Core Insights - The event highlighted the collaboration among leading domestic computing chip companies and the launch of the new multi-modal reasoning model Step 3 by Jumpshare Star, showcasing the strong adaptability of domestic chips [3][5][12] - The establishment of the "Model-Chip Ecological Innovation Alliance" aims to synchronize product development among hardware manufacturers and enhance strategic cooperation [12][19] - Jumpshare Star's revenue guidance for the year is projected to reach 1 billion yuan, indicating a strong market position compared to competitors [13][14] Group 1: Model and Chip Integration - The Step 3 model demonstrates a 300% inference efficiency improvement on domestic chips compared to DeepSeek-R1, and over 70% improvement in distributed inference on NVIDIA Hopper architecture [6][8] - Jumpshare Star's approach integrates model development with hardware characteristics from the outset, addressing the inefficiencies of traditional development cycles [8][9] - The new multi-matrix factorization attention (MFA) architecture significantly reduces key-value cache usage by 93.7%, making it more compatible with domestic chips [11] Group 2: Market Position and Strategy - Jumpshare Star has released over ten multi-modal models in the past year, positioning itself favorably in a market where multi-modal applications are increasingly sought after [15][16] - The company has established significant partnerships with leading domestic smartphone manufacturers and automotive companies, enhancing its market reach [16] - The rapid application of multi-modal models is expected to create a feedback loop that drives further model improvements [16] Group 3: Shanghai's Role in AI Development - Shanghai hosts a significant number of AI companies, with 24,733 registered AI enterprises in 2024, reflecting a 5.1% growth from the previous year [18] - The city benefits from a robust industrial ecosystem, including major wafer fabs and advanced packaging capabilities, which support GPU companies [18][19] - Shanghai's state-owned capital is actively investing in AI startups, indicating strong governmental support for the industry [18]

这周一，开源阵营又迎来一个重磅玩家 —— 华为盘古。 这次，这个新玩家一口气宣布了两个大模型的开源 ——70 亿参数的稠密模型 「 盘古 Emb edded 」和 720 亿参数的混合专家模型「 盘古 Pro MoE 」，甚至连基 于昇腾的模型推理技术也一并开源了。 | pangu-pro-moe | ☆ 108 | pangu-embedded | 公 37 | | --- | --- | --- | --- | | 盘古 Pro MoE (72B-A16B): 昇腾原生的分组混合专家模型 | | 盘古 Embedded (7B)：灵活切换快慢思考的高效7B模型 | | | ‌ ‌ ‌ | | ☆ 37 ￥ 4 | | | ascend-inference-cluster | ☆ 115 | ascend-inference-system | △ 40 | | 昇腾超大规模MoE模型推理部署技术分享 | | 异腾盘古推理系统技术 | | | ☆ 115 ￥ 22 | | · Python ⭐ 40 ዓ° 6 | | 综合来看，这两个大模型都不是「等闲之辈」：在 SuperCLUE 5 月榜单上，盘古 Pro ...