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Frantar, E., Ashkboos, S., Hoefler, T. and Alistarh, D. (2023) GPTQ: Accurate Post-Training Quantization for Generative Pre-Trained Transformers. arXiv:2210.17323.

has been cited by the following article:

• TITLE: Edge-Centric Generative AI: A Survey on Efficient Inference for Large Language Models in Resource-Constrained Environments

  AUTHORS: Rui Huang

  KEYWORDS: Large Language Models, Edge AI, Model Compression, Quantization, Neuromorphic Computing, Heterogeneous Systems

  JOURNAL NAME: Journal of Computer and Communications, Vol.14 No.4, April 30, 2026

  ABSTRACT: The deployment of Large Language Models (LLMs) on edge devices represents a paradigm shift in artificial intelligence, transitioning from cloud-centric dependence to pervasive, privacy-preserving on-device intelligence. However, enabling multi-billion parameter models on battery-powered devices (e.g., AR glasses, mobile agents) faces severe bottlenecks in memory bandwidth, energy efficiency, and thermal dissipation. This survey presents a comprehensive taxonomy of efficient inference techniques, rigorously categorizing recent advancements in post-training quantization, structural pruning, speculative decoding, and heterogeneous system scheduling. Unlike prior reviews, we formalize the trade-offs between model fidelity and hardware constraints through the lens of the Roofline Model and thermodynamic limits. We further identify critical industrial challenges in enabling “always-on” agents, proposing a roadmap for future hardware-algorithm co-design.