It's an alternate architecture of LLMs, they actually predate modern LLMs. An encoder-decoder model was actually the model used in the "Attention if all you need" paper that introduced the transformer and essentially gave birth to modern LLMs.

A encoder-decoder model splits input and output. This makes sense for translation tasks, summarization, etc. They're good when there's a clear separation of "understand the task" and "complete the task", but you can use it for anything really. A example would be send "Translate to english: Le chat est noir." to the encoder, the encoder processes everything in a single step, that is understand the task as a whole, then the output of the encoder is fed to the decoder and then the decoder runs one token at a time.

GPT ditches the encoder altogether and just runs the decoder with some slight changes, this makes it more parameter efficient but tends to hallucinate more due to past tokens containing information that might be wrong. You can see it as the encoder running on each token as they are read/generated.

Edit: On re-read I noticed it might not be clear what I mean by past tokens containing wrong information. I mean that for each token the model generates a hidden state, those states don't change, so for example an input of 100 tokens will have 100 hidden states, the states are generated at once on the encoder model, and one token at a time on the decoder models. Since the decoder doesn't have the full information yet, the hidden state will contain extra information that might not having anything to do with the task, or even confuse it.

For example if you give the model the task "Please translate this to chinese: Thanks for the cat, he's cute. I'm trying to send it to my friend in hong kong.". For a enc-dec model it would read the whole thing at once and understand that you mean cantonese. But a decoder only model would "read" it one token a time it could trip in several places, 1. assume chinese means mandarin chinese not cantonese, 2. assume that the text after "cute." it's something to also translate and not a clarification. This would have several token worth of extra information that would confuse the model. Models are trained with this in mind so they're used to tokens having lots of different meanings embeded in them, then having later tokens narrow down the meanings, but it might cause models to ignore certain tokens, or hallucinate.