--- title: "Transformer Architecture in Large Language Models (LLMs)" description: "This blog will give the knowledge about What is the transformer architecture and why its needed." author: "Yash Srivastava" published: 2026-06-16 updated: 2026-06-17 canonical: https://answers.mindstick.com/blog/408/transformer-architecture-in-large-language-models-llms category: "technology" tags: ["llm", "ai model", "transformers"] reading_time: 4 minutes --- # Transformer Architecture in Large Language Models (LLMs) ## Introduction Modern models like ChatGPT, Llama, Gemini, Claude and Mistral are based on a common architecture called [Transformer Architecture](https://www.mindstick.com/forum/162056/how-does-the-transformer-architecture-work). Transformers are also know as the important [architecture in the world](https://yourviews.mindstick.com/view/86458/the-history-and-the-future-of-architecture-in-the-world) of modern AI and NLP. Mostly popular LLMs use this architecture. Before the Transformers NLP system mainly were based on [Recurrent Neural](https://www.mindstick.com/forum/162054/what-is-a-recurrent-neural-network-rnn) Network (RNN), Long Short-Term Memory (LSTM) and Encoder- Decoder Architecture. These model were capable of processing the sequential data but these models have some limitations. These model process the words one by one, It is difficult to remember long context and difficulty to find relationship between distant words. In 2017, Google researchers publish a famous [research paper](https://answers.mindstick.com/qa/102944/how-to-write-a-research-paper) Attention Is All You Need". This research paper gives the concept of Transformer Architecture for the first time. Transformer completely removed the requirement of RNN and LSTM and used [Attention Mechanism](https://answers.mindstick.com/blog/406/attention-mechanism-in-llms) to understand the language. With the help of attention Mechanism model become capable to find the relationship between the words in a sentence and can capture the context in better way. So the Transformer Architecture was the revolutionary invention in the field of NLP. Many powerful models are also using Transformer Architecture: - ChatGPT - LLaMA - Gemini - Claude - BERT - T5 ## Why Transformers Were Needed Before Transformers, models relied on sequential processing. *Example:* ```plaintext I love learning Artificial Intelligence ``` *An LSTM processes:* ```plaintext I ↓ love ↓ learning ↓ Artificial ↓ Intelligence ``` One word at a time. ## Problems: - Sequential Processing - Limited Memory - Slow Training - Vanishing Gradient Problem ## Researchers needed a model that could: - Process words in parallel - Understand long context - Scale to huge datasets - Train efficiently on GPUs The solution was the Transformer. ## High Level Transformer Flow A Transformer processes text through multiple stages. ```plaintext Input Text ↓ Tokenization ↓ Token Embeddings ↓ Positional Encoding ↓ Multi-Head Attention ↓ Add & Norm ↓ Feed Forward Network ↓ Add & Norm ↓ Output Representation ``` Every Transformer block follows this structure. ## Step 1: Tokenization Computers cannot understand raw text. *Example:* ```plaintext I love AI ``` Tokenizer converts text into tokens. ```plaintext ["I", "love", "AI"] ``` Then token IDs: ```plaintext [12, 532, 981] ``` These IDs are simply integers but the model still does not understand their meaning. ## Step 2: Token Embeddings Token IDs are passed into the Embedding Layer. *Example:* ```plaintext 12 ↓ [0.52, -0.14, 0.89, ...] ``` Each token becomes a dense vector. *Example:* ```plaintext dog → [0.3, 0.9, ...] cat → [0.31, 0.88, ...] car → [-0.8, 0.2, ...] ``` Similar words become closer in vector space and embeddings provide semantic meaning to tokens. ## Step 3: Positional Encoding Transformers process all tokens simultaneously. *Problem:* ```plaintext Dog bites man ``` and ```plaintext Man bites dog ``` contain the same words. Without positional information, both sentences would appear identical so to [solve this problem](https://answers.mindstick.com/qa/95669/my-website-appears-different-in-firefox-and-different-in-chrome-how-do-i-solve-this-problem), positional information is added. ```plaintext Final Embedding = Token Embedding + Positional Encoding ``` Now the model knows the position of every token. ## Step 4: Multi-Head Attention This is the heart of the Transformer. Suppose we have: ```plaintext The capital of France is Paris. ``` The word Paris is strongly related to: ```plaintext France capital ``` Attention identifies these relationships and Multi-Head Attention uses multiple attention mechanisms simultaneously. Example: ```plaintext Head 1 → Grammar Head 2 → Semantic Meaning Head 3 → Long Distance Dependency Head 4 → Entity Relationships ``` Different heads learn different patterns which will gives a richer understanding of language. ## Step 5: Residual Connections As Transformers become deeper, training becomes difficult and Information may lost now to solve this problem, Residual Connections are used. *Flow:* ```plaintext Input ↓ Layer ↓ Output ``` Instead of only using: ```plaintext Output ``` Transformer uses: ```plaintext Output + Original Input ``` Benefits: - Better gradient flow - Stable training - Deeper networks ## Step 6: Layer Normalization [Neural network](https://www.mindstick.com/blog/303767/how-neural-networks-and-new-antibiotics-are-connected) values can become unstable during training, some values can become very large and some become very small. here the Layer Normalization stabilizes the activations. Benefits: - Faster convergence - Stable gradients - Better training performance This is why Transformer blocks use: ```plaintext Add & Norm ``` after major operations. ## Step 7: Feed Forward Network (FFN) Attention gathers information but gathered information must be processed. This is the job of FFN. *Simple flow:* ```plaintext Input ↓ Linear Layer ↓ Activation Function ↓ Linear Layer ↓ Output ``` Example: ```plaintext Attention ↓ Collect Information FFN ↓ Process Information ``` ## Complete Transformer Block Now combine everything. ```plaintext Input ↓ Multi-Head Attention ↓ Add & Norm ↓ Feed Forward Network ↓ Add & Norm ↓ Output ``` This is called: ```plaintext Transformer Block ``` Modern LLMs contain many Transformer Blocks. --- Original Source: https://answers.mindstick.com/blog/408/transformer-architecture-in-large-language-models-llms Copyright © MindStick Software Pvt. Ltd. This Markdown version is provided for developers, AI systems, and offline reading.