Paper Link 👁️ 1. Introduction We present DeepSeek V3, a strong Mixture of Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost effective training, DeepSeek V3 adopts Multi head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek V2. Furthermore, DeepSeek V3 pioneers an auxiliary loss free strategy for load balancing and sets a multi token prediction training objective for stronger performance. We pre train DeepSeek V3 on 14.8 trillion diverse and high quality tokens, followed by Supervised Fine Tuning and Reinforcement Learning stages to fully harness its capabilities. Comprehensive evaluations reveal that DeepSeek V3 outperforms other open source models and achieves performance comparable to leading closed source models. Despite its excellent performance, DeepSeek V3 requires only 2.788M H800 GPU hours for its full training. In addition, its training process is remarkably stable. Throughout the entire training process, we did not experience any irrecoverable loss spikes or perform any rollbacks. 2. Model Summary Architecture: Innovative Load Balancing Strate…
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