What architecture is commonly used in Large Language Models?

The architecture most commonly used in Large Language Models is the Transformer architecture.

Transformers became the foundation of modern language models because they can process token relationships efficiently using attention mechanisms rather than relying on recurrent processing.

What are the main parts of a Transformer?

A Transformer-based language model is built from repeated layers containing components such as:

• Self-attention — lets tokens use information from other relevant tokens in the sequence.
• Feed-forward networks — transform the representation of each token after attention.
• Residual connections — help information flow through many layers.
• Normalization layers — help stabilize training.
• Positional information — tells the model where tokens appear in the sequence.

Conceptually:

Tokens
  ↓
Embeddings + positional information
  ↓
Transformer layers
  ↓
Contextual token representations
  ↓
Output prediction

What Transformer architecture do LLMs use?

There are three common Transformer arrangements:

• Encoder-only
• Decoder-only
• Encoder-decoder

Many modern generative LLMs use a decoder-only Transformer because this architecture is naturally suited to predicting the next token repeatedly.

Encoder-only and encoder-decoder architectures are also important, but they are commonly used for somewhat different language-modeling tasks.

These architectures are covered separately in:

• “What is a decoder-only LLM?”
• “What is an encoder-only language model?”
• “What is an encoder-decoder language model?”

Why are Transformers well suited to LLMs?

Transformers are useful because they can:

• Model relationships between tokens across a sequence
• Scale to very large parameter counts
• Train efficiently on modern parallel hardware
• Work with long sequences
• Learn rich contextual representations
• Support autoregressive text generation

Their ability to scale with large datasets and large amounts of compute is one reason they became the dominant architecture for LLMs.

Is every LLM exactly the same Transformer?

No.

Models can differ in:

• Attention design
• Number of layers
• Hidden dimensions
• Positional representation
• Feed-forward structure
• Normalization strategy
• Context-window design
• Dense vs. mixture-of-experts layers

So two LLMs can both be Transformer-based while having very different internal designs.

Are Transformers the only possible architecture?

No.

Researchers continue to explore alternatives and hybrid architectures that aim to reduce memory or computation, especially for long sequences.

However, the Transformer remains the standard architecture behind most widely used LLMs.

In short: Large Language Models are most commonly built using the Transformer architecture. Transformers combine self-attention, feed-forward layers, positional information, residual connections, and normalization to learn contextual representations and generate language effectively.