New Insights in Digital Marketing: What Is DeepSeek? A New Chapter in the AI Era May Be Unfolding

In today’s era of rapid technological advancement, artificial intelligence (AI) has permeated nearly every aspect of our lives — from smart homes to autonomous driving, from conversational agents to medical diagnostics. AI technologies are transforming the world at an unprecedented pace.

Amid this technological revolution, DeepSeek, a Chinese AI company founded in 2023, has drawn significant attention for its bold vision of Artificial General Intelligence (AGI) and its cuttingedge technological capabilities.

So, what exactly is DeepSeek? And does it truly have the potential to open a new chapter in the AI age?

What Is DeepSeek?

DeepSeek is an intelligent search and analytics system built upon deep learning and data mining technologies, independently developed by DeepSeek Inc., a Chinese AI firm. It employs deep neural networks (DNNs) to model data, automatically extract key features, and interpret complex interrelationships among datasets.

This system is particularly wellsuited for handling unstructured data such as text, images, and audio. By understanding user intent, contextual information, and multimodal inputs, DeepSeek delivers precise, efficient, and personalized search and recommendation results. Demonstrating vast potential across multiple sectors, it not only enables intelligent data retrieval and analysis but also offers customized solutions based on user needs and preferences.

At its core, DeepSeek is powered by a large language model (LLM) — conceptually similar to OpenAI’s GPT or Google’s BERT — but with a stronger focus on realizing AGI, enabling AI to become more generalized and intelligent. To achieve this, DeepSeek integrates several advanced techniques that optimize memory efficiency, computational speed, and scalability for complex tasks:

Multihead Latent Attention (MLA): Utilizes lowrank factorization to minimize memory requirements when processing massive datasets, improving both speed and efficiency.

MoE (Mixture of Experts) Architecture: Dynamically activates only relevant expert modules during task execution, significantly boosting processing efficiency.

FP8 MixedPrecision Training Framework: Compared to traditional FP16 or FP32 formats, FP8 reduces memory usage and accelerates both training and inference.

DualPipe Technology: Enhances data transfer between GPUs, minimizing latency and improving overall system throughput.

DeepSeek V3 vs. DeepSeek R1

At the end of 2024, DeepSeek introduced its nextgeneration large language models, DeepSeekR1 and DeepSeekV3, followed by the release of the DeepSeekR1 chatbot in January 2025. Although these models share the same lineage, they differ significantly in positioning, architecture, performance, and application scenarios.

1. Model Positioning and Core Capabilities


2. Architectural and Technical Differences

• Architecture Design

DeepSeekV3: Employs a Mixture of Experts (MoE) framework combined with Multihead Latent Attention (MLA). Through an intelligent routing system, it dynamically activates specialized expert modules (e.g., programming or summarization experts) to improve computational efficiency.

DeepSeekR1: Builds upon the V3 architecture with further optimizations, incorporating dynamic gating mechanisms and a reinforcement learning framework (e.g., GRPO algorithm) to refine expert activation strategies for reasoningintensive tasks.

• Training Methodology

V3: Follows the traditional pretraining and finetuning paradigm, integrating FP8 mixedprecision training and parallel optimization to achieve training costs as low as 1/20 of GPT4.

R1: Relies entirely on reinforcement learning (RL), using largescale RL and coldstart techniques to enhance reasoning capabilities while minimizing dependence on labeled datasets.

3. Performance and Benchmark Comparison


4. Application Deployment and Ecosystem Support


Conclusion

Both DeepSeekV3 and DeepSeekR1 reduce operational costs through algorithmic optimization while advancing the opensource AI ecosystem and promoting broader accessibility of AI technologies.

DeepSeekV3 is designed for general NLP and multimodal tasks (e.g., text generation, visuallanguage processing), emphasizing costeffectiveness and compatibility.

DeepSeekR1, in contrast, is tailored for complex logical reasoning (e.g., mathematical proofs, code generation) and lightweight local deployment, allowing users to flexibly integrate the two models according to specific needs.

To be continued…