K-Quantization and its Impact on Output Performance

2026-05-19

AI Summary

This study systematically investigates the impact of 2–8-bit integer quantization on large language model performance, elucidating the trade-offs between model compression and task effectiveness. Under a unified evaluation framework, eight prominent models are assessed across multiple benchmarks—including MMLU-Pro, CRUXEval, and MuSR—spanning diverse tasks such as knowledge reasoning, code comprehension, and reading comprehension, with varying model scales and quantization schemes (e.g., Q2_K, Q8_0). The findings reveal diminishing returns at higher bit widths, demonstrate that 2-bit quantization is feasible albeit with notable performance degradation in some models, and identify 7–9B parameter models as achieving an optimal balance between compression efficiency and performance retention. This work presents the first comprehensive, multi-model, multi-task, and multi-precision empirical analysis, offering actionable insights for efficient deployment of quantized language models.

Abstract

Recent advancements in large language models (LLMs) have shown their remarkable capacities in many NLP tasks. However, their substantial size often presents challenges for deployment. This necessitates efficient techniques for model compression, with quantization emerging as a prominent solution. Despite its benefits, the exact impact of quantization (from 2- to 6-bit) on the performance and accuracy of LLMs remains an active area of research. This paper investigates the performance of eight LLMs at various quantization levels, focusing on tasks such as MMLU-Pro for knowledge processing and reasoning, CRUXEval for code comprehension, and MuSR for reading comprehension. Our results show a consistent trend where higher precision (e.g., 8-bit Q8\_0) yields improved performance, albeit with diminishing returns. Aggressive quantization (e.g., 2-bit Q2\_K) usually retains acceptable accuracy, though some models show a substantial loss in performance. Our findings indicate that while lower bit precision generally reduces performance, the impact varies across models and tasks. Larger models show greater resilience to aggressive quantization, but can still undergo significant drops at lower precision levels. Mid-sized models in the 7-9 billion parameter range strike an optimal balance between efficiency and resource usage. Such results provide insights into the trade-offs between model size, quantization, and performance.

Problem

• quantization
• large language models
• model performance
• bit precision
• model compression

Innovation

• quantization
• large language models
• model compression
• bit precision
• performance trade-offs