Definition
The Transformer is a neural network architecture introduced in the 2017 paper Attention Is All You Need by Vaswani and colleagues at Google. It dispenses with the recurrence and convolutions used by earlier sequence models and relies entirely on self-attention to relate every token to every other token. Because attention can be computed in parallel across a whole sequence, Transformers train far faster on modern hardware than the recurrent networks they replaced, which is why they underpin nearly every large language model a sovereign operator might run locally.
How a Transformer is built
The original design is an encoder-decoder stack, though most generative LLMs use a decoder-only variant. Each layer contains two sub-blocks: a multi-head self-attention block and a position-wise feed-forward network. Every sub-block is wrapped in a residual connection and a layer normalization step, which keep gradients stable as depth grows. Because attention itself is order-agnostic, the model needs a positional encoding to know where each token sits in the sequence.
Why it matters for sovereignty
Understanding the Transformer is the entry point to running models you control rather than renting them. Architectural choices like grouped-query attention directly determine how much VRAM a model needs, which decides whether a given model fits on hardware you own.
For practical deployment of these models on your own hardware, see our work on self-hosted inference, and explore related entries such as self-attention and backpropagation to understand how Transformers learn.
In Simple Terms
The Transformer is a neural network architecture introduced in the 2017 paper Attention Is All You Need by Vaswani and colleagues at Google. It dispenses…