Recent code large language models have achieved remarkable progress on general programming tasks. Nevertheless, their performance degrades significantly in industrial scenarios that require reasoning about hardware semantics, specialized language constructs, and strict resource constraints. To address these challenges, we introduce InCoder-32B (Industrial-Coder-32B), the first 32B-parameter code foundation model unifying code intelligence across chip design, GPU kernel optimization, embedded systems, compiler optimization, and 3D modeling. By adopting an efficient architecture, we train InCoder-32B from scratch with general code pre-training, curated industrial code annealing, mid-training that progressively extends context from 8K to 128K tokens with synthetic industrial reasoning data, and post-training with execution-grounded verification. We conduct extensive evaluation on 14 mainstream general code benchmarks and 9 industrial benchmarks spanning 4 specialized domains. Results show InCoder-32B achieves highly competitive performance on general tasks while establishing strong open-source baselines across industrial domains.
@article{arxiv.2603.16790,
title = {InCoder-32B: Code Foundation Model for Industrial Scenarios},
author = {Jian Yang and Wei Zhang and Jiajun Wu and Junhang Cheng and Shawn Guo and Haowen Wang and Weicheng Gu and Yaxin Du and Joseph Li and Fanglin Xu and Yizhi Li and Lin Jing and Yuanbo Wang and Yuhan Gao and Ruihao Gong and Chuan Hao and Ran Tao and Aishan Liu and Tuney Zheng and Ganqu Cui and Zhoujun Li and Mingjie Tang and Chenghua Lin and Wayne Xin Zhao and Xianglong Liu and Ming Zhou and Bryan Dai and Weifeng Lv},
journal= {arXiv preprint arXiv:2603.16790},
year = {2026}
}