Core Insights - The collaboration between SenseTime and Cambricon marks a significant milestone in the development of "domestic chips + domestic models," with the successful adaptation of the Seko series model on the same day it was released [1][2] - This partnership aims to enhance the domestic AI application ecosystem, making advanced multimodal AI capabilities more accessible and cost-effective for developers and enterprises [1][3] Group 1: Technological Collaboration - Cambricon's successful adaptation of SenseTime's Seko series models on "Day 0" demonstrates the rapid response capability of domestic chips in supporting local AI firms [2] - The Seko series, which includes models like SekoIDX and SekoTalk, serves as the core technology foundation for the Seko2.0 intelligent agent [2] Group 2: Ecosystem Development - The collaboration is focused on creating a more efficient and user-friendly tiered product system, aimed at fostering innovation in cutting-edge applications [3] - SenseTime's LightX2V framework incorporates a highly compatible domestic adaptation plugin model, enabling quick adaptation to various domestic hardware [3] - The partnership will lead to further optimization efforts, enhancing model efficiency and resource utilization while lowering the barriers to using multimodal AI [3]

Core Insights - The collaboration between SenseTime and Cambricon marks a significant milestone in the development of "domestic chips + domestic models," with the successful adaptation of the Seko series model on the same day it was released [1][2] - This partnership aims to enhance the domestic AI application ecosystem, making advanced multimodal AI capabilities more accessible and cost-effective for developers and enterprises [1][3] Group 1 - Cambricon's Day 0 adaptation of SenseTime's "Riri Xin" model demonstrates the rapid response capability of domestic chips in supporting local AI manufacturers, indicating a strong collaboration within the domestic AI ecosystem [2] - The Seko series models, including SekoIDX and SekoTalk, form the core technology base for the Seko2.0 intelligent agent, showcasing the extension of the domestic AI ecosystem into more complex multimodal generation fields [2] Group 2 - The partnership focuses on creating a more efficient and user-friendly tiered product system, leveraging the LightX2V framework designed by SenseTime for rapid adaptation to various domestic hardware [3] - Innovations such as low-bit quantization, compressed communication, and sparse attention mechanisms have significantly improved inference performance by over three times, enhancing the overall efficiency and resource utilization of the models [3] - Future collaboration will involve deeper optimization efforts to lower the barriers for using multimodal AI and improve the overall user experience [3]

Core Insights - SenseTime Technology officially launched Seko 2.0, the industry's first multi-episode generative intelligent agent, leveraging its technological accumulation in generative AI and multimodal interaction [1] - The Seko series models, including SekoIDX and SekoTalk, provide a robust technical foundation for image and video generation, showcasing significant advantages in consistency for multi-episode video generation [1] - The collaboration with Cambricon (688256.SH) marks a key advancement in supporting AIGC core scenarios, facilitating a critical leap from language to multimodal capabilities [1] Group 1 - The Seko series models have been adapted to domestic AI chips, enhancing the support for visual content innovation and development within the domestic AI ecosystem [1] - The LightX2V framework is designed with a highly compatible domestic adaptation plugin model, currently supporting multiple domestic chips, including Cambricon [1] - Innovations such as low-bit quantization, compressed communication, and sparse attention mechanisms have been integrated into the Seko series models, resulting in over three times improvement in inference performance [1] Group 2 - SenseTime and Cambricon have established a strategic partnership to optimize software and hardware jointly, aiming to create an open and win-win industrial ecosystem [2] - The collaboration focuses on continuous optimization of core model capabilities, enhancing overall efficiency and response speed for multimodal generation [2] - Efforts will be made to improve computing resource utilization and cost efficiency through operator fusion and automatic tuning, allowing more enterprises to access high-performance multimodal capabilities at lower costs [2] Group 3 - The partnership aims to foster the prosperity and development of the domestic AI application ecosystem, creating more efficient and user-friendly tiered product systems [3] - The goal is to build a more open and friendly tool and ecosystem for developers, stimulating innovation in cutting-edge applications [3]