A large language model (LLM) is a transformer-based neural network trained to predict the next token over enormous text datasets. That simple objective, at scale, yields emergent abilities: writing, reasoning, translation, coding, and tool use. Models are characterised by parameter count, context window, and training data.
Open-source LLMs and the tooling around them — inference servers, quantization, fine-tuning frameworks — let teams run capable models on their own hardware instead of only through a hosted API.