Trying to understand the technical breakthroughs in Deep Seek, particularly the pre-training efficiency.
The pre-training efficiency of DeepSeek-V3 is attributed to several key innovations:
- FP8 Mixed Precision Framework: This reduces GPU memory usage and accelerates computation during training[5].
- DualPipe Algorithm: It optimizes pipeline parallelism by overlapping computation and communication, minimizing idle time and scaling efficiently across nodes[5].
- Multi-Token Prediction (MTP): This densifies training signals, improving data efficiency and model performance[3].
- Efficient Mixture-of-Experts (MoE) Architecture: Only a subset of parameters is activated per token, reducing computational overhead while maintaining performance[2][3].