The Technological Innovations of DeepSeek

The technological innovations of DeepSeek are reflected in the following aspects:

Innovations in Model Architecture

Adoption of Transformer Architecture: Based on the Transformer architecture, a deep neural network is constructed using the attention mechanism. This enables it to handle the dependency relationships in long — sequence data effectively, providing a powerful foundation for tasks such as natural language processing.

Introduction of MLA Architecture: The Multi — Head Latent Attention (MLA) mechanism is adopted. Through the low — rank joint compression mechanism, the Key — Value matrix is compressed into low — dimensional latent vectors, reducing memory usage and significantly decreasing the computational load and inference video memory.

Mixture of Experts (MoE) Architecture: The MoE architecture is utilized. By implementing a dynamic redundancy strategy, the optimal load balance is maintained, significantly reducing the computational cost. Based on the adoption of the MoE architecture, a load — balancing strategy without auxiliary loss is employed to minimize the performance degradation caused by encouraging load balancing.

Innovations in Training Methods

Multi — Token Prediction Objectives: It supports multi — token prediction objectives. This is not only beneficial to the model performance but can also be used to accelerate inference, improving the efficiency of both model training and inference.

FP8 Mixed — Precision Training Framework: An FP8 mixed — precision training framework has been designed. For the first time, the feasibility and effectiveness of FP8 training on extremely large — scale models have been verified. This reduces the demand for computing resources and improves the training efficiency.

Parallel Training Approaches: Training is carried out based on an efficient and lightweight framework. Approaches such as 16 — way zero — bubble pipeline parallelism, 8 — way expert parallelism, and ZeRO — 1 data parallelism are adopted to accelerate the training speed and handle large — scale data.

Innovations in Distillation Technology

Combination of Data and Model Distillation: A powerful teacher model is used for data augmentation, pseudo — label generation, and optimization of data distribution to generate diverse training samples. Supervised Fine — Tuning (SFT) is adopted to transfer the knowledge of the teacher model to the student model without involving an additional Reinforcement Learning (RL) stage, enhancing the distillation efficiency.

Efficient Knowledge Transfer Strategies: Feature — based distillation is employed to transfer the feature information of the middle layers of the teacher model to the student model. For different specific tasks, task — specific distillation is used to optimize the distillation process, enabling the student model to focus more on learning the key knowledge of specific tasks.

Other Technological Innovations

Interpretability of the Inference Process: For the first time, the interpretability of the inference process has been achieved. Users can trace its logical chain, enhancing the trust in the model and evolving AI from a “tool” to a “partner”.

Model Compression Technology: Through model compression technology, the dependence on computing power chips has been broken. This enables high — performance operation of AI applications under limited hardware conditions, improves the algorithm efficiency, and overcomes hardware limitations.

Open — Source Framework and Ecosystem Construction: By building an open — source framework and toolchain, a developer ecosystem is established, forming a positive cycle of “algorithm — application — data” to promote the development and application of AI technology.