Here are 3 crucial LLM compression strategies to improve AI performance

In today’s fast-paced digital landscape, companies that rely on AI face new challenges: latency, memory usage, and computing power costs to run an AI model. As AI rapidly develops, the models that drive these innovations have become increasingly complex and intensive. Although these large models have achieved remarkable performance on a variety of tasks, they often come with significant computational and memory requirements.

For real-time AI applications such as threat detection, fraud detection, biometric aircraft boarding and many others, delivering fast, accurate results is critical. The real motivation for companies to accelerate AI implementations comes not only from simply saving on infrastructure and computing costs, but also from achieving higher operational efficiencies, faster response times, and seamless user experiences, which translate into tangible business results such as improved customer satisfaction and shorter waiting times.

Two solutions immediately come to mind to address these challenges, but they are not without drawbacks. One solution is to train smaller models, trading accuracy and performance for speed. The other solution is to invest in better hardware such as GPUs, which can run complex, high-performance AI models with low latency. However, as demand for GPUs far exceeds supply, this solution will quickly drive up costs. It also does not solve the use case where the AI ​​model needs to be run on edge devices such as smartphones.

Enter model compression techniques: a set of methods designed to reduce the size and computational requirements of AI models while maintaining their performance. In this article, we will explore some model compression strategies that will help developers deploy AI models even in the most resource-rich environments.

How model compression helps

There are several reasons why machine learning (ML) models need to be compressed. First, larger models often provide better accuracy but require significant computing resources to perform predictions. Many state-of-the-art models, such as large language models (LLMs) and deep neural networks, are both computationally expensive and memory intensive. Because these models are deployed in real-time applications, such as recommendation engines or threat detection systems, their need for powerful GPUs or cloud infrastructure drives up costs.

Second, latency requirements for certain applications increase costs. Many AI applications rely on real-time or low-latency predictions, requiring powerful hardware to keep response times low. The greater the volume of predictions, the more expensive it becomes to run these models continuously.

Furthermore, the sheer volume of inference requests in consumer-facing services can cause costs to skyrocket. For example, solutions deployed at airports, banks, or retail locations will incur a large number of inference requests every day, with each request consuming computing resources. This operational burden requires careful latency and cost management to ensure that scaling AI does not drain resources.

However, model compression is not just about cost. Smaller models consume less energy, which translates to longer battery life in mobile devices and lower energy consumption in data centers. This not only reduces operational costs, but also aligns AI development with environmental sustainability goals by reducing CO2 emissions. By addressing these challenges, model compression techniques pave the way for more practical, cost-effective and widely applicable AI solutions.

Compression techniques of top models

Compressed models can perform predictions faster and more efficiently, enabling real-time applications that improve the user experience in various domains, from faster airport security checkpoints to real-time identity verification. Here are some common techniques to compress AI models.

Model pruning

Model pruning is a technique that reduces the size of a neural network by removing parameters that have little influence on the model’s output. Eliminating redundant or insignificant weights reduces the model’s computational complexity, leading to faster inference times and lower memory usage. The result is a slimmer model that still performs well, but requires fewer resources. For businesses, pruning is especially useful because it can reduce both the time and cost of making forecasts without sacrificing accuracy. A pruned model can be retrained to recover any lost accuracy. Model pruning can be performed iteratively until the required model performance, size, and speed are achieved. Techniques such as iterative pruning help in effectively reducing model size while maintaining performance.

Model quantization

Quantization is another powerful method for optimizing ML models. It reduces the precision of the numbers used to represent a model’s parameters and calculations, typically from 32-bit floating point numbers to 8-bit integers. This significantly reduces the model’s memory footprint and speeds up inference by allowing it to run on less powerful hardware. The memory and speed improvements can be up to 4x. In environments where computing resources are limited, such as edge devices or mobile phones, quantization allows companies to deploy models more efficiently. It also reduces the energy consumption of running AI services, which translates into lower cloud or hardware costs.

Typically, quantization is performed on a trained AI model and uses a calibration dataset to minimize performance loss. In cases where the performance loss is still more than acceptable, techniques such as quantization-aware training can help maintain accuracy by letting the model itself adapt to this compression during the learning process. Furthermore, model quantization can be applied after model pruning, further improving latency while maintaining performance.

Knowledge distillation

This technique involves training a smaller model (the student) to mimic the behavior of a larger, more complex model (the teacher). This process often involves training the student model based on both the original training data and the teacher’s soft results (probability distributions). This helps transfer not only the final decisions, but also the nuanced ‘reasoning’ from the larger model to the smaller one.

The student model learns to approximate teacher performance by focusing on critical aspects of the data, resulting in a lightweight model that retains much of the accuracy of the original, but with far fewer computational requirements. For businesses, knowledge distillation enables the deployment of smaller, faster models that provide comparable results at a fraction of the inference cost. It is especially valuable in real-time applications where speed and efficiency are critical.

A student model can be further compressed by applying pruning and quantization techniques, resulting in a much lighter and faster model, which performs comparably to a larger complex model.

Conclusion

As companies look to scale their AI operations, implementing real-time AI solutions becomes a critical concern. Techniques such as model pruning, quantization, and knowledge distillation provide practical solutions to this challenge by optimizing models for faster, cheaper predictions without major performance losses. By adopting these strategies, companies can reduce their dependence on expensive hardware, deploy models more broadly across their services, and ensure AI remains an economically viable part of their operations. In a landscape where operational efficiency can make or break a company’s ability to innovate, optimizing ML inference is not just an option, but a necessity.

Chinmay Jog is a senior machine learning engineer at Pangiam.