What Is the Transformer Architecture Behind Modern AI Large Models?

AI large models dominate current research, and the Transformer is the core algorithm that powers them. It was proposed by Google in the 2017 paper Attention Is All You Need , which introduced a completely new architecture based on self‑attention.

Unlike the previously dominant recurrent neural networks (RNN), long short‑term memory (LSTM) and gated recurrent units (GRU), the Transformer discards recurrence and convolution entirely. Its self‑attention mechanism computes a full attention matrix in parallel, dramatically increasing the utilization of GPU resources and allowing massive scaling.

The sequential nature of RNN‑style models creates two major problems: (1) they cannot be parallelized because each token must wait for the previous token’s computation, leading to under‑utilized GPUs and slower training; (2) they struggle with long‑distance dependencies, often forgetting information as the distance between related tokens grows. The article illustrates this with a Chinese sentence example where the model must process each word one after another, causing memory degradation over long spans.

Because of these limitations, a new architecture was required—this is the Transformer. It resolves the parallelism bottleneck and preserves long‑range context, making it the foundation for almost all mainstream large models such as GPT, Llama, Qwen, DeepSeek, and Gemini.

Traditional NLP approaches listed in the article include:

RNN

LSTM

GRU

He = 小明

我
↓
喜
↓
欢
↓
学
↓
习
↓
AI

CPU:
A
↓
B
↓
C
↓
D