刚刚，智谱和华为搞波大的：中国首个国产芯片训练出的SOTA多模态模型！

Core Viewpoint - The article highlights the launch of GLM-Image, a state-of-the-art (SOTA) multimodal model developed by Zhipu AI in collaboration with Huawei, which is notable for being trained entirely on domestic chips and excelling in text rendering capabilities [1][36]. Group 1: Model Performance - GLM-Image achieved first place in both the CVTG-2K (Complex Visual Text Generation) and LongText-Bench (Long Text Rendering) benchmarks, demonstrating superior performance with a word accuracy of 0.9116 and a normalized edit distance (NED) of 0.9557 [5][6]. - In the LongText-Bench, GLM-Image ranked first among open-source models in both Chinese and English scores, indicating its versatility and effectiveness in handling different languages [6]. Group 2: Cost Efficiency - The cost of generating an image using GLM-Image's API is only 0.1 yuan (approximately 0.014 USD), making it an affordable option for users [7][21]. - This low cost positions GLM-Image as a competitive choice for businesses and developers looking to integrate AI image generation capabilities [60]. Group 3: Technical Innovation - GLM-Image employs a hybrid architecture combining autoregressive and diffusion models, allowing it to understand complex prompts and generate high-quality images effectively [38][40]. - The model was trained on Huawei's Ascend A2 chips, showcasing the potential of domestic computing power in supporting advanced AI models [44][48]. - The training process included optimizations for reinforcement learning (RL) to ensure stability and efficiency, which is critical for handling large-scale models [51]. Group 4: Market Impact - GLM-Image represents a significant advancement in the domestic AI landscape, challenging the dominance of foreign models and proving that high-performance models can be developed using local resources [57][60]. - The open-source nature of GLM-Image, along with its innovative architecture, provides valuable resources for researchers and developers in the field of image generation [59][60].