Resource-Efficient Language Models: Quantization for Fast and Accessible Inference

Tollef Emil Jørgensen

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arXiv · 2505.08620

Resource-Efficient Language Models: Quantization for Fast and Accessible Inference

Tollef Emil Jørgensen

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Abstract

Large language models have significantly advanced natural language processing, yet their heavy resource demands pose severe challenges regarding hardware accessibility and energy consumption. This paper presents a focused and high-level review of post-training quantization (PTQ) techniques designed to optimize the inference efficiency of LLMs by the end-user, including details on various quantization schemes, granularities, and trade-offs. The aim is to provide a balanced overview between the theory and applications of post-training quantization.

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Tollef Emil Jørgensen. 2025-05-13. Resource-Efficient Language Models: Quantization for Fast and Accessible Inference. https://arxiv.org/abs/2505.08620

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