人工智能系列报告（九）、算力系列报告（二）：TileLang：中国的CUDA和Triton

Investment Rating - The industry investment rating is "Overweight" [7] Core Insights - CUDA has developed a significant competitive advantage for NVIDIA in high-performance computing and AI applications over nearly two decades, with enhancements like NVLink and mixed-precision training [12][18] - Triton, introduced by Philippe Tillet, automates low-level optimizations for GPU programming, significantly reducing the development burden for AI applications [19][23] - TileLang, developed by Peking University, aims to bridge the compatibility gap between domestic AI chips and established platforms like CUDA and Triton, potentially lowering development costs and accelerating commercialization [29][36] Summary by Sections Section 1: High-Performance Computing as the Foundation for Generative AI - CUDA has been pivotal in establishing NVIDIA's moat by enabling GPUs to handle parallel computing tasks essential for AI [12][18] - The introduction of Tensor Cores and mixed-precision training has drastically improved matrix computation speeds [14][18] Section 2: TileLang as a Potential Solution for Domestic AI Chips - Domestic AI chip manufacturers face challenges in software compatibility and toolchain maturity compared to NVIDIA's CUDA platform [28] - TileLang, set to be open-sourced in January 2025, utilizes tiling techniques to optimize memory and scheduling, potentially enhancing the performance of AI operators [29][32] - TileLang could effectively address the compatibility issues between leading AI chip companies and domestic platforms, facilitating broader adoption [36] Section 3: Investment Opportunities - Recommended companies to watch include AI inference chip manufacturers like Cambricon and Haiguang Information [37] - Notable server companies include Inspur Information, Zhongke Shuguang, Huqin Technology, and Digital China [37]