Core Viewpoint - The article discusses the advancements in low-precision quantization strategies for AI model training, particularly focusing on the introduction of FP8 and NVFP4 formats, highlighting their implications for the development of domestic chips and large models in China [2][4][36]. Group 1: FP8 and Its Significance - FP8, or 8-bit floating point, is a low-precision data representation format that reduces storage and computational overhead while maintaining numerical stability and model accuracy compared to traditional formats like FP32 and FP16 [2][4]. - Major companies such as Microsoft, Meta, Intel, and AMD are researching FP8 training and inference, indicating a trend towards it becoming the "new gold standard" in the industry [3]. Group 2: DeepSeek's Strategy - DeepSeek's adoption of the non-mainstream FP8 quantization strategy signifies a strategic move to bind its training and scaling strategies to this precision, thereby pushing hardware and toolchains to adapt and accelerating the integration of domestic software and hardware ecosystems [4][6]. - The timing of DeepSeek's announcement coincides with NVIDIA's advancements in low-precision quantization, specifically their leap to FP4 quantization [4][5]. Group 3: NVIDIA's NVFP4 Strategy - NVIDIA's NVFP4 strategy aims to enhance training efficiency and infrastructure effectiveness, claiming to redefine large-scale model training methods [6][10]. - NVFP4 allows for significant improvements in token throughput during inference, which is crucial for unlocking the next stage of model capabilities [8][10]. Group 4: Technical Innovations in NVFP4 - NVIDIA's NVFP4 pre-training solution addresses core challenges in large-scale training, such as dynamic range and numerical stability, enabling efficient 4-bit training [13][18]. - Key technologies include micro-block scaling for numerical representation, high-precision block encoding for scaling factors, and tensor distribution reshaping to accommodate low-precision formats [18][19][20]. Group 5: Performance and Validation - Experiments on a 12 billion parameter model demonstrated that NVFP4 can support trillion-token scale pre-training while maintaining stable convergence, comparable to FP8 [26][30]. - The accuracy of NVFP4 in various downstream tasks was found to be on par with FP8, showcasing its effectiveness in large language model training [31]. Group 6: Future Implications - NVFP4 is positioned to set new benchmarks for speed, efficiency, and purposeful innovation in AI training, paving the way for a more sustainable and expansive AI factory [36].