LLMs (Part-01): The High-level Architecture of Transformers

2026-06-27 · Source: LLM on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

The article describes the high-level architecture of Transformer neural networks, which are foundational to modern Large Language Models (LLMs). It places LLMs within the AI hierarchy (AI > ML > DL > Foundation Models > LLMs/LRMs) and focuses on the original 2017 "Attention is All You Need" paper. The architecture includes an external tokenizer, followed by internal components: Embedding & Positional Encoding Layers, an Encoder Stack (six blocks with Multi-Head Self-Attention and Feed-Forward Network layers), a Decoder Stack (six blocks with Masked Multi-Head Self-Attention, Multi-Head Cross-Attention, and Feed-Forward Network layers), and Un-embedding & Softmax Layers. The rationale for design choices, such as six blocks per stack and eight heads per attention layer, is explained in terms of preventing under-generalization, overfitting, and balancing economics. Modern LLMs often use a decoder-only variant, and the article differentiates SLMs (~10B params), LLMs (10B-100B params), and Frontier Models (100B+ params).

Key takeaway

For AI Scientists and Machine Learning Engineers designing or optimizing LLMs, understanding the original Transformer's architectural rationale is crucial. You should consider the trade-offs between model complexity (e.g., number of encoder/decoder blocks, attention heads) and resource efficiency, as demonstrated by Google's 2017 design choices. This foundational knowledge helps you make informed decisions when adapting or extending Transformer variants, especially when balancing performance, cost, and the risk of under- or over-fitting.

Key insights

The Transformer architecture, detailed in "Attention is All You Need," forms the basis for modern LLMs through its encoder-decoder structure and attention mechanisms.

Principles

• Transformer design balances generalization, cost, and performance.
• Head count in attention layers impacts model capacity and resource use.
• Overfitting relates to model size and dataset, not head count.

Method

The Transformer processes text by tokenizing input, converting tokens to vector embeddings, passing them through encoder and decoder stacks with attention mechanisms, and finally un-embedding and softmax for human-readable output.

In practice

• Understand the original Transformer's full architecture.
• Recognize the role of tokenizers in LLM pipelines.
• Differentiate between SLM, LLM, and Frontier Model parameter counts.

Topics

• Transformer Architecture
• Large Language Models
• Attention Mechanism
• Encoder-Decoder Stacks
• Deep Learning Hierarchy
• Tokenization

Best for: AI Scientist, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by LLM on Medium.