인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Quantized small language models (SLMs) offer a promising approach for deploying advanced natural language process- ing (NLP) services on resource-constrained edge devices. However, an in-depth examination of how different quantization configurations influence accuracy and efficiency remains underexplored. This paper systematically evaluates 72 quantized variants of Llama 3.2 (1B and 3B parameters) and Qwen 2.5 (1.5B and 3B parameters) across 13 quantization configura- tions, ranging from q2_K to q6_K. We use the MMLU-Pro benchmark to measure the accuracy (including and excluding random guesses), inference time, resource utilization, and power consumption on an NVIDIA Jetson Orin Nano. Our findings reveal that low-bit quantized models often rely heavily on random guessing, with modest accuracy improvements observed when these are excluded. Furthermore, Qwen 2.5 models generally yield superior accuracy and lower latency than Llama 3.2, albeit with higher sensitivity to quantization, whereas Llama 3.2 exhibits more consistent performance across quantization configurations. CPU utilization remains low (approximately 1-4%), with GPU utilization peaking up to 90% and power consumption ranging from 9.2 W to 11.5 W. Variability across different domains (computer science, engineering, and math) underscores the importance of selecting the appropriate model family, parameter size, and quantization configuration for specific applications. We conclude by outlining future directions for improving on-device NLP, including mixed-precision quantization, hardware-specific optimizations, and broader assessments covering multilingual or multimodal tasks.
본문·목차
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