In a recent investor call, Xin Kai Pu Company revealed the latest evaluation results of its self-developed Xingpu large language model. The model, trained using Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), achieved comparable intelligent reasoning performance to DeepSeek-R1, while consuming only 1/20th of the computing power. This achievement not only demonstrates Xin Kai Pu's R&D strength in the field of artificial intelligence but also offers the potential for significantly reduced hardware investment.
The success of the Xingpu large language model lies not only in its improved intelligent reasoning capabilities but also in its highly efficient computing power consumption. This advancement means that the hardware investment required for deploying such models is greatly reduced, allowing companies to allocate saved computing resources to software development and the expansion of intelligent applications. Xin Kai Pu stated that it will participate in industry evaluations in the future to obtain more authoritative data support and further demonstrate the market competitiveness of the Xingpu large language model.
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Meanwhile, industry demand for computing power continues to surge, and companies seeking more efficient solutions often face high hardware costs. Xin Kai Pu's achievement is timely, providing a new option for many companies. By reducing hardware investment for computing power while maintaining user experience and service accuracy, it helps improve overall operational efficiency and economic benefits.
Overall, the test results of the Xin Kai Pu Xingpu large language model will bring more innovation and competition to the industry, helping companies achieve greater breakthroughs in their digital transformation journeys. With continuous technological advancements and maturation, the application of AI models will become increasingly widespread in the future, driving the intelligent development of various industries.