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 - The release of GLM-4.6 by Zhipu marks a significant advancement in large model capabilities, particularly in Agentic Coding and other core functionalities [1] - GLM-4.6 has achieved comprehensive alignment with Claude Sonnet4 in code generation, establishing itself as the strongest coding model in China [1] - The model has undergone extensive upgrades in long context processing, reasoning, information retrieval, text generation, and agent applications, surpassing the performance of DeepSeek-V3.2-Exp [1] - As an open-source model, GLM-4.6 is one of the strongest general-purpose large models globally, enhancing the position of domestic large models in the global competitive landscape [1] Technological Developments - Zhipu has implemented FP8+Int4 mixed-precision quantization inference deployment on leading domestic AI chips from Cambrian, marking the first production of an FP8+Int4 model-chip integrated solution on domestic chips [1] - This solution significantly reduces inference costs while maintaining model accuracy, providing a feasible path for local operation of large models on domestic chips [1] - Moore Threads has adapted GLM-4.6 based on the vLLM inference framework, demonstrating the advantages of the MUSA architecture and full-featured GPUs in ecological compatibility and rapid adaptation [2] Industry Implications - The collaboration between Cambrian and Moore Threads signifies that domestic GPUs are now capable of iterating in synergy with cutting-edge large models, accelerating the construction of a self-controlled AI technology ecosystem [2] - The combination of GLM-4.6 and domestic chips will initially be offered to enterprises and the public through the Zhipu MaaS platform, unlocking broader social and industrial value [2] - The deep collaboration between domestically developed GLM series large models and domestic chips will continue to drive dual optimization of performance and efficiency in model training and inference, fostering a more open, controllable, and efficient AI infrastructure [2]

Core Insights - The domestic large model key enterprise, Zhipu, has officially released and open-sourced its next-generation large model GLM-4.6, achieving significant advancements in core capabilities such as Agentic Coding [1] - This release follows the major technology launches of DeepSeek-V3.2-Exp and Claude Sonnet4.5, marking another significant development in the industry before the National Day holiday [1] - Zhipu announced that GLM-4.6 has been deployed on leading domestic AI chips from Cambrian using FP8+Int4 mixed-precision quantization inference, representing the first production of an FP8+Int4 model-chip integrated solution on domestic chips [1] - Additionally, Moore Threads has completed the adaptation of GLM-4.6 based on the vLLM inference framework, allowing the new generation of GPUs to stably run the model at native FP8 precision [1]

Core Insights - The article highlights the official release and open-sourcing of the new generation large model GLM-4.6 by Zhiyuan on September 30, showcasing significant improvements in core capabilities such as Agentic Coding and code generation, aligning with Claude Sonnet 4 [1] Group 1: Product Development - GLM-4.6 has achieved substantial enhancements in its core functionalities, particularly in code generation capabilities [1] - The model has been deployed on domestic AI chips from Cambrian, utilizing a mixed quantization inference solution of FP8+Int4, marking the first instance of such a model-chip integration on domestic chips [1] Group 2: Technological Adaptation - Moore Threads has adapted GLM-4.6 using the vLLM inference framework, enabling the new generation GPU to operate stably under native FP8 precision [1]

Core Insights - The domestic large model company Zhipu has released and open-sourced its next-generation large model GLM-4.6, achieving significant advancements in core capabilities such as Agentic Coding [1] - GLM-4.6's code generation ability has fully aligned with Claude Sonnet 4, making it the strongest coding model in China, while also surpassing DeepSeek-V3.2-Exp in various aspects including long context processing and reasoning capabilities [1] - The model has been deployed on domestic AI chips with an FP8+Int4 mixed-precision inference solution, marking the first instance of such a model-chip integration on domestic chips, significantly reducing inference costs while maintaining model accuracy [1] Industry Developments - Moore Threads has adapted GLM-4.6 based on the vLLM inference framework, demonstrating the advantages of the MUSA architecture and full-featured GPUs in ecological compatibility and rapid adaptation [2] - The combination of GLM-4.6 with domestic chips will be offered through the Zhipu MaaS platform, aiming to release broader social and industrial value [2] - The deep collaboration between the domestically developed GLM series large models and domestic chips is expected to continuously enhance performance and efficiency in model training and inference, contributing to a more open, controllable, and efficient AI infrastructure [2]

Core Insights - The latest GLM-4.6 model has been launched, showcasing a 27% improvement in coding capabilities compared to its predecessor GLM-4.5, excelling in real programming, long context handling, and reasoning abilities [1] - GLM-4.6 achieved the highest domestic standard in public benchmark tests and surpassed other domestic models in 74 real programming tasks [1] - The model has been deployed on leading domestic AI chips from Cambrian using FP8+Int4 mixed-precision quantization, marking the first production of an FP8+Int4 model integrated with chip solutions on domestic chips [1] - Additionally, Moore Threads has adapted GLM-4.6 to the vLLM inference framework, enabling the new generation of GPUs to run the model stably at native FP8 precision [1]