A Survey of Compression Methods for Efficient Model Inferencing

The advent of Large Language Models (LLMs) has revolutionized the field of artificial intelligence, enabling a broad spectrum of applications across academic research and industrial domains. Central to this transformation is the rise of Transformer-based architectures, which have set new benchmarks in Natural Language Processing (NLP) tasks, including text generation, machine translation, and sentiment analysis. However, despite their remarkable performance, the computational demands of these models present significant challenges, particularly when it comes to deploying them in resource-constrained environments. Models like GPT-4, which boast upwards of 1.8 trillion parameters, require substantial processing power, memory, and storage, making them ill-suited for smaller devices such as those found on the Internet of Things (IoT) and embedded systems.This limitation raises a critical need for methods to make LLMs more efficient and deployable on edge devices, which often have strict constraints on computational resources. Several promising techniques have emerged to address this challenge, particularly those focused on model compression. These approaches, which involve reducing the precision of model weights and activations, offer potential avenues for shrinking model size and accelerating inference speed. This paper explores a range of model compression techniques, particularly emphasizing their applicability to LLMs. Our goal is to identify strategies that can enhance the efficiency of LLMs, enabling their deployment on devices with limited resources. Furthermore, the synergistic potential of combining multiple compression methods to optimize model performance is being investigated. The ultimate aim is to contribute to democratizing AI by making state-of-the-art models more accessible for real-world applications across diverse devices.