Hon Hai Research Institute Launches Traditional Chinese LLM With Reasoning Capabilities
March 13, 2025 | PRNewswireEstimated reading time: 3 minutes
Hon Hai Research Institute announced today the launch of the first Traditional Chinese Large Language Model (LLM), setting another milestone in the development of Taiwan's AI technology with a more efficient and lower-cost model training method completed in just four weeks.
The institute, which is backed by Hon Hai Technology Group ("Foxconn") (TWSE:2317), the world's largest electronics manufacturer and leading technological solutions provider, said the LLM – code named FoxBrain – will be open sourced and shared publicly in the future. It was originally designed for applications used in the Group's internal systems, covering functions such as data analysis, decision support, document collaboration, mathematics, reasoning and problem solving, and code generation.
FoxBrain not only demonstrates powerful comprehension and reasoning capabilities but is also optimized for Taiwanese users' language style, showing excellent performance in mathematical and logical reasoning tests.
"In recent months, the deepening of reasoning capabilities and the efficient use of GPUs have gradually become the mainstream development in the field of AI. Our FoxBrain model adopted a very efficient training strategy, focusing on optimizing the training process rather than blindly accumulating computing power," said Dr. Yung-Hui Li, Director of the Artificial Intelligence Research Center at Hon Hai Research Institute. "Through carefully designed training methods and resource optimization, we have successfully built a local AI model with powerful reasoning capabilities."
The FoxBrain training process was powered by 120 NVIDIA H100 GPUs, scaled with NVIDIA Quantum-2 InfiniBand networking, and finished in just about four weeks. Compared with inference models recently launched in the market, the more efficient and lower-cost model training method sets a new milestone for the development of Taiwan's AI technology.
FoxBrain is based on the Meta Llama 3.1 architecture with 70B parameters. In most categories among TMMLU+ test dataset, it outperforms Llama-3-Taiwan-70B of the same scale, particularly exceling in mathematics and logical reasoning. The following are the technical specifications and training strategies for FoxBrain:
Established data augmentation methods and quality assessment for 24 topic categories through proprietary technology, generating 98B tokens of high-quality pre-training data for Traditional Chinese
- Context window length: 128 K tokens
- Utilized 120 NVIDIA H100 GPUs for training, with total computational cost of 2,688 GPU days
- Employed multi-node parallel training architecture to ensure high performance and stability
- Used a unique Adaptive Reasoning Reflection technique to train the model in autonomous reasoning
In test results, FoxBrain showed comprehensive improvements in mathematics compared to the base Meta Llama 3.1 model. It achieved significant progress in mathematical tests compared to Taiwan Llama, currently the best Traditional Chinese large model, and surpassed Meta's current models of the same class in mathematical reasoning ability. While there is still a slight gap with DeepSeek's distillation model, its performance is already very close to world-leading standards.
FoxBrain's development – from data collection, cleaning and augmentation, to Continual Pre-Training, Supervised Finetuning, RLAIF, and Adaptive Reasoning Reflection – was accomplished step by step through independent research, ultimately achieving benefits approaching world-class AI models despite limited computational resources. This large language model research demonstrates that Taiwan's technology talent can compete with international counterparts in the AI model field.
Although FoxBrain was originally designed for internal group applications, in the future, the Group will continue to collaborate with technology partners to expand FoxBrain's applications, share its open-source information, and promote AI in manufacturing, supply chain management, and intelligent decision-making.
During model training, NVIDIA provided support through the Taipei-1 Supercomputer and technical consultation, enabling Hon Hai Research Institute to successfully complete the model pre-training with NVIDIA NeMo. FoxBrain will also become an important engine to drive the upgrade of Foxconn's three major platforms: Smart Manufacturing. Smart EV. Smart City.
Testimonial
"We’re proud to call I-Connect007 a trusted partner. Their innovative approach and industry insight made our podcast collaboration a success by connecting us with the right audience and delivering real results."
Julia McCaffrey - NCAB GroupSuggested Items
Happy’s Tech Talk #42: Applying Density Equations to UHDI Design
08/19/2025 | Happy Holden -- Column: Happy’s Tech TalkWith the need for faster speeds, more parts on an assembly, and the trend to make things smaller for portability, the printed circuit design and layout process is both creative and challenging. The process involves “applying the density equation” while considering certain boundary conditions, such as electrical and thermal performance. Unfortunately, many designers don’t realize there is a mathematical process to laying out a printed circuit.
Happy’s Tech Talk #41: Sustainability and Circularity for Electronics Manufacturing
08/13/2025 | Happy Holden -- Column: Happy’s Tech TalkI attended INEMI’s June 12 online seminar, “Sustainable Electronics Tech Topic Series: PCBs and Sustainability.” Dr. Maarten Cauwe of imec spoke on “Life Cycle Inventory (LCI) Models for Assessing and Improving the Environmental Impact of PCB Assemblies,” and Jack Herring of Jiva Materials Ltd. spoke on “Transforming Electronics with Recyclable PCB Technology.” This column will review information and provide analysis from both presentations.
Getting Our ‘Fil’ of Design Constraint Techniques
08/07/2025 | Andy Shaughnessy, Design007 MagazineFilbert Arzola is a principal electrical engineer at Raytheon SAS and an instructor who teaches one of the few classes (that I know of) that focuses on setting design constraints. I asked Fil to share his thoughts on design constraints: the factors involved, the various trade-offs, and his best practices for optimizing constraints for your particular design. As Fil says, “Everything about a PCB is a constraint.”
INEMI Interim Report: Interconnection Modeling and Simulation Results for Low-Temp Materials in First-Level Interconnect
05/30/2025 | iNEMIOne of the greatest challenges of integrating different types of silicon, memory, and other extended processing units (XPUs) in a single package is in attaching these various types of chips in a reliable way.
AI Helps Build Smarter, More Resilient Power Grids
05/16/2025 | BUSINESS WIREAs society’s reliance on electricity deepens, artificial intelligence (AI) is reshaping how we manage power grids and optimize energy sources.