Reimagining Model Efficiency in Generative AI Through Unified and Differentiable Quantization Approaches
Abstract
As generative artificial intelligence (GenAI) models, particularly large-scale autoregressive transformers, diffusion models, and multi-modal architectures, continue to grow in size and complexity, their immense computational and memory requirements pose substantial challenges to real-world deployment. Quantization, the process of reducing numerical precision of model parameters, activations, or gradients, has emerged as a critical tool to mitigate these challenges by enabling significant reductions in model size, inference latency, and energy consumption. However, the quantization of generative models introduces a uniquely complex set of obstacles that distinguish it from traditional applications in discriminative models. Unlike classifiers or object detectors, generative models must preserve semantic coherence, distributional fidelity, and high-dimensional output structure, all of which are highly sensitive to the perturbations introduced by low-precision representations. This review presents a comprehensive and technical examination of the current landscape of quantization in GenAI, spanning theoretical formulations, algorithmic advances, training strategies, hardware implications, and deployment scenarios. We begin by introducing the mathematical foundations of quantization, including uniform and non-uniform quantizers, rounding operations, scaling mechanisms, and optimization frameworks for minimizing quantization-induced distortion. We then survey a wide spectrum of…
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