Most conversations about AI models treat them like they are fundamentally the same thing. Different names, different logos, but identical under the hood. That assumption is wrong, and it costs people more than they realize, in compute bills, mismatched tools, and systems that underperform for the job they were hired to do.

The way a model is built determines what it is good at, where it struggles, and whether it can handle a given task at scale. Those are not small details. For anyone evaluating models for a real use case, they tend to be the whole conversation.

What follows is a walkthrough of the major LLM architectures in use today, what makes each one different, and why it matters even if you never plan to train a model yourself. No research background required.

What is the Transformer Architecture and Why Do Most AI Models Still Use It?

Almost every large language model you’ve heard of — GPT-5, Claude, Gemini, Llama 4 — is built on the transformer architecture. It was introduced in 2017, and the core idea hasn’t changed: the model looks at all the words in a passage at the same time and figures out which ones matter most to each other. That’s the attention mechanism.

Attention is powerful because language is full of long-distance relationships. A pronoun in paragraph three might refer to a name in paragraph one. Sarcasm flips the meaning of an entire sentence based on context. Attention handles all of this by letting every token “see” every other token.

But there’s a catch. Attention gets expensive fast. The cost grows with the square of the input length. Double the text, quadruple the compute. That’s why, until recently, most models had relatively short context windows.

And it’s why a lot of the innovation in the past two years has been about making attention cheaper without losing what makes it good.

Decoder, Encoder, and Encoder-Decoder: The Three Types of Transformer Models

Not all transformers work the same way. There are three main variants, and each has a different job.

• Decoder-only: models generate text left to right — each word can only look at the words before it. This is what GPT, Claude, Llama, and most chatbots use. It turns out this simple setup is extremely flexible. You can make it do classification, translation, coding, reasoning — pretty much anything — just by changing the prompt. That versatility is why decoder-only won the scaling race.
• Encoder-only: models look at text in both directions at once, which gives them a richer understanding of context. BERT is the famous example. These models can’t generate text, but they’re incredibly fast at tasks like classification, search ranking, and content filtering. BERT is still one of the most downloaded models on the planet because for a lot of production workloads, it’s 20 times faster than a big generative model and just as accurate.
• Encoder-decoder: models combine both approaches — a bidirectional encoder feeds into a generative decoder. Google’s T5 is the well-known example. These work well for structured tasks like translation and summarization, but the field has largely moved on because decoder-only models turned out to be simpler to scale and surprisingly good at everything.

Mixture of Experts: The Biggest Shift in How Models Scale

If I had to name the single most important architectural trend right now, it’s Mixture of Experts. Almost every major model released in the past year uses it: DeepSeek-V3, Llama 4, Mistral, Qwen3. It’s become the default.

The idea is straightforward. Instead of one massive neural network that fires every parameter on every input, you split the network into many smaller “expert” sub-networks. A learned router looks at each piece of input and decides which experts are most relevant. Only those experts activate. The rest sit idle.

The result is a model that has enormous total capacity but only uses a fraction of it on any given input. DeepSeek-V3, for example, has 671 billion parameters total but only activates about 37 billion per token. That’s roughly 5% of the model working at any given moment. This is how they trained a frontier-quality model for a fraction of what it would normally cost.

We’ve seen MoE models perform as well as dense models that cost five to ten times more to run. For clients watching their inference bills, that difference is real.

The tradeoff? All those parameters still need to live in memory, even if most aren’t active. So MoE models are cheaper per query but need bigger machines to host. And the routing mechanism can be finicky — if experts aren’t balanced well, some get overloaded while others waste capacity.

State Space Models: A Different Way to Read

Transformers read an entire document at once and compare everything to everything. State space models take a fundamentally different approach. They process text sequentially — one token at a time — maintaining a compressed running summary of what they’ve seen so far.

The practical payoff is that their cost grows linearly with input length instead of quadratically. For very long documents, this is a massive difference. A model called Mamba showed that a 3-billion-parameter SSM could match transformers twice its size on language tasks while running at five times the speed on long sequences.

We’ve experimented with SSM-based models on projects that involve processing large volumes of text — legal documents, research archives, long conversation histories. The speed advantage is real. But there’s a limitation: because SSMs compress everything into a fixed-size summary, they can struggle with tasks that require looking up a specific detail from earlier in the text.

Attention excels at that kind of precise recall. SSMs are more like someone who read the whole book but can’t always find the exact page you’re asking about.

RWKV: The RNN That Trains Like a Transformer

There’s a community-driven project called RWKV that took a different path entirely. It’s a recurrent neural network — the architecture that everyone assumed transformers had made obsolete. But RWKV figured out how to train an RNN with the same parallelism tricks that make transformers fast to train, while keeping the RNN’s advantage of constant-cost inference.

The latest version runs with zero attention, processes tokens at constant cost regardless of how long the input is, and has been shipping on over a billion Windows devices for energy-efficient AI features. For edge deployment and low-power environments, this kind of architecture is compelling.

It’s not going to replace frontier models for complex reasoning anytime soon. But it shows that the transformer isn’t the only viable path, and for certain deployment constraints, alternatives already work.

RAG: The Architecture That Isn’t Really an Architecture

Retrieval-Augmented Generation deserves a mention because it comes up in almost every client conversation, and people often confuse it with a model architecture. It’s not. It’s a system design pattern.

Instead of asking the model to know everything, you give it access to a search tool. When a question comes in, the system retrieves relevant documents from a database and feeds them into the model’s context alongside the question. The model generates its answer based on that retrieved information.

RAG doesn’t change how the model itself is built. But it changes what the model can do. It gives any LLM access to up-to-date information, company-specific data, and domain knowledge that wasn’t in the training data.

For most business use cases, a well-built RAG pipeline matters more than which model you pick.

LLM Architecture Comparison: Speed, Memory, and Best Use Cases

Here’s a simplified comparison of the major architecture families and what they’re best suited for.

Why This Matters If You’re Not Building Models

You don’t need to understand attention mechanisms to use AI well. But understanding that different architectures exist — and that they have different strengths — changes the conversations you have with your technical team.

When someone says “we should use a bigger model,” you can ask whether a MoE model would give them more capacity without the compute bill. When your legal team wants to process thousands of long contracts, you can ask whether an SSM-based or hybrid model would handle that more efficiently than throwing a giant transformer at it.

We’ve had clients save significant money just by switching from a dense model to an MoE variant for the same task. Not because the MoE was smarter, but because it was smart enough and much cheaper to run.

The model you choose isn’t just about intelligence. It’s about fit. And understanding architecture is how you find the right fit.

About the Author

Sebastian Mondragon is the CEO of Particula Tech, where he leads AI development, consulting, and research initiatives. His work spans building custom AI solutions for clients, advising organizations on AI strategy and implementation, and conducting research on the technical and institutional challenges of deploying increasingly capable systems.

Through Particula Tech, Sebastian works with companies at different stages of AI adoption, from initial strategy to full-scale implementation, helping them make informed decisions about what to build, how to build it, and when deployment actually makes sense.

Origianl Creator: Sebastian Mondragon
Original Link: https://justainews.com/ai-compliance/ai-research/llm-architecture-explained/
Originally Posted: Sat, 21 Mar 2026 12:49:56 +0000