  The internal structure and operation mechanism of large-scale language models
are analyzed theoretically, especially how Transformer and its derivative
architectures can restrict computing efficiency while capturing long-term
dependencies. Further, we dig deep into the efficiency bottleneck of the
training phase, and evaluate in detail the contribution of adaptive
optimization algorithms (such as AdamW), massively parallel computing
techniques, and mixed precision training strategies to accelerate convergence
and reduce memory footprint. By analyzing the mathematical principles and
implementation details of these algorithms, we reveal how they effectively
improve training efficiency in practice. In terms of model deployment and
inference optimization, this paper systematically reviews the latest advances
in model compression techniques, focusing on strategies such as quantification,
pruning, and knowledge distillation. By comparing the theoretical frameworks of
these techniques and their effects in different application scenarios, we
demonstrate their ability to significantly reduce model size and inference
delay while maintaining model prediction accuracy. In addition, this paper
critically examines the limitations of current efficiency optimization methods,
such as the increased risk of overfitting, the control of performance loss
after compression, and the problem of algorithm generality, and proposes some
prospects for future research. In conclusion, this study provides a
comprehensive theoretical framework for understanding the efficiency
optimization of large-scale language models.
