成本优势将造就AI产品中的“王者”

Core Viewpoint - The control of AI product costs can be achieved through optimal parameter selection, hardware combination, data preprocessing, small sample learning, and the use of automation tools. As AI products become increasingly commercialized, the importance of cost will become more apparent [1][6]. Cost Control Strategies - AI product cost control can be approached from four main aspects: 1. Adjusting model parameters based on application scenarios to reduce model complexity and costs through techniques like pruning, distillation, and quantization [1]. 2. Selecting and optimizing hardware combinations to lower costs by utilizing not only GPUs but also CPUs and storage devices to share the computational load [3]. 3. Preprocessing training data and employing small sample learning methods to reduce the amount of training data and computational power required, thus lowering costs [4]. 4. Utilizing automation tools in model training, deployment, and operation to minimize manual and trial-and-error costs, enhancing efficiency [5]. Market Implications - The emergence of DeepSeek, with its high performance and low cost, has disrupted the AI industry, leading to significant market shifts and the evaporation of substantial market value for competitors like Nvidia. This highlights the potential for cost-effective AI products to gain market acceptance and competitive advantage [1][6].