What technique involves distributed computing for training large language models?

The technique that involves distributed computing for training large language models is model parallelism. This approach is particularly beneficial when a single model is too large to fit into the memory of a single machine. With model parallelism, the model is split across multiple devices (such as GPUs or machines), allowing different parts of the model to be processed simultaneously. This is essential for efficiently handling very large models, as it enables the sharing of the computational load, leveraging the strengths of distributed resources.

While data parallelism, another commonly used technique in training large models, focuses on distributing training data across multiple processors to perform the same operations, model parallelism specifically addresses the challenges posed by large models themselves. Vertical scaling refers to increasing the power of a single machine (e.g., adding more memory or CPU cores), which is not as effective for extremely large models. Task scheduling, while important for managing when and how tasks are executed across multiple systems, does not directly relate to the concept of distributing model components for training.