What Are Transformer Blocks in LLMs?

At the core of modern large language models (LLMs) such as ChatGPT, Claude, Gemini, and LLaMA is a powerful neural architecture known as the Transformer. Introduced by Vaswani et al. in the 2017 paper Attention Is All You Need, the Transformer architecture fundamentally changed the landscape of natural language processing by enabling models to learn dependencies between words across entire sequences, without relying on recurrence or convolution.

A Transformer block is the fundamental building unit of an LLM. It is a modular layer that processes token embeddings through a combination of core components:

• Multi-head self-attention, which allows the model to focus on relevant parts of the input sequence when interpreting each token.
• Feed-forward networks (FFNs), which apply learned transformations to each token representation independently.
• Residual connections, which help preserve useful information and improve gradient flow during training.
• Layer normalization, which stabilizes training by normalizing inputs across layers.

In large-scale models, multiple Transformer blocks are stacked sequentially. Each block incrementally refines the representation of tokens, enabling the model to capture increasingly abstract and contextualized meanings. This layered processing forms the basis for sophisticated language understanding and generation in today’s LLMs.

Transformer Block Architecture

The diagram below illustrates the internal structure of a Transformer block as used in large language models (LLMs). Each component plays a critical role in processing token embeddings and enabling the model to understand language contextually.

Figure: Detailed Transformer block showing embedding, attention, and feed-forward layers.

Token Input

The input to a Transformer block begins with a sequence of tokens. These tokens represent individual words or subword units derived from the original text. Each token is assigned a unique identifier through a tokenizer. This sequence of token IDs forms the raw input that is subsequently processed by the model.

Token Embedding

Since neural networks operate on numerical data, each token ID is mapped to a high-dimensional vector using an embedding layer. This token embedding captures semantic information about each word. For example, words with similar meanings tend to have embeddings that are close to each other in vector space. These embeddings serve as the model’s internal representation of the textual input.

Positional Encoding

Transformers do not inherently understand the order of tokens in a sequence. To address this, positional encoding vectors are added to the token embeddings. These encodings allow the model to capture the relative and absolute position of each word in the sentence. The result is a combined vector that includes both the identity of the word and its position in the input.

Multi-Head Self-Attention

This is the core innovation of the Transformer architecture. The self-attention mechanism enables each token to examine and weigh the importance of every other token in the sequence, including itself. The multi-head design allows the model to capture different types of relationships simultaneously. Each head focuses on different patterns or dependencies in the text, which are later combined to form a comprehensive representation.

Add and Normalize (Post-Attention)

After the self-attention step, the model adds the original input (via a residual connection) to the attention output. This helps preserve important information from earlier layers. The result is then passed through a layer normalization step, which standardizes the input distribution for the next layer and accelerates convergence during training.

Feed-Forward Network

Each token’s representation is independently transformed by a small neural network composed of two fully connected layers with a non-linear activation function between them. This component introduces non-linearity and additional learning capacity, enabling the model to represent more complex transformations of the token data.

Add and Normalize (Post-Feed-Forward)

Similar to the attention output, the result of the feed-forward network is added to its input using another residual connection. This sum is again normalized using layer normalization. These repeated normalization and residual steps make it possible to train very deep networks without losing stability.

Output Tokens

After passing through all the internal layers of the Transformer block, the token representations are significantly enriched. These output vectors can either be passed into the next Transformer block in a stacked architecture or directly used for prediction, depending on the depth and purpose of the model.

Conclusion

Transformer blocks form the foundation of modern large language models, enabling them to process, understand, and generate natural language with remarkable fluency. By combining components such as self-attention, feed-forward networks, residual connections, and normalization, each Transformer block incrementally enhances the representation of textual input.

As these blocks are stacked and scaled across multiple layers, the model gains a powerful ability to model language patterns, contextual meaning, and long-range dependencies. Understanding how each part of the Transformer block contributes to this process is essential for anyone looking to explore, build, or optimize large language models.

Whether you are a researcher, engineer, or enthusiast, grasping the mechanics of Transformer blocks provides valuable insight into the design principles behind some of the most advanced AI systems in use today.