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Understand AI Visually: Start Here

Start with attention, KV caching and quantization, then explore visual explanations throughout the hub: model architectures, serving, GPUs, parallelism and Linux.

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Watch · explore · understand

Build the model one idea at a time

Follow the learning path from pieces of text to information mixing, memory reuse, number formats and whole architectures.

AI-assisted learning visualization · schematic article explanation

Step 1 / 5Begin with a piece of text

A token is a piece of text represented by an ID. The model turns these IDs into lists of numbers that later operations can process.

WorkflowCurrent stepSelect a block to explain it
Token: a piece of text
Text becomes IDs and numerical representations.
    What to remember

    Attention explains the information flow; caching and precision explain its storage costs; architectures connect these ideas.

    This is a learning order, not a literal model execution graph. The three lessons use deliberately tiny examples, and different model families connect these components differently.

    Terms and the complete walkthrough
    Token
    A text unit mapped to a numerical identifier.
    Representation
    A list of numbers encoding information for the model.
    Architecture
    The arrangement of operations, layers and information paths in a model.
    1. Begin with a piece of text

      A token is a piece of text represented by an ID. The model turns these IDs into lists of numbers that later operations can process.

    2. Follow the attention lesson

      A query expresses what one position looks for; keys provide comparisons and values supply information. The first lesson shows how numerical attention weights mix those values.

    3. Reuse keys and values

      The cache lesson follows what can be kept after a prompt is processed. Later steps reuse earlier keys and values while adding data for newly processed tokens.

    4. Inspect how numbers are stored

      Quantization stores numbers using fewer representable values and shared scales. Compare the INT4 and FP4 number lines before interpreting a model’s “4-bit” label.

    5. Connect the parts in a real architecture

      Continue with the LLM overview and an architecture article. Use the same questions there: what information moves, what is stored, and which operation changes it?

    All connections

    • Token: a piece of text → Attention: combine information: numerical input
    • Attention: combine information → KV cache: reuse previous work: keys and values
    • KV cache: reuse previous work → Precision: store numbers compactly: stored numbers
    • Precision: store numbers compactly → Architecture: connect the parts: memory and computation
    Set your own pace. Pause at any time or select a step directly.

    You can use a language model without understanding its internals. When you want to understand why context matters, why longer conversations need more memory, or what “4-bit” means, it helps to see the individual steps.

    This learning path introduces one idea at a time. You need a little familiarity with computers and numbers; you do not need machine-learning experience or matrix algebra. Each lesson explains its vocabulary before using it.

    Three questions, three short lessons

    1. How does attention combine information? Follow a query, compare keys, and watch numerical weights form a mixture of values.
    2. What does the KV cache reuse? Follow a prompt into the cache, then see what changes when another token arrives.
    3. What does NVFP4 versus INT4 mean? Compare number lines, rounding and shared scales to understand what four bits can represent.

    Start in that order: the cache lesson builds on the keys and values introduced in attention. The quantization lesson then asks how numbers like these can be stored more compactly.

    Learn at your own pace

    Use the diagrams one step at a time or play a complete sequence. Pause whenever a number changes and check the explanation beside it. The text below each diagram also describes the complete idea, so the lessons remain readable without playback or JavaScript.

    The examples are deliberately small and invented for teaching. They illustrate calculations and tradeoffs; they are not measurements of a particular model.

    For the larger picture, open How LLMs Actually Work. Each lesson also links to a deeper guide when you are ready for architecture details and memory calculations.

    Keep exploring: a diagram in every technical article

    The three lessons are a starting point. Every technical article and tool page also has its own visual explanation. Select a block to learn its role, follow the highlighted connections, or play the sequence. Open “Terms and the complete walkthrough” for definitions and a readable version of every step.

    What you want to understandWhere to start
    How a whole model fits togetherQwen3-Next architecture
    How GPUs divide the workTensor parallelism, pipeline parallelism, expert parallelism
    How speech becomes textGranite Speech TurboCTC
    How search supplies useful contextRAG retrieval
    How model serving uses memory and timevLLM and SGLang, inference math
    How GPU networks connectNVLink
    How to run a GPU inside a containerNVIDIA GPU containers on Ubuntu

    Architecture diagrams explain structure and data flow. Comparison diagrams separate assumptions, evidence and conclusions. They simplify the article's central idea; the technical discussion below each diagram supplies the details and limits. Use the topic overview to continue through the rest of the hub.