Transformer
Language Models & ArchitectureThe neural network architecture underpinning modern LLMs and foundation models, introduced in 2017, enables efficient parallel processing of sequences through self-attention.
Eleveight AI's B300 cluster, with its HBM3e memory bandwidth and InfiniBand XDR interconnect, is specifically optimized for the transformer workloads that dominate modern AI development.
Overview
The transformer, introduced in the 2017 paper "Attention Is All You Need," reshaped AI by discarding the step-by-step sequential processing of the recurrent networks that preceded it. In its place came a parallel mechanism called self-attention, which lets a model weigh the relationships between every element of a sequence at once rather than reading through them one position at a time.
How it works
The central idea is the attention mechanism. For each element in an input sequence, attention computes a weighted combination of all the other elements, deciding how much each one should influence it, and it does this for every position simultaneously. That simultaneity is what makes transformers so highly parallelizable, and in turn what makes them such a natural fit for GPU hardware.
Why it matters
Every major large language model, GPT-4, Claude, Llama, Gemini, is built on the transformer. The architecture and the GPU are the defining hardware-software pairing of the present AI era: transformers happen to demand exactly the kind of massively parallel computation GPUs supply best, and that fit is much of why progress has moved so quickly.
Use cases
- Backbone of all major LLMs
- Image generation models (vision transformers)
- Multimodal AI processing text, images, and audio
- Code generation and reasoning models