Core Insights - DeepSeek v3.2 introduces a significant change by adopting TileLang, a domain-specific language for GPU kernel development, which has garnered substantial attention in the tech community [1][4][6] - TileLang is noted for its performance, allowing developers to implement attention mechanisms faster than existing solutions, with claims of achieving a 30% speed increase over Flash Attention 2 [3][5] Group 1: TileLang Overview - TileLang is designed to simplify the development of high-performance GPU/CPU kernels, comparable to NVIDIA's CUDA, and is recommended by DeepSeek for experiments due to its debugging and rapid iteration advantages [4][13] - The language is built on a Python-like syntax and operates on top of the TVM compiler infrastructure, enabling developers to focus on productivity without sacrificing performance [13] - TileLang features three programming interfaces catering to different developer skill levels, from high-level abstractions for beginners to low-level controls for performance experts [15] Group 2: DeepSeek's Adoption of TileLang - DeepSeek's collaboration with TileLang was first highlighted at the Beijing Zhiyuan Conference in June, where a report indicated that TileLang's operator implementation could be faster [6][19] - The DeepSeek team has utilized TileLang for rapid prototype development, subsequently optimizing performance with lower-level methods [17][23] - Following the release of DeepSeek v3.2, TileLang's capabilities were validated, demonstrating its effectiveness in model training [23]