Core Viewpoint - DeepSeek's release of TileLang and CUDA operator versions represents a significant step towards achieving "independence and control" in AI foundational technology, particularly in the GPU operator development field, addressing issues of technical autonomy, domestic hardware compatibility, ecological collaboration, and innovation efficiency [2][11]. Group 1: Breaking CUDA Monopoly - The dominance of CUDA, a closed-source platform led by NVIDIA, poses risks of technological dependency for domestic developers, limiting their ability to customize operators for new model research [2][3]. - Domestic GPUs, despite improving in computational power, face high migration costs due to the lack of compatible operator libraries and development tools with CUDA [3][5]. Group 2: Lowering Barriers for Domestic Hardware - DeepSeek's open-source solution, TileLang, allows developers to quickly validate operator logic without relying on CUDA, thus reducing dependency on NVIDIA [4][6]. - The dual-version approach provides a precision baseline for domestic platforms, facilitating the verification of operator implementations and lowering debugging costs [4][6]. Group 3: Activating Open Source Community Collaboration - The success of domestic alternatives relies on ecological collaboration, where DeepSeek's open-source initiative encourages community participation in developing new operators [7][8]. - Researchers can quickly develop and share new operator prototypes using TileLang, which can then be adapted by domestic hardware manufacturers [8]. Group 4: Accelerating Domestic Research Pathways - The reliance on CUDA and its tools can hinder innovation in cutting-edge fields like large models and multi-modal research, creating an "optimization black box" [9][10]. - DeepSeek's dual-version operators provide a pathway for domestic teams to innovate without the constraints of CUDA compatibility and licensing issues [10][11]. Group 5: From Single Point Replacement to Ecological Breakthrough - DeepSeek's actions signify a shift from passive following to active construction in the domestic AI foundational technology stack, addressing the challenges of high barriers, long cycles, and adaptation difficulties in GPU operator development [11]. - The approach of using open-source to break monopolies, abstracting complexities, and fostering collaboration may become a crucial paradigm for domestic alternatives in the AI foundational technology sector [11].