Technical Report: https://arxiv.org/pdf/2412.19437 Github: https://github.com/deepseek-ai/DeepSe... This research paper introduces DeepSeek-V3, a 671-billion parameter Mixture-of-Experts (MoE) large language model. The paper details DeepSeek-V3's architecture, including its innovative auxiliary-loss-free load balancing strategy and Multi-Token Prediction objective, and its efficient training framework utilizing FP8 precision. Extensive evaluations demonstrate DeepSeek-V3's superior performance across various benchmarks compared to other open-source and some closed-source models, particularly in code and math tasks. The paper also discusses post-training methods like supervised fine-tuning and reinforcement learning, along with deployment strategies and hardware design suggestions. Finally, it acknowledges limitations and suggests future research directions #ai, #artificialintelligence, #arxiv, #research, #paper, #publication, #llm, #genai, #generativeai, #largevisualmodels, #largelanguagemodels, #largemultimodalmodels, #nlp, #text, #machinelearning, #ml, #nvidia, #openai, #anthropic, #microsoft, #google, #technology, #cuttingedge, #meta, #llama, #chatgpt, #gpt, #elonmusk, #samaltman, #deployment, #engineering, #scholar, #science, #apple, #samsung, #turing, #aiethics, #innovation, #futuretech, #deeplearning, #datascience, #computervision, #autonomoussystems, #robotics, #dataprivacy, #cybersecurity, #digitaltransformation, #quantumcomputing, #aiapplications, #aiethics, #techleadership, #technews, #aiinsights, #aiindustry, #aiadvancements, #futureai, #airesearchers