Here are three critical LLM compression techniques to boost AI performance
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In the fast-paced digital world of today, businesses that rely on AI are faced with new challenges. These include latency, memory consumption, and computing power costs. The models that power these innovations are becoming more complex and resource intensive as AI continues to advance. These large models are capable of achieving remarkable results across a wide range of tasks. However, they often come with significant memory and computational requirements.
Fast, accurate results are essential for real-time AI apps like fraud detection, threat detection, biometric plane boarding, and more. Businesses are motivated to accelerate AI implementations not just to save on infrastructure costs and compute power, but to achieve higher operational efficiency, quicker response times, and seamless user experience, which translate into tangible business results such as increased customer satisfaction and decreased wait times. The first solution is to use smaller models and sacrifice accuracy and performance in favor of speed. Another solution is to upgrade your hardware, such as GPUs. These can run high-performance AI models with low latency. This solution, however, will quickly increase costs due to the fact that demand for GPUs is far greater than supply. It also does not solve the use case where the AI model needs to be run on edge devices like smartphones.
Enter model compression techniques: A set of methods designed to reduce the size and computational demands 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-constrained environments.
How model compression helps
There are several reasons why machine learning (ML) models should be compressed. First, although larger models are often more accurate, they require a lot of computational power to make predictions. These models are computationally and memory intensive. As these models are deployed in real-time applications, like recommendation engines or threat detection systems, their need for high-performance GPUs or cloud infrastructure drives up costs.
Second, latency requirements for certain applications add to the expense. In order to maintain low response times, many AI applications require real-time or low latency predictions. This requires powerful hardware. It becomes more expensive to run models continuously as the volume of predictions increases.
Additionally, the sheer volume of inference requests in consumer-facing services can make the costs skyrocket. Inference requests are made by solutions at retail, airports and banks every day, consuming computing resources. This operational load demands careful latency and cost management to ensure that scaling AI does not drain resources.
However, model compression is not just about costs. Model compression is not just about costs. Smaller models use less energy. This translates into longer battery life for mobile devices and lower power consumption in data centres. It not only reduces operational costs, but it also aligns AI with environmental sustainability goals through a reduction in carbon emissions. Model compression techniques address these challenges and pave the road to more cost-effective, practical, and widely deployable AI.
Top model compression techniques
Compressed models can perform predictions more quickly and efficiently, enabling real-time applications that enhance user experiences across various domains, from faster security checks at airports to real-time identity verification. Model pruning is one of the most common techniques used to reduce the size and complexity of AI models. The computational complexity of a model can be reduced by removing redundant or insignificant parameters. This leads to faster inference and less memory usage. This results in a model that is leaner, but still performs well. It also requires less resources to run. Pruning is especially beneficial for businesses because it reduces both the cost and time of making predictions, without sacrificing accuracy. Re-training a pruned model will restore any accuracy lost. Iterative model pruning is possible until the desired performance, size, and speed of the model are reached. Techniques like iterative pruning help in effectively reducing model size while maintaining performance.
Model quantization
Quantization is another powerful method for optimizing ML models. Quantization reduces the precision in the numbers that are used to represent the parameters and computations of a model, usually from 32-bit numbers to 8 bit integers. It reduces the memory footprint of the model and speeds up inference because it can run on hardware with less power. Memory and speed improvements are as high as 4x. Quantization is a great way to increase efficiency in environments with limited computational resources, like mobile phones or edge devices. It also slashes the energy consumption of running AI services, translating into lower cloud or hardware costs.
Typically, quantization is done on a trained AI model, and uses a calibration dataset to minimize loss of performance. Quantization-aware learning can maintain accuracy in cases where performance loss is acceptable. The model will adapt to the compression during the training process. Additionally, 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). The student model is often trained on the original data as well as the soft outputs of the teacher (probability distributed). This allows the “reasoning”, as well as the final decisions of the large model, to be transferred into the smaller model. The student model is taught to mimic the performance of a teacher by focusing only on the most important aspects of data. This results in a lightweight, accurate model with fewer computations. It’s especially valuable in real-time applications where speed and efficiency are critical. It’s particularly 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 similarly to a larger complex model.
Conclusion
As businesses seek to scale their AI operations, implementing real-time AI solutions becomes a critical concern. Model pruning, quantization, and knowledge distillation are practical techniques that can help businesses scale their AI operations. They optimize models to make faster, cheaper predictions, without any major performance loss. These strategies allow companies to reduce their dependence on expensive hardware and deploy models across a wider range of services, ensuring that AI is an economically viable component 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 — it’s a necessity.
Chinmay Jog is a senior machine learning engineer at Pangiam.
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