>
E
Model Details
Domain:
Task:
Model Access:
Open weights (non-commercial)
Introduction
This technical report introduces EXAONE 4.0, which integrates a Non-reasoning mode and a Reasoning mode to achieve both the excellent usability of EXAONE 3.5 and the advanced reasoning abilities of EXAONE Deep. To pave the way for the agentic AI era, EXAONE 4.0 incorporates essential features such as agentic tool use, and its multilingual capabilities are extended to support Spanish in addition to English and Korean. The EXAONE 4.0 model series consists of two sizes: a mid-size 32B model optimized for high performance, and a small-size 1.2B model designed for on-device applications. The EXAONE 4.0 demonstrates superior performance compared to open-weight models in its class and remains competitive even against frontier-class models. The models are publicly available for research purposes and can be easily downloaded via this https URL.
Benchmarking
FLOPs2.69e+24
Notes: Reported in Table 2. from communication with the authors: "EXAONE 4.0 32B: NVIDIA H200 GPUs x 512 EA for 19 weeks (FP8 mode training)"
Training
Training Code AccessibilityExaone license (permits only academic, research, or educational usage) https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-32B
HardwareNVIDIA H200 SXM
Hardware Quantity512
Size Notes: max sequence length 131,072 (Table 1) size of pretraining data: 14T (Table 2)
Parameters
Parameters32000000000
Notes: 32B
Authors
LG AI Research: Kyunghoon Bae, Eunbi Choi, Kibong Choi, Stanley Jungkyu Choi, Yemuk Choi, Kyubeen Han, Seokhee Hong, Junwon Hwang, Taewan Hwang, Joonwon Jang, Hyojin Jeon, Kijeong Jeon, Gerrard Jeongwon Jo, Hyunjik Jo, Jiyeon Jung, Euisoon Kim, Hyosang Kim, Jihoon Kim, Joonkee Kim, Seonghwan Kim, Soyeon Kim, Sunkyoung Kim, Yireun Kim, Yongil Kim, Youchul Kim, Edward Hwayoung Lee, Gwangho Lee, Haeju Lee, Honglak Lee, Jinsik Lee, Kyungmin Lee, Sangha Park, Young Min Paik, Yongmin Park, Youngyong Park, Sanghyun Seo, Sihoon Yang, Heuiyeen Yeen, Sihyuk Yi, Hyeongu Yun
Vision
Medicine