What is Quantisation in AI?

Quantization in artificial intelligence refers to the process of converting high-precision numerical representations to lower-precision formats within neural networks. By transforming 32-bit floating-point numbers into more compact formats like 8-bit integers, quantization achieves significant reductions in model size and computational requirements while preserving most of the model's performance. This technique can be compared to image compression, where some precision is traded for substantial gains in storage and processing efficiency.

For example, in 8-bit quantization, floating-point values are mapped to 256 distinct levels (2^8), with each value represented by an integer between 0 and 255.

The process involves determining a scale factor and zero-point that minimize the information loss during conversion. The scale factor represents the size of each quantization step, while the zero-point maps to the floating-point zero.

This mapping can be expressed as:

quantized_value = round((floating_point_value / scale) + zero_point)

Different quantization schemes exist, including:

• Dynamic quantization: Computes scaling factors on-the-fly during inference
• Static quantization: Uses pre-computed scaling factors based on calibration data
• Quantization-aware training: Simulates quantization effects during model training to improve accuracy

The choice of quantization method depends on the specific requirements of the deployment scenario, balancing factors like accuracy, latency, and implementation complexity.

The impact of quantization spans across multiple deployment scenarios in AI. In practical terms, quantized models require substantially less memory bandwidth and storage space, often achieving size reductions of up to 75%. This translates directly into faster inference times and reduced power consumption, making AI models more viable for deployment on resource-constrained devices like smartphones and IoT sensors. For server environments, quantization can significantly improve throughput and lower operational costs by enabling more efficient resource utilization.

While several model optimization techniques exist – such as Low-Rank Adaptation (LoRA) for efficient fine-tuning – quantization stands out for its broad applicability in model deployment. Modern quantization approaches, available through frameworks like TensorFlow Lite and PyTorch, range from simple post-training quantization to more sophisticated quantization-aware training methods. These tools enable developers to optimize their models based on specific use cases, typically achieving compression with minimal impact on model accuracy – usually less than 1-2% degradation. This makes quantization an essential tool for practical AI deployment across diverse computing environments.