Core Insights - DeepSeek company has released the DeepSeek-V3.2-Exp model, introducing a sparse attention mechanism that significantly reduces computational resource consumption and enhances inference efficiency [1] - The new model has led to a price reduction of API services by 50% to 75% [1] - The release has prompted immediate recognition and adaptation from several domestic chip manufacturers, indicating a growing synergy within the domestic AI hardware and software ecosystem [1][2] Group 1: Model Release and Features - The DeepSeek-V3.2-Exp model incorporates the DeepSeek Sparse Attention mechanism, optimizing training and inference efficiency for long texts [5] - The model is compatible with CUDA and utilizes TileLang for rapid prototyping, aiming for higher efficiency through lower-level language implementations [5][6] - The release of V3.2-Exp marks a significant shift from the previous version, V3.1, which did not receive any proactive recognition from companies regarding the "UE8M0 floating-point format" [4][5] Group 2: Industry Response and Ecosystem Development - Within four minutes of the model's release, Cambricon announced its adaptation of the DeepSeek-V3.2-Exp model and open-sourced its large model inference engine [2] - Huawei and Haiguang also quickly followed suit, demonstrating the rapid response from the domestic chip industry to the new model [2] - The consensus within the domestic AI industry regarding the DeepSeek model has empowered the company to take the lead in defining standards for domestic chips [3][4] Group 3: Competitive Landscape - The rapid development of the domestic chip ecosystem is highlighted by the swift adaptation of major players like Tencent and Alibaba, who are actively integrating domestic chips into their cloud computing services [6] - Experts believe that the emergence of DeepSeek has accelerated the pace of domestic chip development, with expectations for significant advancements by 2025 [3]