The transformer is a neural network architecture, introduced in 2017, that processes an entire sequence of tokens in parallel using a mechanism called self-attention to weigh how much each token should influence every other token. It replaced the sequential recurrent networks that came before it and is the foundation of every modern large language model, from GPT to Claude to Llama.
Key takeaways
- The transformer’s breakthrough is self-attention: it lets every token look at every other token to build context, rather than reading strictly left to right.
- It processes sequences in parallel, which is why transformers train efficiently on GPUs and scale to hundreds of billions of parameters.
- Modern LLMs are mostly decoder-only transformers stacked dozens of layers deep, each refining the representation of the sequence.
- Understanding the architecture explains real deployment tradeoffs — context length, memory use, and inference cost all trace back to how attention works.
The transformer, defined
The transformer architecture was introduced in the 2017 paper “Attention Is All You Need.” Its central idea is deceptively simple: to understand a word, look at all the other words around it and decide how much each one matters. That weighing operation is called attention, and by stacking many attention layers the model builds an increasingly rich, contextual representation of the input.
Before transformers, the dominant approach was recurrent neural networks (RNNs), which read a sequence one token at a time and carried a hidden “memory” forward. That sequential design made them slow to train and prone to forgetting information from early in a long sequence. The transformer removed recurrence entirely, processing the whole sequence at once and letting attention connect distant tokens directly — the change that made today’s large models possible.
The core building blocks
A transformer layer has two main sub-parts. The first is multi-head self-attention, which computes, for every token, a weighted blend of all other tokens — running several attention “heads” in parallel so the model can track different kinds of relationships (syntax, reference, topic) at once. The second is a feed-forward network, a small per-token neural network that transforms each token’s representation. Around both sit residual connections and layer normalization that keep training stable.
Because attention has no inherent sense of order, transformers add positional encodings so the model knows a token’s place in the sequence. A full model stacks dozens of these layers; the number of layers, the width of each layer, and the number of attention heads are the main knobs that set a model’s parameter count and capability.
Encoders, decoders, and decoder-only models
The original transformer had two halves. An encoder reads the input and builds a representation of it; a decoder generates output one token at a time, attending both to what it has produced so far and to the encoder’s representation. This encoder–decoder design suits translation, where you map one full sequence to another.
Most generative LLMs today are decoder-only: they drop the encoder and simply predict the next token given everything before it, using “causal” attention that prevents a token from peeking at future tokens. Encoder-only models like BERT, by contrast, are optimized for understanding tasks such as classification and embeddings rather than open-ended generation. Which design a model uses tells you what it is built to do.
Why the architecture matters for deployment
The architecture is not just academic — it dictates the practical limits you plan around. Self-attention compares every token to every other token, so its cost grows with the square of the sequence length. That is why a longer context window is expensive in both memory and compute, and why efficient-attention variants and caching matter so much for production inference.
For an enterprise choosing where and how to run models, these mechanics translate directly into GPU memory sizing, latency budgets, and cost. Knowing that attention is the bottleneck helps you reason about which model size fits your hardware, how long a prompt you can afford, and where techniques like quantization or a mixture-of-experts design can buy back headroom.
How it works
- 01
Tokenize and embed the input
The text is split into tokens and each token is mapped to a vector (an embedding), with positional information added so the model knows the order of the sequence.
- 02
Apply self-attention
In each layer, every token attends to all others, producing a context-aware blend. Multiple attention heads capture different relationship types simultaneously.
- 03
Transform through feed-forward layers
Each token’s representation passes through a small neural network, then residual connections and normalization stabilize the result before the next layer repeats the process.
- 04
Predict the next token
After the final layer, the model outputs a probability distribution over the vocabulary, samples the next token, appends it, and repeats until the response is complete.
From concept to a governed, on-premise reality
VDF AI is architecture-agnostic by design: it runs open-weight transformer models — Llama, Mistral, Qwen, and others — inside your own environment, so the mechanics described here execute on infrastructure you control rather than a third-party API.
Because attention length and model size drive GPU cost, VDF AI pairs private model hosting with a governed model router that sends each request to the smallest transformer that can do the job well — turning architectural understanding into measurable cost savings.
Frequently asked questions
What is the transformer architecture in simple terms?
It is a neural network design that reads a whole sequence at once and uses attention to let every word consider every other word when building meaning. That parallel, context-rich processing is what makes modern language models both powerful and trainable at scale.
Why did transformers replace RNNs and LSTMs?
Recurrent networks processed text one step at a time, which was slow and caused them to forget earlier context in long sequences. Transformers process the whole sequence in parallel and connect distant tokens directly through attention, making them faster to train and better at long-range context.
What does "attention" do in a transformer?
Attention scores how relevant every token is to every other token, then blends their representations accordingly. It is how the model decides that, in a given sentence, a pronoun refers to a particular noun, or that one clause modifies another.
Are all LLMs built on transformers?
Nearly all mainstream large language models today — GPT, Claude, Gemini, Llama, Mistral — are transformer-based. Research into alternatives such as state-space models exists, but the transformer remains the dominant architecture for production LLMs.
What is the difference between encoder-only and decoder-only transformers?
Encoder-only models (like BERT) are built to understand and represent text for tasks such as classification and search. Decoder-only models (like GPT-style LLMs) generate text token by token. Most generative assistants are decoder-only.
Why does context length affect transformer cost so much?
Self-attention compares every token with every other token, so compute and memory grow roughly with the square of the sequence length. Doubling the context can quadruple the attention cost, which is why long context windows are a real engineering and budget consideration.
Put these concepts to work on infrastructure you control.
VDF AI runs governed agents, private retrieval, and model routing inside your own cloud, data center, or air-gapped network. Book a walkthrough mapped to your stack.