Introduction to Transformer Architecture

In the rapidly evolving world of artificial intelligence, few innovations have been as transformative as the Transformer architecture. Introduced in the seminal 2017 paper “Attention is All You Need” by Vaswani et al., Transformers have become the backbone of virtually all state-of-the-art language models, including GPT-4, ChatGPT, and Google’s Bard.

But what exactly is a Transformer, and why has it revolutionized natural language processing? In this comprehensive guide, we’ll break down the Transformer architecture from the ground up, using clear explanations and visual diagrams to help you understand how these powerful models work.

Whether you’re a beginner just starting your AI journey or a practitioner looking to solidify your understanding, this guide will walk you through the core concepts that power today’s most advanced language models.

The Problem with the Past: RNNs and Feedforward Networks

Before Transformers, we had two main approaches for sequential data like text: Recurrent Neural Networks (RNNs) and simple Feedforward Networks (FFNs). Let’s understand why these approaches had limitations that needed to be addressed.

Feedforward Networks (FFNs)

Feedforward Networks are the most basic type of neural network. Data flows in one direction, from input to output, with no loops. They can’t remember past information, which makes them terrible for language, where context is everything.

Think about it: the meaning of a word often depends on the words that came before it. A basic FFN has no way to capture this.

  graph LR
    A["Input: The cat sat on"] --> B[FFN]
    B --> C["Output: ?"]

As you can see, a Feedforward Network processes the input and produces an output without any memory of previous inputs. This is problematic for language understanding.

Recurrent Neural Networks (RNNs)

RNNs were designed to address the memory problem by having a “memory” or hidden state that allows them to process a sequence one element at a time, feeding the output of one step back into the input of the next. This lets them “remember” context.

However, RNNs have significant limitations:

Sequential Processing: They process data one step at a time, making them slow to train
Vanishing Gradient Problem: It becomes difficult to learn long-range dependencies
Limited Parallelization: Due to their sequential nature, they can’t take full advantage of modern parallel computing hardware

  flowchart TB
    subgraph "RNN Processing Sequence"
        A["Input: The"] --> B[RNN]
        B --> C[Hidden State]
        C --> D[RNN]
        D --> E[Hidden State]
        E --> F[RNN]
        F --> G[Output]
        
        H["Input: cat"] --> D
        I["Input: sat"] --> F
    end

As shown above, RNNs process each word sequentially, with the hidden state carrying information from previous steps. While this provides some memory, it’s still limited and inefficient.

The Transformer Solution

The Transformer solves these problems by introducing two key innovations:

Attention Mechanism: Instead of processing sequences step-by-step, the model can look at all words simultaneously and determine which ones are most relevant to each other
Parallel Processing: Because words aren’t processed sequentially, the entire sequence can be processed in parallel, dramatically speeding up training

  flowchart LR
    subgraph "Transformer Approach"
        A["Input: The cat sat"] --> B[Attention]
        A --> C[Attention]
        A --> D[Attention]
        
        B --> E[Output]
        C --> E
        D --> E
    end

Understanding the Transformer Architecture

The Transformer architecture, introduced in the 2017 paper “Attention is All You Need,” completely changed the game. Instead of processing data sequentially, it processes the entire sequence at once, relying on a mechanism called self-attention to understand context.

The core idea is to let the model weigh the importance of all other words in a sentence when processing a single word. It can look at the entire sequence and decide which parts are most relevant to the word it’s currently considering.

The Original Transformer: Encoder-Decoder Architecture

The original Transformer model consists of two main parts:

Encoder: Processes the input sequence and creates representations of each word in context
Decoder: Uses the encoder’s representations to generate the output sequence

  flowchart LR
    subgraph "Original Transformer Architecture"
        A[Input Sequence] --> B[Encoder Stack]
        B --> C[Encoder-Decoder Attention]
        C --> D[Decoder Stack]
        D --> E[Output Sequence]
    end

However, modern large language models (LLMs) like GPT-4 and GPT-5 use a decoder-only architecture, so we’ll focus on that for the rest of this guide.

Decoder-Only Architecture

In a decoder-only Transformer (like GPT models), the architecture consists of a stack of identical layers. Each layer contains three key components:

Masked Multi-Head Attention: The heart of the Transformer that allows the model to focus on relevant parts of the input
Feed-Forward Network (FFN): A simple neural network that processes each word’s representation independently
Residual Connections and Layer Normalization: Technical components that help with stable training

  flowchart TB
    subgraph "Transformer Decoder Layer"
        A[Input Embeddings] --> B[Masked Multi-Head Attention]
        B --> C[Add & Norm]
        C --> D[Feed Forward Network]
        D --> E[Add & Norm]
        E --> F[Output]
    end

Multiple such layers are stacked together to form the complete model:

  flowchart TB
    subgraph "Stack of Decoder Layers"
        A[Input] --> B[Decoder Layer 1]
        B --> C[Decoder Layer 2]
        C --> D["..."]
        D --> E[Decoder Layer N]
        E --> F[Output]
    end

Understanding Input Processing: Embeddings

Before we dive into the layers and attention mechanisms, let’s understand how text is prepared for processing by the Transformer model.

2025 Update:
Most state-of-the-art models now use rotary positional embeddings (RoPE) or dynamic position encodings, which better capture relative positions and scale to longer contexts than the original sinusoidal method.

Token Embeddings

When you input text to a Transformer model, the first step is to convert each word (or subword) into a numerical representation called an embedding. Think of an embedding as a list of numbers that capture the meaning of a word.

For example, the word “cat” might be represented as a list like [0.2, -0.4, 0.7, …] with hundreds or thousands of numbers. Words with similar meanings will have similar embedding values.

Positional Embeddings

Since Transformers process all words simultaneously, they don’t naturally understand the order of words in a sentence. To solve this, the model adds positional embeddings - special numbers that represent where each word appears in the sequence.

This way, the model can distinguish between “The cat chased the dog” and “The dog chased the cat” even though they contain the same words.

  graph LR
    A["Input Text: The cat sat"] --> B[Tokenization]
    B --> C["Tokens: [The, cat, sat]"]
    C --> D[Token Embeddings]
    C --> E[Positional Embeddings]
    D --> F[Combined Embeddings]
    E --> F

The combined embeddings (token + positional) are what gets fed into the first layer of the Transformer.

Deep Dive into Attention Mechanism

The attention mechanism is the core innovation that makes Transformers so powerful. But what exactly is “attention” in the context of AI?

Think of attention like human focus. When you read a sentence, your brain doesn’t process all words with equal importance. Instead, you naturally focus more on certain words that are relevant to understanding the meaning.

For example, in the sentence “The cat sat on the mat”, when trying to understand what “sat” means, your brain pays more attention to “cat” (the one doing the action) than to “the” or “on”. This is exactly what the attention mechanism does in Transformers.

How Simple Attention Works

In a simple attention mechanism, each word can “attend to” or “look at” all other words in the sequence. The model calculates attention weights that determine how much focus to place on each word.

  flowchart LR
    subgraph "Simple Attention Mechanism"
        A["Word: sat"] --> B[Attention Weights Calculator]
        C["Word: The"] --> B
        D["Word: cat"] --> B
        E["Word: on"] --> B
        F["Word: the"] --> B
        G["Word: mat"] --> B
        
        B --> H[Attention Weights]
        H --> I["The: 0.1"]
        H --> J["cat: 0.7"]
        H --> K["sat: 0.05"]
        H --> L["on: 0.05"]
        H --> M["the: 0.05"]
        H --> N["mat: 0.05"]
        
        I --> O[Context-Aware Representation]
        J --> O
        K --> O
        L --> O
        M --> O
        N --> O
    end

In this example, when processing the word “sat”, the model assigns the highest attention weight (0.7) to “cat” because it’s the subject performing the action. The word “The” gets a smaller weight (0.1) because it’s less important for understanding the meaning.

The Magic of Self-Attention

What makes Transformers special is “self-attention”. This means each word can attend to all words in the sequence, including itself. This allows the model to build a rich understanding of context by considering relationships between all words simultaneously.

For instance, in the sentence “The chef cooked the books”, self-attention helps the model understand that “books” likely refers to accounting records (because of “cooked”) rather than literature, even though “books” usually means literature.

  graph LR
    A["chef"] -- relates to --> B[cooked]
    C["cooked"] -- relates to --> D[books]
    E["The"] -- modifies --> A
    F["the"] -- modifies --> D
    
    subgraph "Self-Attention Connections"
        A
        B
        C
        D
        E
        F
    end

Multi-Head Attention: Getting Multiple Perspectives

While simple attention is powerful, it only gives the model one way of looking at the relationships between words. Multi-head attention allows the model to look at these relationships from multiple perspectives simultaneously, just like how humans can interpret the same information in different ways.

Think of it like a team of experts analyzing a sentence:

One expert might focus on grammatical relationships
Another might focus on semantic meaning
A third might look for emotional context

Each “head” in multi-head attention learns to focus on different types of relationships, and their insights are combined to create a more complete understanding.

  flowchart TB
    subgraph "Multi-Head Attention"
        A["Input Sentence: The cat sat on the mat"] --> B[Attention Head 1]
        A --> C[Attention Head 2]
        A --> D[Attention Head 3]
        A --> E["..."]
        A --> F[Attention Head h]
        
        B --> G["Head 1 Focus: Grammar"]
        C --> H["Head 2 Focus: Meaning"]
        D --> I["Head 3 Focus: Position"]
        F --> J["Head h Focus: Other patterns"]
        
        G --> K[Concatenate]
        H --> K
        I --> K
        E --> K
        J --> K
        
        K --> L[Linear Transformation]
        L --> M[Combined Output]
    end

2025 Update:
Some models now use hybrid attention architectures (e.g., combining attention with state space models) to further improve efficiency and context length.

In practice, models like GPT typically use 12 or more attention heads. Each head develops its own specialization through training, and together they provide a much richer understanding of the text than a single attention mechanism could.

Why is it “Masked”?

This is the key difference for a generative model like GPT. During training, the model needs to learn to predict the next word. To prevent it from “cheating” and looking at the words that come after the current word, a mask is applied. This mask essentially hides future words in the sequence, ensuring that the model only uses the words it has already “seen” to make its prediction.

This is what makes GPT an autoregressive model—it generates text one word at a time, based on the words that came before.

  graph LR
    A["Input: The cat sat on ___"] --> B[Masked Attention]
    
    subgraph "What the Model Can See"
        C["The"] --> B
        D["cat"] --> B
        E["sat"] --> B
        F["on"] --> B
        G["???"] -->|Masked| B
    end
    
    B --> H["Prediction: the"]

So, in a nutshell, masked multi-head attention allows the model to consider the entire past context in a sophisticated, parallel way, without peeking at the future.

Understanding the Transformer Decoder Layer Components

Now that we understand attention, let’s look at how it fits into a complete decoder layer. Each decoder layer has several components that work together:

1. Masked Multi-Head Attention

This is the first component in each decoder layer. It applies the masked multi-head attention mechanism we discussed earlier to the input embeddings. The “masking” ensures that when predicting a word, the model can only look at previous words, not future ones.

2. Add & Norm (Residual Connection and Layer Normalization)

After the attention mechanism produces an output, the model applies “Add & Norm” which consists of two operations:

Residual Connection (Add): The input to the attention layer is added to its output. This helps with gradient flow during training and prevents the vanishing gradient problem.
Layer Normalization (Norm): Normalizes the values to keep them in a reasonable range, which helps with training stability.

  flowchart LR
    A[Input] --> B[Attention]
    A --> C[Residual Connection]
    B --> C
    C --> D[Layer Normalization]
    D --> E[Output]

3. Feed-Forward Network (FFN)

After the attention mechanism, the output goes through a Feed-Forward Network. This is a simple neural network that processes each position (word) independently. It consists of two linear transformations with a ReLU activation in between:

First linear transformation (expands the dimension)
ReLU activation function
Second linear transformation (compresses back to original dimension)

The FFN helps the model process the attention output further and adds more representational power.

  flowchart LR
    A[Input] --> B[Linear 1]
    B --> C[ReLU]
    C --> D[Linear 2]
    D --> E[Output]

4. Another Add & Norm

After the FFN, there’s another Add & Norm operation, similar to the one after attention. The input to the FFN is added to its output, and then layer normalization is applied.

Complete Decoder Layer Flow

Here’s how all components work together in a single decoder layer:

  flowchart TB
    subgraph "Complete Decoder Layer"
        A[Input Embeddings] --> B[Masked Multi-Head Attention]
        B --> C[Add & Norm]
        C --> D[Feed Forward Network]
        D --> E[Add & Norm]
        E --> F[Output]
    end

  flowchart LR
    subgraph "Residual Connections"
        G[Input] --> H[Attention Output]
        G --> I[Residual to Add & Norm 1]
        J[Add & Norm 1 Output] --> K[FFN Output]
        J --> L[Residual to Add & Norm 2]
    end

How Layers Work Together in Decoder-Only Architecture

In a decoder-only model like GPT, multiple decoder layers are stacked on top of each other. The output of one layer becomes the input to the next layer. This allows the model to build increasingly complex representations of the text as it goes through more layers.

  flowchart TB
    subgraph "Stack of Decoder Layers Processing"
        A[Input Embeddings] --> B[Layer 1]
        B --> C[Layer 2]
        C --> D["..."]
        D --> E[Layer N]
        E --> F[Final Representations]
    end

  flowchart TB
    subgraph "Inside Each Layer"
        G[Attention → Add & Norm → FFN → Add & Norm]
    end

Each layer refines the representations from the previous layer:

Early layers focus on basic syntax and word relationships
Middle layers understand more complex sentence structure
Later layers capture high-level meaning and context

This hierarchical processing is what allows Transformers to understand complex language patterns and generate coherent text.

Transformer Data Flow Visualization

To better understand how information flows through a Transformer, let’s look at a complete visualization of the data processing pipeline:

  flowchart TB
    subgraph "Complete Transformer Data Flow"
        A[Input Text] --> B[Tokenization]
        B --> C[Token Embeddings]
        C --> D[Positional Encoding]
        D --> E[Embedding Layer]
        E --> F[Decoder Layer 1]
        F --> G[Decoder Layer 2]
        G --> H["..."]
        H --> I[Decoder Layer N]
        I --> J[Output Probabilities]
        J --> K[Next Token Prediction]
    end
    
    subgraph "Inside a Decoder Layer"
        L[Masked Attention]
        L --> M[Add & Norm]
        M --> N[FFN]
        N --> O[Add & Norm]
    end

Anatomy of a Modern LLM: Parameter Calculation and Mixture of Experts (MoE)

A common misconception is that the self-attention mechanism holds the most parameters. In reality, the vast majority of parameters are in the Feed-Forward Networks (FFNs). This insight is key to understanding one of the most important recent innovations in LLM architecture: the Mixture of Experts (MoE).

2025 Update:
MoE is now standard in most frontier models (GPT-4.5, DeepSeek-V2, Gemini Ultra, etc.), enabling efficient scaling to hundreds of billions of parameters without proportional increases in inference cost.

Let’s demystify this with a concrete example inspired by a modern MoE model like DeepSeek-V2.

How Many Layers? It Depends!

First, it’s important to know that there’s no single answer for the number of layers in an LLM. It’s a key architectural choice that scales with the model’s size. For example:

Llama 3 8B has 32 layers.
Llama 3 70B has 80 layers.
DeepSeek-V2 has 60 layers.

More layers allow the model to build up more complex and abstract representations of the data.

Parameter Breakdown: A DeepSeek-V2-Inspired MoE Model

Let’s design a hypothetical MoE model to see where the parameters are.

Key Architectural Specs:

Total Layers (n_layers): 60
Hidden Dimension (d_model): 4096 (the main vector size)
Vocabulary Size (vocab_size): 100,000
MoE Layers: MoE is applied to the FFNs in every other layer (30 MoE layers in total).
Number of Experts (n_experts): 64 per MoE layer.
Active Experts (n_routed_experts): 6 (only 6 are used for any given token).

The model’s parameters are split into two main categories: Shared Parameters (used by every token) and Expert Parameters (used selectively).

1. Shared Parameters (The Backbone)

These are the parts of the model that are always active.

Token Embeddings: Converts input tokens to vectors.
- vocab_size * d_model = 100,000 * 4096 = 410 Million
Attention Blocks: Every layer has a self-attention mechanism. We’ll use Grouped-Query Attention (GQA) for realism, which has separate projections for Query (Q), Key (K), and Value (V).
- How is “Attention parameters per layer ≈ 42 Million” calculated?
  - In a standard multi-head attention, the parameter count is roughly 4 * d_model^2 (for Q, K, V, and output projections). For d_model = 4096, that’s 4 * 4096^2 = 67M.
  - However, modern models use optimizations like GQA, multi-query attention, shared projections, or reduced head sizes, which typically bring this down to about 42M per layer or even lower in practice (sometimes <30M).
  - Architect’s note: The exact number depends on implementation details (e.g., grouped-query, parameter sharing, or low-rank projections).
- Total for all 60 layers: 60 * 42M = 2.5 Billion
Total Shared Parameters: 0.41B (Embeddings) + 2.5B (Attention) = ~2.9 Billion

These ~3B parameters form the model’s backbone, handling the sequence processing and context-gathering for every token.

2. Expert Parameters (The Knowledge Store)

This is where the MoE architecture comes into play. Instead of one FFN per layer, we have many.

FFN / Expert Size: Each expert is a standard Feed-Forward Network.
- How is “Parameters per expert ≈ 117 Million” calculated?
  - Each FFN/expert has two weight matrices: input-to-hidden and hidden-to-output.
  - Formula: 2 * d_model * ffn_dim. If ffn_dim = 3.5 * d_model, then 2 * 4096 * (3.5 * 4096) = 117M.
  - If ffn_dim = 4 * d_model, the count would be 134M—the actual multiplier is a design choice.
  - Architect’s note: The FFN (expert) is the main parameter consumer in large LLMs.
Experts per MoE Layer: We have 64 of these experts.
- 64 experts * 117M params/expert = 7.5 Billion parameters in just one MoE layer’s FFN block!
Total Expert Parameters: We have 30 MoE layers in our model.
- 30 layers * 7.5B params/layer = 225 Billion

  graph TD
    subgraph "MoE Transformer Layer"
        A[Input from Attention] --> B{Router};
        B -- "Chooses 6" --> C[Expert 1];
        B --> D[Expert 2];
        B --> E[...];
        B --> F[Expert 64];
        
        subgraph "Selected Experts for one Token"
            C --> G((Combine Outputs));
            D -- "e.g." --> G;
            style D fill:#f9f,stroke:#333,stroke-width:2px
            style C fill:#f9f,stroke:#333,stroke-width:2px
            style H fill:#f9f,stroke:#333,stroke-width:2px
            style I fill:#f9f,stroke:#333,stroke-width:2px
            style J fill:#f9f,stroke:#333,stroke-width:2px
            style K fill:#f9f,stroke:#333,stroke-width:2px
            H("Expert 12") --> G;
            I("Expert 28") --> G;
            J("Expert 45") --> G;
            K("Expert 51") --> G;
        end

        G --> L[Output of Layer];
    end

Summary for architects:

The majority of parameters in a modern LLM are in the FFN/experts, not attention.
Attention parameter count per layer is implementation-dependent (67M naive, ~42M with optimizations, sometimes lower with MQA/GQA).
FFN/expert parameter count is set by 2 * d_model * ffn_dim (e.g., 117M for 3.5× width).
Design choices (like GQA, expert width, and number of experts) have a direct, order-of-magnitude impact on model size and compute.
2025 Note: Inference-time optimizations like speculative decoding, quantization, and efficient batching are now standard for production LLMs.

Transformer Architecture Evolution

Since the original Transformer was introduced in 2017, there have been many improvements and variations:

2025 Update:
Recent research explores state space models (SSMs), hybrid attention/state space layers, and dynamic routing for even longer context and better efficiency. Some models now mix attention and SSM blocks within the same architecture.

  timeline
    title Evolution of Transformer Architectures
    section 2017
    Attention is All You Need (Original Transformer)
    section 2018
    GPT-1: Decoder-only architecture
    BERT: Encoder-only architecture
    section 2019
    GPT-2: Larger and more capable
    section 2020
    GPT-3: Massive scale, early use of rotary embeddings
    section 2021-2022
    GPT-3.5: Instruction tuning, efficient attention (GQA/MQA)
    section 2023
    GPT-4 & MoE: Multimodal, MoE mainstream, longer context
    section 2024
    DeepSeek-V2, Gemini Ultra: MoE everywhere, grouped-query attention, 128k+ context, rotary/dynamic position encoding
    section 2025
    State Space Models (SSMs), Hybrid Attention/SSM Layers, Dynamic Routing, Efficient Inference (speculative decoding, quantization)

Summary: The Big Picture

The Transformer architecture, with its reliance on the attention mechanism, has solved the key limitations of previous models by allowing for parallel processing and a superior ability to capture long-range dependencies.

Transformer vs. RNN

Aspect	RNN	Transformer
Processing	Sequential	Parallel
Speed	Slow	Fast
Long-range dependencies	Difficult	Easy
Training	Time-consuming	Efficient

The Transformer’s parallel processing makes it much faster and more efficient to train than the sequential RNN. The attention mechanism also helps it overcome the vanishing gradient problem, enabling it to handle much longer sequences of text effectively.

Transformer vs. FFN

While both use feed-forward networks, the Transformer’s self-attention layers provide the crucial context-awareness that FFNs completely lack.

  flowchart LR
    subgraph "Comparison"
        A["FFN: No Context"] --> B[Simple Output]
        C["Transformer: Full Context"] --> D[Context-Aware Output]
    end

Conclusion

Models like GPT-4 and the upcoming GPT-5 are simply massive, highly-tuned versions of this same basic architecture, proving that the Transformer is a truly scalable and powerful foundation for the future of AI.

2025 Perspective:
The Transformer remains the backbone of LLMs, but the field is rapidly evolving. Expect to see more hybrid models, longer context windows, and smarter routing mechanisms in the next generation of AI systems.

The key innovations that make Transformers so powerful are:

Self-Attention: The ability to weigh the importance of different words in a sequence
Multi-Head Attention: Using multiple attention mechanisms to capture different types of relationships
Parallel Processing: Processing all words simultaneously rather than sequentially
Masking: Ensuring that generative models don’t “cheat” by looking at future words
Deep Stacked Architecture: Building complex understanding through many layers
Embeddings: Converting text to numerical representations that capture meaning
Feed-Forward Networks: Processing each position independently to add representational power
Add & Norm: Ensuring stable training through residual connections and normalization

Understanding these concepts is crucial for anyone interested in modern AI and natural language processing. With this foundation, you can begin to explore more advanced topics like fine-tuning, prompt engineering, and even building your own Transformer models.

I hope this has provided a clear, accessible introduction to this fascinating topic!

For more detail on the masked multi-head attention mechanism and how it’s used in models like GPT, you can watch this video.

Understanding Transformer Architecture: The Foundation of Modern AI

A Comprehensive Guide to Attention Mechanisms and How They Power LLMs Like GPT