VentureBeat/Ideogram

Today, virtually every cutting-edge AI product and model uses a transformer architecture. Large language models (LLMs) such as GPT-4o, LLaMA, Gemini and Claude are all transformer-based, and other AI applications such as text-to-speech, automatic speech recognition, image generation and text-to-video models have transformers as their underlying technology.  

With the hype around AI not likely to slow down anytime soon, it’s time to give transformers their due, which is why I’d like to explain a little about how they work, why they are so important for the growth of scalable solutions and why they are the backbone of LLMs.  

Transformers are more than meets the eye 

In brief, a transformer is a neural network architecture designed to model sequences of data, making them ideal for tasks such as language translation, sentence completion, automatic speech recognition and more. Transformers have really become the dominant architecture for many of these sequence modeling tasks because the underlying attention-mechanism can be easily parallelized, allowing for massive scale when training and performing inference.  

Originally introduced in a 2017 paper, “Attention Is All You Need” from researchers at Google, the transformer was introduced as an encoder-de...