Core Viewpoint - DeepSeek's announcement of its new model DeepSeek-V3.1 utilizing UE8M0 FP8 Scale parameter precision has sparked significant interest in the capital market, leading to a surge in stock prices of chip companies like Cambrian. However, industry insiders express a more cautious outlook regarding the practical value and challenges of FP8 in model training and inference [1][4]. Group 1: DeepSeek's Impact on Capital Market - The launch of DeepSeek-V3.1 has led to a strong reaction in the capital market, with stock prices of chip companies rising sharply [1]. - The industry response at the 2025 Computing Power Conference was more subdued, focusing on the actual value and challenges of FP8 rather than the excitement seen in the capital market [1]. Group 2: Understanding FP8 - FP8 is a lower precision format that reduces data width to 8 bits, enhancing computational efficiency compared to previous formats like FP32 and FP16 [2]. - The direct advantages of FP8 include doubling computational efficiency and reducing network bandwidth requirements during training and inference, allowing for larger models to be trained or shorter training times under the same power consumption [2]. Group 3: Limitations of FP8 - While FP8 offers speed advantages, it can lead to calculation errors due to its limited numerical range, necessitating a mixed precision training approach to balance efficiency and accuracy [3]. - Different calculations have varying precision requirements, with some operations being more tolerant of lower precision [3]. Group 4: Future of DeepSeek and FP8 Standards - DeepSeek's use of FP8 is seen as a signal that domestic AI chips are entering a new phase, providing opportunities for local computing power manufacturers [4]. - The industry acknowledges that while FP8 represents a step towards computational optimization, it is not a panacea, and the actual implementation results are crucial [4]. - The transition to FP8 may require an upgrade across the entire domestic computing ecosystem, including chips, frameworks, and applications [4]. Group 5: Challenges in Large Model Training - The core bottlenecks in large model training and inference include not only computational scale but also energy consumption, stability, and cluster utilization [5]. - There is a need for advancements from simple hardware stacking to more efficient single-card performance and optimized cluster scheduling to meet growing demands [5].

Core Viewpoint - DeepSeek's announcement of its new model DeepSeek-V3.1 utilizing UE8M0 FP8 Scale parameter precision has sparked significant interest in the capital market, leading to a surge in stock prices of chip companies like Cambrian [1] Group 1: FP8 Technology - FP8 is a lower precision standard that enhances computational efficiency, allowing for a doubling of computational power and reducing network bandwidth requirements during AI training and inference [2] - The transition from FP32 to FP16 and now to FP8 reflects a broader industry trend towards optimizing computational resources while maintaining model performance [4] Group 2: Industry Reactions - Despite the positive market reaction, industry experts express caution regarding the practical implications of FP8, emphasizing that it is not a one-size-fits-all solution and that mixed precision training is often necessary to balance efficiency and accuracy [3][4] - The adoption of FP8 by DeepSeek is seen as a potential catalyst for setting new standards in large model training and inference, although the actual implementation and effectiveness remain to be seen [4] Group 3: Ecosystem Upgrades - The shift to FP8 necessitates a comprehensive upgrade of the domestic computing ecosystem, including chips, frameworks, and application layers, to ensure compatibility and optimization across the supply chain [5] - Addressing core bottlenecks in large model training, such as energy consumption, stability, and cluster utilization, is crucial for advancing the capabilities of domestic computing clusters [5]