There are two key innovations that make transformers particularly adept for large language models: positional encodings and self-attention. 

Positional encoding embeds the order of which the input occurs within a given sequence. Essentially, instead of feeding words within a sentence sequentially into the neural network, thanks to positional encoding, the words can be fed in non-sequentially.

Self-attention assigns a weight to each part of the input data while processing it. This weight signifies the importance of that input in context to the rest of the input. In other words, models no longer have to dedicate the same attention to all inputs and can focus on the parts of the input that actually matter. This representation of what parts of the input the neural network needs to pay attention to is learnt over time as the model sifts and analyzes mountains of data.

These two techniques in conjunction allow for analyzing the subtle ways and contexts in which distinct elements influence and relate to each other over long distances, non-sequentially. 

The ability to process data non-sequentially enables the decomposition of the complex problem into multiple, smaller, simultaneous computations. Naturally, GPUs are well suited to solve these types of problems in parallel, allowing for large-scale processing of large-scale unlabelled datasets and enormous transformer networks.