Yunhe Partners

Qingna Technology is a company specializing in the research, development, production, and sale of sodium-ion batteries. It primarily serves the electric vehicle industry, offering energy storage solutions for data centers as well. The company is committed to providing clients with high-quality, industry-standard battery products and solutions.

Zhongxin New Materials is a developer of new energy material recycling technology and is committed to the development and research of new energy material recycling technology.

Vbao is a provider of asset management and trading solutions specifically tailored for insurance companies. The company develops various platforms focused on enhancing business productivity and operational efficiency. Its core offerings include an insurance premium management platform, investment and trading software, and an insurance account management system. These products are designed to deliver integrated, reliable, and efficient transaction and account management solutions, catering to the unique needs of the insurance sector. By focusing on these specialized tools, Vbao aims to streamline processes and improve performance for its clients in the insurance industry.

Enflame, also known as Suiyuan Technology, specializes in developing advanced neural network solutions for artificial intelligence applications. The company focuses on creating high-end deep learning chips designed specifically for cloud data centers, optimizing them for AI training platforms. These chips feature a proprietary architecture that delivers significant computational power while ensuring high energy efficiency and low costs. Additionally, they are programmable and compatible with mainstream machine learning frameworks, making them suitable for a wide range of AI training and optimization tasks.

Enflame, also known as Suiyuan Technology, specializes in developing advanced neural network solutions for artificial intelligence applications. The company focuses on creating high-end deep learning chips designed specifically for cloud data centers, optimizing them for AI training platforms. These chips feature a proprietary architecture that delivers significant computational power while ensuring high energy efficiency and low costs. Additionally, they are programmable and compatible with mainstream machine learning frameworks, making them suitable for a wide range of AI training and optimization tasks.