  Transformer based large language models have achieved tremendous success.
However, the significant memory and computational costs incurred during the
inference process make it challenging to deploy large models on
resource-constrained devices. In this paper, we investigate compression and
efficient inference methods for large language models from an algorithmic
perspective. Regarding taxonomy, similar to smaller models, compression and
acceleration algorithms for large language models can still be categorized into
quantization, pruning, distillation, compact architecture design, dynamic
networks. However, Large language models have two prominent characteristics
compared to smaller models: (1) Most of compression algorithms require
finetuning or even retraining the model after compression. The most notable
aspect of large models is the very high cost associated with model finetuning
or training. Therefore, many algorithms for large models, such as quantization
and pruning, start to explore tuning-free algorithms. (2) Large models
emphasize versatility and generalization rather than performance on a single
task. Hence, many algorithms, such as knowledge distillation, focus on how to
preserving their versatility and generalization after compression. Since these
two characteristics were not very pronounced in early large models, we further
distinguish large language models into medium models and ``real'' large models.
Additionally, we also provide an introduction to some mature frameworks for
efficient inference of large models, which can support basic compression or
acceleration algorithms, greatly facilitating model deployment for users.
