How DeepSeek Contributes To The AI Application Popularity?

Recently, DeepSeek— a China-based AI startup— introduced a groundbreaking AI model that delivers excellent performance, fast response times, and cost-efficient development compared to major models such as OpenAI, Meta, and Gemini.

DeepSeek’s introduction contributes to the widespread adoption of AI applications across various sectors, including individuals, businesses, and government agencies. Alongside other advanced AI models, DeepSeek is accelerating the accessibility and practicality of Generative AI, proving its potential to drive transformative value in digital transformation.”

Curious about DeepSeek and its potential impact on the future of the AI landscape? Let’s explore more below!

WHAT IS DEEPSEEK? 

DeepSeek is a Chinese artificial intelligence (AI) company specializing in developing open-source large language models (LLMs). Founded in July 2023 by Liang Wenfeng, who also serves as its CEO, the company is based in Hangzhou, Zhejiang, China, and is owned and funded by the Chinese hedge fund High-Flyer. 

WHAT IS THE DEEPSEEK R1 MODEL?

DeepSeek’s flagship model, DeepSeek-R1, was launched on January 20, 2025. Notably, the company claims that it trained R1 for approximately US$6 million, significantly lower than the reported $100 million cost for training OpenAI’s GPT-4 in 2023. Additionally, DeepSeek-R1 required about one-tenth of the computing power used for Meta’s comparable model, LLaMA 3.1. 

Since its release, DeepSeek-R1 has rapidly gained popularity, surpassing OpenAI’s ChatGPT as the highest-rated free AI app in various app stores. This swift ascent has had notable market implications, including a decline in the stock prices of Western chip manufacturers like NVIDIA. In addition, DeepSeek also publicly releases the DeepSeek V3 and DeepSeek R1 models under the MIT license, allowing users to download and utilize the technology freely, even for commercial purposes.

Core Architectural Innovations

DeepSeek-R1’s architecture is built on three pillars: Mixture of Experts (MoE), Multi-Head Latent Attention (MLA), and advanced transformer optimizations.

Mixture of Experts (MoE) 

Think of Mixture of Experts (MoE) as a team of specialists working together. Instead of one AI model handling everything, MoE splits the job among different “expert” mini-models, each trained for a specific task. A smart selector (gating mechanism) decides which experts to use based on the problem at hand. DeepSeek-R1 utilizes MoE to leverage its processing capabilities as follows: 

• Dynamic Parameter Activation: With 671B total parameters, only 37B (5.5%) are activated per input, drastically reducing computational overhead.
• Load Balancing: A dynamic gating mechanism selects relevant experts for each task, preventing bottlenecks via a load-balancing loss function.

Multi-Head Latent Attention (MLA)

Multi-head latent Attention (MLA) helps AI understand and remember information better by compressing key details and keeping important data in memory while ignoring less useful parts. MLA is implemented in DeepSeek through: 

• Efficient Compression: Replaces traditional attention’s full Key-Value (KV) matrices with low-rank latent vectors, slashing memory usage to 5–13% of conventional methods.
• Rotary Position Embeddings (RoPE): Integrates positional encoding directly into attention heads, enhancing long-context comprehension (up to 128K tokens).

Transformer Optimizations

Transformer optimization is all about making AI models faster, smarter, and more efficient so they can handle tasks like language processing, image generation, and decision-making without wasting time and resources. DeepSeek R-1’s performance is optimized within the following aspects: 

• Hybrid Attention: Combines global (long-range) and local (context-specific) attention for balanced efficiency.
• Token Merging/Inflation: Reduces redundant tokens during processing and restores critical details later, improving throughput.

Training Methodology

DeepSeek-R1’s training pipeline emphasizes reinforcement learning (RL) and distillation, bypassing traditional supervised fine-tuning (SFT) limitations. The Four-Phase Training Pipeline of DeepSeek-R1 includes: 

Cold-Start Fine-Tuning: Initializes with a small dataset (~5,000 curated chain-of-thought examples) to stabilize reasoning.

Reasoning RL: Uses Group Relative Policy Optimization (GRPO), a rule-based RL method rewarding accuracy and format compliance (e.g., code correctness, structured outputs). The LLM’s policy during text generation is represented as a probability distribution of each token. This model is called a policy model, in which each token is generated based on the prompt and the previously generated tokens.

By training the policy model (LLM) to maximize the cumulative reward, the LLM can update the weights in its neural network to generate higher-quality answers that are better aligned with human expectations. This is how the LLM is trained using reinforcement learning. This method is commonly called RLHF (Reinforcement Learning from Human Feedback).

DeepSeek R1 uses a reinforcement learning method called GRPO (Group Relative Policy Optimization). GRPO does not use a value model like PPO (Proximal Policy Optimization) but calculates the average reward for many different answers generated from a prompt. As a result, GRPO is much more efficient than PPO and significantly reduces computational costs. 

Rejection Sampling: Filters low-quality outputs using DeepSeek-V3 as a reward model, retaining only high-accuracy responses.

Diverse RL: Aligns the model with human preferences using safety constraints and domain-specific rewards.

Self-Evolution and Emergent Behaviors

Self-Verification: The model iteratively checks its outputs for consistency (e.g., re-evaluating math solutions).

“Aha Moment”: Demonstrates autonomous error correction during reasoning tasks, a capability emergent from RL training.

Limitations and Future Directions

• Language Sensitivity: Struggles with non-English languages (e.g., Chinese) due to RL safety constraints.
• Prompt Sensitivity: Outputs vary significantly with minor prompt changes.
• MCTS Overhead: Monte Carlo Tree Search, while effective, remains computationally intensive.

Future updates aim to enhance multilingual support, reduce format dependency, and expand into multi-turn interactions.

In summary, DeepSeek’s primary differentiators include its cost-effective training methodology, open-source model, and rapid market adoption. These factors have positioned it as a significant player in the AI landscape, offering an alternative to models developed by more established Western companies.

WHAT POTENTIALS AWAIT FROM DEEPSEEK?

DeepSeek’s R1 model stands out in the AI landscape due to its innovative design and cost-effective implementation, offering unique advantages across various sectors.

Accelerating AI Transformation

DeepSeek-R1 employs a “mixture of experts” architecture, activating only relevant subnetworks for specific tasks. This design significantly reduces computational requirements, enabling businesses to analyze complex data, predict outcomes, and automate processes more efficiently. The model’s open-source nature further facilitates rapid AI adoption and customization, accelerating transformation initiatives.

Benefits Across Multiple Sectors

• E-commerce: DeepSeek-R1 enhances user experiences by providing personalized product recommendations and managing customer queries through intelligent chatbots. Its advanced natural language processing capabilities allow for the creation of unique, engaging product descriptions at scale, leading to increased sales and improved customer satisfaction.
• Logistics: The model’s predictive analytics capabilities assist logistics companies in forecasting demand and optimizing inventory levels. By analyzing real-time data, businesses can reduce supply chain disruptions and enhance operational efficiency. DeepSeek-R1’s reinforcement learning approach enables continuous improvement in decision-making processes, leading to more accurate demand predictions and efficient resource allocation.
• Banking: In the finance sector, DeepSeek-R1 aids in detecting unusual transaction patterns, flagging potential fraud, and assessing risks. Its ability to process and analyze large datasets ensures compliance with regulatory requirements, enhancing security and trust. Financial institutions can leverage DeepSeek-R1 to offer personalized financial advice, manage transactions, and assist with account inquiries, thereby improving customer service and operational efficiency.

Improving Human Resource Quality

By automating routine tasks such as data entry and report generation, DeepSeek-R1 allows employees to focus on strategic activities, enhancing productivity and job satisfaction. The model provides real-time insights and recommendations, empowering employees to make informed decisions quickly. Its open-source framework offers flexibility for customization, enabling businesses to tailor the AI to their specific needs and integrate it seamlessly into existing workflows.

Enhancing Business Operations

DeepSeek-R1 contributes to operational efficiency by automating repetitive tasks and optimizing processes. For instance, in logistics, the model can analyze data to optimize delivery routes, reducing fuel consumption and operational costs. Its efficient use of computational resources makes it a cost-effective solution for businesses aiming to enhance performance without significant investment in hardware. The model’s open-source nature also allows for continuous community-driven improvements, ensuring that businesses have access to the latest advancements in AI technology.

In summary, DeepSeek-R1’s innovative architecture and open-source accessibility offer transformative potential for businesses seeking to enhance operations, improve human resource quality, and maintain competitiveness in today’s data-driven landscape.

HOW CAN YOU MAXIMIZE THE EFFECTS OF DEEPSEEK FOR BUSINESS DEVELOPMENT? 

Businesses can leverage DeepSeek to boost operational efficiency by strategically integrating its cost‐effective AI capabilities into their operations. Here’s how:

Build a Quality Data System

A foundational data resource is an essential component of every generative AI model, including DeepSeek. Before deploying the model, businesses should invest in robust data governance and cleaning processes to ensure data is reliable and continuously updated. High‐quality, well‐structured data is critical for DeepSeek to generate accurate insights.

Given regulatory and security concerns, particularly with data privacy, establishing secure data pipelines and storage solutions is essential.

Integrate Strategically

• Pilot Before You Commit: The best way to integrate DeepSeek effectively is to start with pilot projects focused on specific tasks—such as automating customer support or predictive maintenance. Define key performance indicators (KPIs), run controlled experiments, and analyze the outcomes. This approach helps businesses assess DeepSeek’s potential without major upfront risks.
• Refine and Expand: Once the pilot proves successful, gradually expand its applications across departments. This iterative approach ensures that any inefficiencies are addressed before moving into full-scale adoption, allowing for smoother, more effective AI integration.

Build In‐House AI Resources

• Develop Talent and Expertise: Investing in training and hiring dedicated AI professionals enables the business to better understand, customize, and optimize DeepSeek’s models.
• Foster a Culture of Innovation: Establish cross-functional teams to explore new use cases and continuously improve AI implementations.

Integrate into Core Products and Services

Being well-known for outstanding mathematical problem-solving and logical reasoning, the Deepseek-R1 model showcases its utilities in numerous aspects. You can make the most of Deepseek-R1 capabilities through the ideas below: 

• Embed AI Across Operations: Incorporate DeepSeek’s advanced reasoning capabilities into core systems like ERP, CRM, or customer support platforms. This integration can automate complex analytical tasks, provide real-time, data-driven insights, and enhance strategic decision-making processes.
• Enhance Customer Experience: Utilize DeepSeek’s natural language processing (NLP) strengths in chatbots and personalized recommendation systems. Its logical reasoning abilities can interpret nuanced customer inquiries, leading to more accurate and context-aware responses, thereby improving overall service quality.
• Optimize Financial Analysis and Risk Management: Leverage DeepSeek’s mathematical prowess to perform intricate financial modeling, risk assessments, and predictive analytics. This application can lead to more precise forecasting and informed financial decisions.
• Innovate Product Development: Apply DeepSeek’s problem-solving skills to research and development initiatives. Its capacity to analyze complex datasets and simulate various scenarios can accelerate innovation and optimize product design processes.

By integrating DeepSeek’s mathematical and logical reasoning strengths into various business operations, companies can enhance efficiency, foster innovation, and maintain a competitive edge in their respective industries. 

CONCLUSION 

With endless possibilities and potential, tech giants & companies worldwide have spent billions of dollars to innovate, leverage & introduce new applications of Generative AI. While DeepSeek is not considered another transformative innovation, the model has shown a significant example of how Generative AI is becoming accessible, cost-efficient, and ready to become a critical pillar in businesses’ AI Transformation and digital transformation journey as a whole. 

To maximize the effects of Deepseek and Generative AI Adoption in general, it’s important to consider various aspects to ensure efficient implementation. This includes understanding what aspects can be transformed, building a high-quality data source, and focusing on AI resources & talent development. With careful consideration and a long-term strategy, businesses can improve their productivity, leveraging competitive advantage with the help of AI technology. 

As a company centralizing approach around technology innovation & development, NTQ has been actively focusing on research & development of new AI Implementations to create breakthrough solutions. Our exclusive Generative AI Ecosystem – NxUniverse – is the most notable example of how we turn AI into multiple applications, including programming & coding, language translation, sales management, recruitment & more. With comprehensive implementations & scalable capabilities, NxUniverse is the optimal solution that helps boost productivity & efficiency of knowledge management for businesses on different scales.

Stay tuned for more updated insights as we’re tracking the latest AI and emerging technology innovations! 

This article is a collection of multiple resources, and it does not necessarily reflect NTQ’s opinions. 

Author – Nha Nguyen – NTQ AI Expert