Recently, the DeepSeek team released a technical paper about their latest model, DeepSeek-V3, focusing on the "scaling challenges" encountered during large-scale AI model training and related considerations regarding hardware architecture. This 14-page paper not only summarizes the experiences and lessons learned during the development of V3 but also provides profound insights for future hardware design. Notably, the CEO of DeepSeek, Liang Wenfeng, also participated in writing this paper.
Paper link: https://arxiv.org/pdf/2505.09343
This study shows that the rapid expansion of current large language models (LLMs) has exposed many limitations of existing hardware architectures, such as memory capacity, computational efficiency, and interconnection bandwidth. DeepSeek-V3 was trained on a cluster of 2048 NVIDIA H800 GPUs, overcoming these limitations through effective hardware-aware model design, enabling cost-effective large-scale training and inference.
The paper highlights several key points. First, DeepSeek-V3 adopts an advanced DeepSeekMoE architecture and multi-head latent attention (MLA) architecture, significantly improving memory efficiency. The MLA technique compresses key-value caches, greatly reducing memory usage so that each token only requires 70KB of memory, which is significantly less than other models.
Second, DeepSeek also achieved cost-effective optimization. By implementing its mixture-of-experts (MoE) architecture, DeepSeek-V3 significantly reduced the number of activated parameters, lowering the training cost by an order of magnitude compared to traditional dense models. Additionally, the model optimized inference speed by adopting a double micro-batch overlapping architecture to maximize throughput, ensuring full utilization of GPU resources.
DeepSeek proposed innovative ideas for future hardware design. They suggest addressing the three major challenges of memory efficiency, cost-effectiveness, and inference speed in LLMs through joint optimization of hardware and model architecture. This provides valuable reference for the development of future AI systems.