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

• URL: http://arxiv.org/abs/2505.08620v1
• Date: Tue, 13 May 2025 14:39:33 GMT
• Title: Resource-Efficient Language Models: Quantization for Fast and Accessible Inference
• Authors: Tollef Emil Jørgensen,
• Abstract summary: Large language models have significantly advanced natural language processing, yet their heavy resource demands pose challenges regarding hardware accessibility and energy consumption.<n>This paper presents a focused and high-level review of post-training quantization techniques designed to optimize the inference efficiency of LLMs by the end-user.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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