This document details the architecture, training methodology, and performance of DeepSeek-V3, an advanced language model emphasizing cost-effective training and efficient inference. The model uses a combination of Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, along with an auxiliary-loss-free load balancing strategy to enhance specialization and performance. A significant focus is placed on training efficiency through an FP8 mixed precision framework utilizing fine-grained quantization and a novel pipeline parallelism algorithm called DualPipe to fully overlap computation and communication. The results demonstrate that DeepSeek-V3 achieves state-of-the-art open-source performance in areas like code and math, exhibiting capabilities comparable to leading closed-source models despite its economical training cost of approximately $5.576 million. Finally, the paper concludes with hardware design suggestions based on the efficiency challenges encountered during its large-scale deployment