The article discusses DeepSeek's approach to designing and training a large-scale MoE model called DeepSeek-V3 on NVIDIA H800 GPUs. Key strategies include hardware-aware parallelization techniques (avoiding Tensor Parallelism, enhancing Pipeline Parallelism, and accelerating Expert Parallelism), node-aware routing for efficient communication, and the use of FP8 mixed-precision training to reduce computational costs while maintaining model quality. The network infrastructure leverages a Multi-Plane Fat-Tree architecture with 400G Infiniband switches to support up to 16,384 GPUs in theory, although regulatory constraints limited deployment to over two thousand GPUs. Future hardware directions aim at integrating intra-node and inter-node communication into a unified framework to optimize bandwidth utilization and reduce software complexity.
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