In a world where AI drives business solutions, model size and computational demands pose critical challenges. As models grow more complex, they require significant computational and memory resources, complicating real-time applications that need instant response, like threat detection and biometric processing. These requirements also lead to rising infrastructure costs. The need to maintain AI efficiency while reducing latency and cost is prompting businesses to explore model compression techniques that reduce model size without a major drop in performance.
Machine learning (ML) models like large language models (LLMs) and deep neural networks offer high accuracy but come with substantial computational needs, making them costly to run, especially for continuous prediction tasks. Real-time AI applications demand low latency, often requiring high-performance GPUs or cloud infrastructure. With high prediction volumes, costs can rise dramatically, particularly in consumer-facing environments such as airports or retail locations. Compressing these models can reduce costs and, for applications on mobile devices, extend battery life while reducing data center power consumption, aligning AI with sustainability goals by lowering energy use and carbon emissions.
Top Model Compression Techniques
- Model Pruning: This technique reduces neural network size by eliminating parameters that contribute little to the model’s output, cutting down on computation and memory needs. Through pruning, businesses achieve faster prediction times and lower costs with minimal accuracy loss. Iterative pruning allows models to be retrained, recovering any accuracy lost in the process. This step-by-step pruning helps balance model size, performance, and speed, delivering a leaner model that remains accurate while consuming fewer resources.
- Model Quantization: Quantization reduces the precision of the numbers representing a model’s parameters from 32-bit floats to 8-bit integers, reducing memory use and speeding up inference, particularly useful for edge devices. This approach allows models to run on hardware with lower capabilities, such as mobile phones, reducing power usage and associated costs. Quantization can be combined with techniques like quantization-aware training, which maintains accuracy by allowing models to adapt to this compression. It can also be applied after pruning to further decrease latency while keeping performance steady.
- Knowledge Distillation: In this method, a smaller “student” model learns to approximate a larger “teacher” model’s performance, training on both the original data and probability distributions of the larger model. The result is a smaller model that performs well with fewer resources. Businesses gain from deploying smaller, faster models in applications where speed and cost are paramount. Distillation is also compatible with pruning and quantization, further optimizing models for real-time application efficiency.
By applying model pruning, quantization, and knowledge distillation, companies can run AI models more widely and economically across their services, maintaining performance while reducing reliance on expensive hardware.
Image courtesy of DALL-E by ChatGPT
