A large language model learns statistical patterns across large text and code datasets, then predicts tokens to produce responses. Modern models can also use tools, process additional modalities, and participate in multi-step agent workflows.
Model capability depends on architecture, training, context, inference software, and hardware. A strong model can still run poorly when kernels or serving systems do not match its operations efficiently.
Acronyms and aliases
LLM acronymlarge language models variant
General terms
Related terms
Frequently asked questions
What does LLM stand for?
LLM stands for large language model, a model trained to process and generate language through sequences of tokens.
Do large language models require specialized hardware?
They can run on general processors, but GPUs and purpose-built accelerators usually provide much better throughput and efficiency at large scale.
