DeepSeek Large Model: Core Architecture, Key Technologies, and Training Strategies

DeepSeek Appears: A New Force in the AI Wave

Amid the surge of artificial‑intelligence advancements, DeepSeek’s large model has quickly become a standout due to its unique architecture and impressive cost‑performance, attracting developers, researchers, and enterprises worldwide.

Core Architecture: Innovation‑Driven Engine

(1) Mixture‑of‑Experts (MoE): Efficiency Pioneer

DeepSeek adopts a hybrid MoE architecture that routes each input to the most suitable expert, activating only a fraction of the total parameters—e.g., DeepSeek‑V2 activates 21 billion of its 236 billion parameters per token, while DeepSeek‑V3 activates 37 billion of 671 billion—thereby reducing unnecessary computation.

(2) Transformer Architecture: Solid Foundation

The Transformer backbone provides robust sequence processing for text, speech, and other data types, with its attention mechanism allowing the model to focus on key information across long contexts, enabling strong performance in generation, QA, and translation tasks.

Key Technologies: Breaking Traditional Limits

(1) Multi‑Head Latent Attention (MLA): Long‑Text Companion

MLA compresses the key‑value matrices into low‑dimensional latent vectors, drastically lowering memory usage and enabling efficient handling of very long documents such as multi‑tens‑of‑thousands‑word papers or lengthy translations.

(2) Auxiliary‑Loss‑Free Load Balancing: Fair Scheduler

This strategy dynamically adjusts routing biases to evenly distribute workload among experts, preventing some experts from being overloaded while others remain idle, thus improving overall performance and training stability.

(3) Multi‑Token Prediction (MTP): Inference Booster

MTP combines a main model with several sequential modules, allowing the system to predict multiple future tokens at once, which speeds up generation and yields more coherent output.

(4) FP8 Mixed‑Precision Training: Cost‑Effectiveness Balance

By storing parameters in FP32 while performing many computations in FP8, training reduces memory footprint to one‑quarter and accelerates computation, cutting training time and hardware costs without sacrificing accuracy.

Model Training: Exploring Growth Paths

(1) Knowledge Distillation: Wisdom Transfer

Distillation passes the capabilities of a large model to a smaller one, enabling the compact model to achieve strong performance on benchmarks such as AIME 2024 and MATH‑500.

(2) Pure Reinforcement Learning: Trial‑and‑Error Advancement

DeepSeek‑R1‑Zero is trained solely via reinforcement learning, allowing it to iteratively improve its reasoning by receiving rewards or penalties from interaction with environments, though it may produce occasional repetitive or low‑readability outputs.

(3) Multi‑Stage Training and Cold‑Start Data: Ladder and Guide

Training proceeds through stages—from basic language learning to advanced reinforcement learning—while high‑quality cold‑start data act as a pre‑study guide, helping the model acquire human‑like reasoning styles before intensive training.

Workflow: From Input to Output

(1) Input Processing and Task Judgment: Security Check and Triage

Incoming queries are pre‑processed for errors and formatted, then routed by the MoE router to the appropriate expert based on domain (e.g., history, science) and task complexity.

(2) Invoking Appropriate Modules: Collaborative Team

Relevant expert modules handle the task—translation modules for language conversion, domain‑specific modules for analysis—and communicate to produce a cohesive result.

(3) Generating Output: Polished Product

The combined results are refined, checked for coherence, correctness, and completeness, and iteratively adjusted until the final high‑quality answer is produced.