Recently, DeepSeek released a comprehensive research paper on the best end-to-end techniques for large model training, drawing significant attention from the industry. The paper thoroughly outlines DeepSeek's technological breakthroughs in large model development, covering software, hardware, and hybrid optimization solutions, showcasing its remarkable engineering depth.
In the **software** aspect, the paper provides detailed introductions to Multi-Head Latent Attention (MLA), which significantly reduces memory usage during the inference process; FP8 mixed precision training enhances efficiency through low-precision calculations while maintaining numerical stability; DeepEP communication library optimizes Expert Parallelism (EP) communication, supports FP8 low-precision operations, and accelerates MoE model training and inference; LogFMT logarithmic floating-point format further optimizes computational efficiency by homogenizing activation distribution.
In terms of **hardware**, DeepSeek adopts the Multi-Rail Fat Tree network topology combined with Ethernet RoCE switches, greatly enhancing cluster network performance, reducing communication overhead, and ensuring efficient large-scale training.
**Hybrid Optimization** includes IBGDA (InfiniBand-based Group Data Aggregation), which uses efficient communication kernels to alleviate bottlenecks in cross-node MoE training; 3FS (Fire-Flyer File System) fully utilizes modern SSDs and RDMA network bandwidth to optimize data access efficiency, providing strong support for AI high-performance computing.
By co-designing algorithms, frameworks, and hardware, DeepSeek overcomes bottlenecks related to memory capacity, computational efficiency, and interconnect bandwidth, significantly reducing training costs. Its V3 model was trained using 2048 NVIDIA H800 GPUs, requiring only 2,788,000 GPU hours, matching the performance of top-tier closed-source models, highlighting the enormous potential of open-source AI.
This paper not only demonstrates DeepSeek's leading position in technological innovation but also provides valuable reference for the global AI community, advancing the efficiency and accessibility of large model training. DeepSeek’s open collaborative spirit and engineering capabilities are driving AI technology to new heights.
Paper link: https://www.alphaxiv.org/abs/2505.09343