DeepSeek V3/R1 Architecture Explained: Innovations in AI Modeling

Introduction

In the rapidly evolving field of artificial intelligence, DeepSeek V3/R1 stands out as a groundbreaking model, harnessing a massive 671 billion parameters (with 37 billion activated per token) and integrating state-of-the-art components such as Multi-Head Latent Attention (MLA), a dynamic Mixture-of-Experts (MoE) mechanism, an auxiliary-loss-free load balancing strategy, and a Multi-Token Prediction (MTP) approach. This article provides an expert-level analysis of the architecture by breaking down the overall system design and a detailed view of its internal processes. Additionally, we compare its benchmark performance to industry standards, underscoring its superior efficiency and effectiveness.

Overall System Architecture Overview

This section presents a macro view of DeepSeek V3/R1, outlining how major components work together to process data from input to output.

Key Components and Process Flow

1. Input Preprocessing Module:

Function: This module is responsible for cleansing, tokenizing, and converting raw input text into standardized token embeddings.

Process:

• Raw text is ingested.