DeepSeek: Paradigm Shifts and Technical Evolution in Large AI Models

TL;DR

This paper reviews the evolution of large AI models, introduces DeepSeek's novel algorithms and engineering breakthroughs, and analyzes their impact on the AI landscape, highlighting future development trends.

Contribution

It presents new algorithms, engineering innovations, and a comprehensive analysis of DeepSeek's models, marking a significant advancement in large AI model development.

Findings

DeepSeek models achieve high performance at low cost.

DeepSeek's algorithms improve model efficiency and scalability.

The paper forecasts future trends in AI model development.

Abstract

DeepSeek, a Chinese Artificial Intelligence (AI) startup, has released their V3 and R1 series models, which attracted global attention due to their low cost, high performance, and open-source advantages. This paper begins by reviewing the evolution of large AI models focusing on paradigm shifts, the mainstream Large Language Model (LLM) paradigm, and the DeepSeek paradigm. Subsequently, the paper highlights novel algorithms introduced by DeepSeek, including Multi-head Latent Attention (MLA), Mixture-of-Experts (MoE), Multi-Token Prediction (MTP), and Group Relative Policy Optimization (GRPO). The paper then explores DeepSeek engineering breakthroughs in LLM scaling, training, inference, and system-level optimization architecture. Moreover, the impact of DeepSeek models on the competitive AI landscape is analyzed, comparing them to mainstream LLMs across various fields. Finally, the paper reflects on the insights gained from DeepSeek innovations and discusses future trends in the technical and engineering development of large AI models, particularly in data, training, and reasoning.