LLM Architecture: Tokens, Attention, and Decoding

Follow a decoder-only language model from tokenization through attention blocks to next-token selection. Distinguish model architecture from an application stack.

Open the LLM Architecture Diagram Generator to draft the visual, then use the topic-specific checks below to review it.

Original AI-generated llm architecture diagram teaching reference. Simplified for explanation; review labels and relationships before use.

Choose a model family

Decoder-only, encoder-only and encoder-decoder models do not share the same top-level structure. Use the LLM architecture diagram generator with a stated family and purpose. An inference explanation should not quietly import a cross-attention branch from a different architecture.

Separate computation from serving

Token embeddings, attention and feed-forward transformations belong inside the model. A web API, queue and retrieval store belong to a serving or application view. Keep these on separate levels so the drawing answers one question rather than mixing neural operations with deployment boxes.

Label the autoregressive step

The model produces vocabulary scores and the decoding policy selects a next token. Explain that generation repeats this step with updated context. A conceptual diagram can omit caches and normalization details if the caption says so; it should not invent a model-specific layer count.

Reference key

Mistakes to check

Using the original Transformer as every LLM

Some models omit an encoder or change position encoding, attention and normalization. Choose the intended architecture instead of copying an all-purpose block list.

Calling logits generated words

Vocabulary scores are followed by a selection policy. Show this boundary explicitly.

Claiming speed from a clean layout

A block diagram does not establish latency, memory use or quality. Measurements require a specified implementation and workload.

A caption that states the abstraction

Name the model family and describe which details are left out. For example: conceptual decoder-only inference path; normalization, residual connections and cache management omitted for clarity. This distinguishes a teaching figure from an implementation specification and gives reviewers a useful target for checking the draft.

Common questions

What does the LLM architecture diagram generator create?

It drafts a conceptual model structure from your description, with labeled components and connections.

Does it infer a proprietary model architecture?

No. Supply verified details for a named model; the tool does not discover its hidden design.

Can I show training instead of inference?

Yes. State the objective and where loss and parameter updates belong, rather than reusing an inference-only layout.

How is this different from a RAG architecture diagram?

This page focuses on computation inside a language model. A RAG diagram focuses on retrieving and supplying external context around a model.

Sources and editorial notes

• Vaswani et al.: Attention Is All You Need
• Hugging Face: Generation with LLMs

Prepared by the AcaDraw product team on November 15, 2026. The reference illustration is original AI-generated artwork. It is a simplified explanatory model, not an experimental result, production specification or independently reviewed analysis.