Types of LLM Architectures

Let's first break it down - what exactly are large language models (LLMs), why do we call them "large," and how are they different from other types of language models?

An LLM is a machine learning model trained on massive amounts of text using transformer-based architectures (or their variations). These models can generate, understand, and process human-like text, making them useful for tasks like translation, summarization, reasoning, coding etc,.

How is an LLM different from other language models?

Scale – Traditional language models (e.g., n-gram models, early RNNs) were trained on much smaller datasets with far fewer parameters. LLMs, on the other hand, have billions or even trillions of parameters.

Generalization – Older models were usually task-specific, while LLMs are general-purpose, capable of handling a wide range of tasks with zero-shot or few-shot learning.

Memory and Context Length – LLMs use attention mechanisms to capture long-range dependencies, whereas older models (e.g., LSTMs) struggled with understanding long contexts.

Training Methodology – LLMs are trained using self-supervised learning on massive datasets, followed by fine-tuning and reinforcement learning (e.g., RLHF) to boost performance (but not necessarily always).

Why do we call them "Large"?

Parameter Size – LLMs typically have billions to trillions of parameters (e.g., GPT-4 has over 1 trillion parameters, Grok-3 has 2.7 trillion parameters).

Training Data – They are trained on massive, diverse text datasets covering a broad range of topics from across the internet.

Computational Scale – Training an LLM requires thousands of GPUs/TPUs running for weeks or even months.

Now, as I’m writing this blog, there are several different architectures for LLMs. When classifying LLMs by distinct architectures, we focus on the core structural designs that define how they process and generate language. Here are the main types

Transformer Architecture

Description: The transformer architecture is the backbone of most modern LLMs. It was introduced in the 2017 paper "Attention Is All You Need." At its core is self-attention—a mechanism that lets the model figure out how important each word is in relation to others, no matter where they appear in a sentence. This was a huge step up from older models that processed text sequentially (word by word), because transformers can analyze entire sequences in parallel. That makes them faster and more efficient.

The architecture is built from stacked layers, each containing:

• Multi-head attention, which looks at different aspects of the input at the same time.
• Feed-forward neural networks, which further process the data.

Transformers generally come in three main types:

• Encoder-Only: These models process the entire input bidirectionally—looking at both past and future words in a sentence. That makes them great for understanding text or classifying sentiment (e.g., BERT).

• Decoder-Only: These work unidirectionally, meaning they predict the next word based only on what came before. This makes them ideal for generating text step by step (e.g., GPT-3).

• Encoder-Decoder: A hybrid approach where the encoder analyzes the input (like a sentence in one language), and the decoder generates an output (like a translation). This setup is perfect for tasks like translation or summarization (e.g., T5).

Key Trait: Highly parallelizable, excels at capturing long-range dependencies via attention.

Examples: GPT, BERT, T5.

Mixture of Experts (MoE) Architecture

Description: The Mixture of Experts (MoE) approach is like having a team of specialists instead of a single jack-of-all-trades. Built on top of transformer layers, it breaks the model into multiple smaller sub-networks, or “experts,” each specializing in different types of inputs or tasks.

A smart gating mechanism acts like a manager, deciding which experts to call on for a given piece of text—one expert might handle technical jargon, while another focuses on casual conversation. But here’s the trick: only a handful of experts activate at any given time, so the model doesn’t waste energy firing up all its billions of parameters at once. This sparse activation makes MoE models insanely efficient, especially as they scale to trillions of parameters.

The result? Faster processing and lower computational costs without sacrificing performance. While MoE isn’t as widely known as pure transformers, it’s gaining traction for its ability to balance power and efficiency—especially in massive models deployed for real-world applications.

Key Trait: Sparse activation — only a subset of parameters is used per task, boosting efficiency at scale.

Examples: Mixtral, Switch Transformers

Convolutional Neural Network (CNN) Architecture

Description: Convolutional Neural Networks (CNNs) are best known for image recognition, but they’ve also had a role in language processing—though they’re not as dominant in today’s LLM-driven world. In NLP, CNNs work by sliding small filters over a sequence of text. Think of these filters as magnifying glasses that zoom in on short chunks of words or token embeddings (numerical representations of words). They detect local patterns—like phrases or word combinations—and stack them into higher-level features.

Unlike transformers, which can take in an entire sentence at once, CNNs have a fixed-size context window based on the filter size. That means they’re great at spotting nearby relationships but struggle with long-range dependencies, like connecting ideas across paragraphs. Historically, CNNs were used for tasks like text classification (e.g., spotting spam emails), but they’ve mostly been overshadowed by transformers in large-scale NLP.

That said, hybrid models like ConvBERT show that CNNs can still play a role—especially in scenarios where speed matters more than deep contextual understanding.

Key Trait: Fixed-size context windows, computationally efficient but less common in modern LLMs due to limited long-range dependency modeling.

Examples: TextCNN (used for classification), ConvBERT (a hybrid with transformers); rarely seen as standalone full-scale LLMs.

Retrieval-Augmented Architecture

Description: Retrieval-Augmented architectures are like LLMs with a built-in research assistant. Instead of relying solely on what they’ve memorized during training, these models can tap into external knowledge sources—databases, web pages, or structured knowledge graphs to pull in fresh or specialized information.

Here’s how it works: The model combines a traditional language generator (usually a transformer) with a retrieval module that searches for relevant facts or documents based on the input. So if you ask about a recent event, instead of guessing from outdated training data, it might fetch info from a news archive. The retrieved data then gets woven into the model’s response, making it way more accurate for fact-heavy or domain-specific tasks; think legal research or medical Q&A.

This setup is super useful when a model’s built-in knowledge (everything it learned during training) isn’t enough. Say you ask about something brand new or really specific, like a news event from last week. The model might not have the answer just from memory. That’s where the retrieval part kicks in—it can pull in fresh details from outside sources, like a library or the internet. But this also makes things a bit trickier because the model has to mix what it already knows with what it just found, kind of like a chef adding a new ingredient to their signature dish.

When it works, though, the results are spot-on. Systems like RAG (Retrieval-Augmented Generation) are great examples—they combine language skills with the ability to dig up the latest and most accurate info, making them perfect for tasks where staying precise and up-to-date really matters.

Key Trait: Augments model capabilities with external data, useful for tasks requiring structured or factual accuracy.

Examples: RAG (Retrieval-Augmented Generation), REALM.