Tutorial9:52

How LLMs Actually Work — Tokens, Context Windows & Next-Word Prediction

It looks like it's thinking. It's guessing the next word. This is Video 1 of the free animated AI Engineering course — how large language models actually work, from first principles: the tokens your words become, the context window the model reads from, and the prediction loop behind every single reply. What's inside: 0:00 – It's not thinking 0:44 – Tokens: what your words become 2:17 – Tokens are the invoice: what you actually pay for 3:20 – The prediction loop: how every reply gets written 5:07 – The context window: why the model forgets 7:06 – System, user, assistant: the three roles 8:39 – The one sentence worth keeping 9:16 – Next up: Attention Distilled from Chip Huyen's "AI Engineering" (O'Reilly) and the "LLM Engineer's Handbook" (Iusztin & Labonne), plus 7 years of software engineering experience. Full course playlist: https://www.youtube.com/playlist?list=PLA9nEG4IZAzM Next video: How Attention Works, Visually — subscribe so you don't miss it. #ai #aiengineering #llm #machinelearning #deeplearning #techeducation

AI Explained: What Does the Number of Parameters in an LLM Mean?

5:17

Tutorial5:17

AI Explained: What Does the Number of Parameters in an LLM Mean?

Welcome to the *AI Explained* series, where I break down the basics of artificial intelligence for you. In this episode, we'll dive into why the number of parameters is so crucial in large language models. You'll discover how neural networks are constructed, what the transformer architecture entails, and why the paper "Attention is All You Need" was a game-changer. Plus, we'll try to compare the parameters in a large language model to the complexity of the human brain. 00:00 - Introduction to Large Language Models 00:14 - Understanding Neural Networks 00:32 - Components of a Neural Network 01:13 - Example of a Simple Neural Network 02:28 - Scaling Up Neural Networks: Parameters and Layers 03:37 - Large Language Models and Transformer Architecture 04:25 - Comparing AI Models to Biological Brains 05:02 - Final Thoughts: Complexity of AI Models #AIExplained #NeuralNetworkParameters #LLM #LargeLanguageModels #Transformers #GPT #AttentionIsAllYouNeed

Visualizing transformers and attention | Talk for TNG Big Tech Day '24

57:44

Tutorial57:44

Visualizing transformers and attention | Talk for TNG Big Tech Day '24

An overview of transforms, as used in LLMs, and the attention mechanism within them. Based on the 3blue1brown deep learning series: https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi

Who uses it

Real use cases, no hype

LLM Fundamentals Learning

Understand how language models generate text step by step.

Transformer Internals

See how token-level processing works inside a Transformer.

Training Process Demos

Visualize how a model learns from sample documents.

Classroom Teaching

Use simple visualizations to explain LLM mechanics to students.

Self-Study Cheatsheet

Download the LLM cheatsheet as a quick reference PDF.

Developer Onboarding

Give engineers a visual primer before they work with LLMs.

Open Source Review

Inspect the GitHub repo to learn how the visualizations are built.

What's great

Free and browser-based
No install or setup required
Clear visuals for LLM basics
Simple and deep-dive modes

Technical strengths

Token-level Transformer walkthroughs
Separate generation and training visualizations
Open source on GitHub

Where it falls short

Not a production LLM tool
Limited to Transformer fundamentals
Sample prompts only, no custom input
Minimal UI and navigation depth

Technical limitations

No live model inference or API
Hardcoded educational examples
No model upload or extensibility

For technical folks

The deep-dive specs

Subject matter

Transformer-based language models

Format

Browser-based interactive visualizations

Source code

Open source on GitHub (kasnerz/animated-llm)

Author / affiliation

Zdeněk Kasner, ÚFAL MFF UK

FAQ

Includes technical Q&A

AnimatedLLM is an educational web app that visualizes how large language models generate text and how Transformers are trained.

Traffic Insights

Monthly Visits

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November 2026

Growth Rate

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Dominance

in Search

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Top Countries Breakdown

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Growth Trend

Stable

Traffic has remained stable.

Pricing overview

Unknown Pricing

AnimatedLLM Pricing

AnimatedLLM uses a unknown pricing structure. Real-time rates, custom enterprise quotas, and plan details are managed directly on their official platform.

Check Pricing on AnimatedLLM

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