Communication Efficiency. This aspect relates to the exchange of parameters and gradients between different devices
or layers during training. Techniques like all-reduce are employed to synchronize gradients across all devices at the end of
backward propagation in data parallel training. The goal is to minimize the volume of communication data during collective
operations such as broadcast, reduce, all-reduce, and all-gather.

In short, training and tuning LLMs is a complex challenge that demands a comprehensive approach. An integrated strategy
that considers all these efficiency aspects is vital for the effective and scalable training and tuning of LLMs. The subsequent
sections will provide a detailed exploration of these aspects.

6.2 Scalable Training
TheEfficiencySpectrumofLargeLanguageModels-AnAlgorithmicSurvey