What does the term 'distributed computing' refer to in the context of large language models?

The term 'distributed computing' in the context of large language models specifically refers to the approach of employing techniques to process data across multiple devices. This allows for the splitting of tasks and workloads among different systems, which can lead to more efficient processing and faster execution times when working with large datasets or performing complex computations.

In the realm of large language models, which require significant computational power for both training and inference, distributed computing enables the model training process to be completed in a feasible time frame by using a network of computers that can operate in parallel. By distributing the model parameters and data across various devices, or nodes, the overall computational burden is reduced, thus allowing for the handling of larger models and datasets that wouldn't be manageable by a single machine.

The other options, focusing on data storage, user access, or centralizing resources, do not capture the essence of distributed computing as it relates specifically to the computational aspects and processing demands of large language models. Instead, they pertain more to data management and infrastructure rather than the distribution and parallel processing capability that defines distributed computing in this context.