Transformers are a breakthrough neural network architecture underlying modern LLMs, using a 'self-attention' mechanism to weigh the importance of different data
The transformer architecture processes sequences with self-attention: every token computes weighted relationships to other tokens in parallel, letting the network model long-range dependencies without recurrence. Stacked attention and feed-forward layers, trained at scale, produce the representations behind modern language, vision, and audio models alike.
Transformers are the architecture of the AI era: the design behind LLMs, vision-language models, and diffusion backbones. Their parallelism unlocked training at internet scale, and their attention mechanism is why models handle context windows of hundreds of thousands of tokens. Architectural literacy here explains both capabilities and costs.
A mechanism where each token scores its relevance to other tokens and blends their information accordingly: letting 'it' link to the right noun paragraphs back, in parallel across the whole sequence.
Recurrent networks processed sequentially and forgot long-range context; transformers parallelize across the sequence and attend globally, making web-scale training feasible. Capability scaling followed compute scaling.
Challengers (state-space models like Mamba, hybrid architectures) target attention's long-sequence costs, and production systems increasingly mix techniques, but in 2026 transformers remain the dominant backbone of frontier models.