Transformer Architecture
Every major LLM — GPT, Claude, Gemini, Llama, DeepSeek — is a Transformer. The idea is simple: instead of reading a sentence one word at a time, read the whole thing at once and let each word check in with every other word.
The Transformer replaced sequential processing with parallel attention. This one change made training on massive datasets practical — and unlocked the scaling race that produced GPT-4, Claude, and everything after.
Before 2017, every AI that read text did it one word at a time.
RNNs and LSTMs were the standard. Feed them a sequence and they'd churn through it left to right: process word 1, update their internal state, then word 2, update again, then word 3. Each step depended on the previous one finishing.
This worked for short text. But long sequences broke down in two ways. First, information from early words got diluted — by the time word 50 needed to reference something from word 1, the signal was weak. Second, and worse: you couldn't speed it up by throwing GPUs at it, because the work was inherently sequential. You can't parallelize "step 50 waits for step 49."
Let every word look at every other word at the same time.
This is self-attention. Take the sentence "The cat sat on the mat because it was tired." The word "it" needs to know it refers to "cat," not "mat." In an RNN, "it" would only have access to whatever the hidden state carried forward from earlier words — a compressed, lossy version. In a Transformer, "it" can directly look at "cat" and "mat" and compare them, no matter how far apart they are. Every word gets a direct line to every other word.
And because all these lookups happen independently, the whole operation runs in parallel on a GPU. A 1,000-word document gets processed in roughly the same wall-clock time as a 10-word sentence. This is the property that made training on internet-scale data possible.
Three pieces, each solving one problem.
Attention
Every word produces a question: "which other words in this sentence matter for understanding me?" It scores every other word, and the high scorers get more influence on the final meaning. "Sat" scores "cat" high (who sat?) and "mat" high (where?). "The" scores low on everything — it's just grammar glue.
Position
Attention alone doesn't know word order — "Dog bites man" and "Man bites dog" look the same to it. So before attention runs, each word gets a small position tag stamped into its representation: "I'm word 1," "I'm word 2," and so on. The model learns to factor this into its attention decisions.
Multi-Head
One attention pass catches one kind of relationship. The Transformer runs many in parallel — typically 32 to 128 heads — each learning to spot different patterns. One head might track grammar, another tracks pronoun references, another tracks topic flow. They run simultaneously and their results get combined.
A Transformer is a repeating block: attention, then thinking, then repeat.
Modern LLMs stack dozens of these blocks. GPT-4 uses about 120. Each block does the same two things: first, attention — let every word look at every other word and gather context. Then, a feed-forward layer — process that enriched information through the model's learned knowledge. Then on to the next block.
Early layers handle basic grammar and word relationships. Middle layers build sentence structure and factual knowledge. Late layers handle reasoning, planning, and generation.
Every chatbot you've used since 2020 is a Transformer.
GPT, Claude, Llama, Gemini
The architecture behind every chatbot. Predicts the next word given all previous words — that's how it generates text one token at a time. Each word can only look backward at words already written, not forward at words not yet generated.
Translation, summarization
The original 2017 design. Reads the full input in one pass (encoder), then generates output step by step (decoder) while referencing the encoded input. Better for tasks where you need to fully understand before responding.
BERT, embedding models
Reads text in both directions simultaneously. Excellent for understanding tasks: classification, search, sentiment analysis. Can't generate text — it produces a rich numerical representation, not new words.
DeepSeek V4, Mixtral
Replaces the feed-forward layer with many specialized expert sub-networks. Each token only activates a few experts — DeepSeek V4 Pro has 1.6 trillion total parameters but only uses 49 billion per token, keeping inference fast.
"Attention is all you need — and eight years later, it still is."
The Transformer's core idea is self-attention: every word in a sequence can directly reference every other word, in parallel, in one pass.
GPT, Claude, Gemini, and DeepSeek are all decoder-only Transformers. Their differences come from training data, scale, and fine-tuning — not the underlying architecture.