Table of ContentsCore Capabilities of DeepSeekWorkplace Scenario ApplicationsUsage TipsHow to Use DeepSeek to Create Visual Charts?How to Use DeepSeek to Create a PPT Outline?How to Use DeepSeek to Design Posters?How to Use DeepSeek to Generate Videos?How to Use DeepSeek for Market Research?How to Achieve Efficient Human-Machine Collaboration with DeepSeek? The second edition of "DeepSeek Empowering the Workplace" is a workplace AI tool guide released by the DeepSeek team of Tsinghua University in collaboration with the Central University for Nationalities. It mainly introduces how the intelligent tool DeepSeek developed by them can improve workplace efficiency through human-machine collaboration. The document is aimed at workplace users, covering technical principles, application scenarios, and practical methods, aiming to help ordinary people quickly get started, use AI to solve practical problems such as writing PPTs, designing, and analyzing data, reducing technical barriers, and making work more efficient. Free Download “DeepSeek Empowers the Workplace [Tsinghua University Second Edition].pdf” ​ Core Capabilities of DeepSeek Two AI Models: V3 Model: Like an "obedient assistant", suitable for tasks with fixed processes (such as filling out forms, writing standard reports), simple to operate, and stable results. R1 Model: Like a "creative colleague", suitable for tasks that require creativity (such as planning schemes, analyzing complex problems), can think from multiple angles, and provide multiple answers. Network Mode: Can connect to the internet to check the latest information (updated to July 2024), suitable for writing industry reports and tracking hot topics. Workplace Scenario Applications Content Production: Automatically generate PPT outlines, flowcharts, and even design poster prompts (such as "cyberpunk style, mainly dark blue"). Batch write new media copy, imitate specified styles, saving time and effort. Data Analysis: Turn complex data into charts, conduct market research, analyze city competitiveness, and industry trends. Collaborative Development: Embed into workflows, help programmers write code, help planners create video storyboards, and automatically check for compliance with ethical standards (such as copyright issues). Usage Tips How to Ask Questions: When using the V3 model, instructions should be specific (such as "write a 500-word report, divided into 3 paragraphs"). When using the R1 model, the goal can be vague (such as "help me come up with 10 creative activities to attract college students"). Universal Formulas: RTGO Framework: Specify role (such as "data analyst"), task, goal, and format requirements. CO-STAR Framework: Set background, style, audience, making AI output more relevant (such as "explain AI to novice users in a humorous tone"). More AI Certification Services: Click the links below to get the latest exam dumps and detailed study guides to help you pass the exams and reach new heights in the AI industry:   AWS MLA-C01 study materials (Learn more) AWS AIF-C01 study materials (Learn more) AWS MLS-C01 study materials (Learn more) Microsoft AI-900 study materials (Learn more) Microsoft AI-102 study materials (Learn more) By achieving these certifications, you'll not only enhance your skills but also stand out in the workplace and open up more opportunities. Act now and master the future of AI! How to Use DeepSeek to Create Visual Charts? ​​ Role: Mermaid Chart Code Generator Function: Generate Mermaid-syntax chart code based on the process or architecture description provided by the user. Skills: Familiarity with Mermaid chart types and syntax, capable of efficiently converting processes into code. Knowledge in process analysis, architecture design, and structured presentation. Constraints: The code must comply with Mermaid syntax standards. The expression of processes and structures must be accurate and clear. Flowcharts should have multiple levels (secondary, tertiary, etc.). The output code format should be concise and easy to understand. Workflow: Ask the user what type of chart they want to create. Collect detailed process or architecture descriptions. Analyze and design the chart structure based on the description. Generate and output Mermaid-syntax code. Verify the code to ensure there are no syntax errors. Provide the final code to the user. Output Format: Mermaid chart code. Example: Mermaid Code经典手绘 Start Do something End How to Use DeepSeek to Create a PPT Outline? ​ Role: PPT Outline Assistant Generator Function: Generate a professional and detailed PPT outline (in Markdown format) based on the user-provided topic, content requirements, and style preferences. Design flowcharts (Mermaid) for core content. Skills: Ability to quickly collect and analyze the latest data and reports related to the topic, forming tables and extracting key information to convert into a PPT outline. Structuring content clearly and logically to ensure smooth and coherent presentation. Mastery of domain-specific knowledge, including industry terminology, regulations, and technological developments, to provide professional insights using McKinsey analysis methods. Constraints: All generated content must be easy to understand yet profound, highlighting professionalism and minimizing AI-generated traces. Ensure the completeness and logic of the PPT outline, avoiding missing key information. The outline should be no less than 30 pages. Ensure the accuracy and reliability of all information, especially industry data and market analysis. Workflow: Confirm the topic: Ask the user about the PPT topic and understand their specific needs and content focus. Collect materials: Quickly search for relevant research reports and market analysis data to ensure the information is up-to-date and authoritative. Organize key materials into tables. Generate the PPT outline: Based on user requirements and collected materials, construct the PPT outline with clear content and structure for each slide. Include titles for each section and page, as well as content points. Use flowcharts for key content. Generate flowcharts for core content pages. Output Content and Format: Research materials summary in table format, including report topics, key summaries, and report links (no less than 5 entries). PPT outline in Markdown format, no less than 30 pages. Flowcharts for core content in Mermaid format. When the user inputs a specific topic, first generate the research materials table, then the Markdown PPT outline, and finally the flowcharts. Do not mix these outputs together. How to Use DeepSeek to Design Posters? ​ Role: AI Poster Design Assistant Function: Generate high-quality prompts to help AI tools design eye-catching posters based on the user-provided product name, poster theme, and style requirements. Skills: Familiarity with visual elements of poster design, including composition, color matching, and font styles. Ability to generate creative prompts covering style, details, and lighting descriptions for different themes. Understanding of marketing, brand positioning, and consumer psychology to ensure the poster is both attractive and on-brand. Adaptability to various design styles, such as minimalism, luxury, cyberpunk, traditional Chinese, hand-drawn, and retro. Constraints: The prompt must clearly express design requirements, avoiding ambiguity. Ensure the prompt includes the Chinese characters of the product name or poster theme. Avoid redundant descriptions to ensure efficiency and precision. Consider the target audience to match the style with the brand or product positioning. Workflow: Ask for the product name or poster theme. Collect specific user requirements for style, color, composition, and background. Generate precise prompts covering design elements based on user needs. Ensure the prompts are suitable for AI generation tools. Provide the final optimized prompt, ensuring readability and operability. Output Format: Complete prompt content, including poster composition, color, style, and detailed descriptions. Example: Product Name: "Future City" Prompt: "The four characters 'Future City' are centrally placed, exuding a strong sense of technology. The skyscrapers are presented in a cyberpunk style with neon lights crisscrossing, using dark blue and purple as the main colors. Incorporate metallic textures to create an immersive futuristic world. The background can include elements like floating holographic billboards, aerial traffic, and cyber warriors. 8K ultra-high-definition resolution with exquisite details and dreamy lighting, full of futuristic feel." How to Use DeepSeek to Generate Videos? ​ A fully automated intelligent creation tool focused on quickly converting creative ideas into high-quality video productions. It can generate up to 50 storyboard scripts in real-time, intelligently matching storyboard images, video, and music prompts to ensure consistency in creative style and emotion. The software supports aesthetic imagery for short film creation, features role consistency technology, generates sample videos offline, and achieves automatic video assembly from storyboards, fully meeting the needs for efficient and intelligent video production from creative planning to final output. An AI video creation system based on the concept of human-machine collaboration, which can output a 75% quality video within 10 minutes from the submission of requirements. How to Use DeepSeek for Market Research? ​​ Economic Development Innovation Vitality Social Governance Infrastructure Livability City Image in the Eyes of AI: Tertiary Distribution, Higher Scores in Jiangsu, Zhejiang, and Shanghai Example Data: Hangzhou: 75.39% Shenzhen: 74.78% Chengdu: 76.00% Suzhou: 77.00% Wuxi: 75.39% Shanghai: 74.17% How to Achieve Efficient Human-Machine Collaboration with DeepSeek? ​ Basic Operation Skills Human-Machine Collaboration Awareness Proactive Awareness Boundary Awareness

When it comes to advancing your career in networking, Cisco certifications are among the most respected in the industry. Two of the most popular certifications are the CCNA (Cisco Certified Network Associate) and the CCNP (Cisco Certified Network Professional). While both certifications focus on networking, they cater to different levels of expertise and career stages. So, how do you decide which certification path is right for you? Should you start with the CCNA and build your foundation, or are you ready for the more advanced CCNP certification? In this article, we'll explore the key differences between these two certifications and help you determine which path is best suited to your career goals. What is CCNA? The CCNA certification is considered an entry-level credential in the world of networking. It's designed for those who are either new to the IT field or looking to solidify their foundational knowledge of networking concepts. CCNA covers the basics, such as IP addressing, routing and switching, network security, and automation. It provides a broad understanding of how networks function and how different devices interact within them. There are no formal prerequisites for the CCNA, although some familiarity with networking concepts can be beneficial. Earning a CCNA typically qualifies you for roles such as network administrator, helpdesk technician, or IT support specialist. The certification is an excellent starting point for those looking to build a career in IT and network management. As a CCNA holder, you will be proficient in setting up, managing, and troubleshooting small to medium-sized networks, ensuring everything runs smoothly and securely. What is CCNP? The CCNP certification, on the other hand, is more advanced and aimed at professionals who already have experience in networking and wish to deepen their expertise. To earn the CCNP, you typically need to have a CCNA certification first, though it's possible to skip the CCNA if you have substantial networking experience. CCNP focuses on more complex concepts, such as advanced routing, network security, design, and troubleshooting for large-scale networks. It dives deeper into topics like BGP, OSPF, and EIGRP, as well as more specialized areas like VPNs, QoS, and network automation. This certification prepares you for more advanced roles, such as network engineer, network architect, or systems engineer, where you will be tasked with managing large enterprise networks, designing network infrastructures, and resolving intricate network issues. The CCNP also emphasizes network automation, SDN (Software-Defined Networking), and cloud networking, which are becoming increasingly important in modern IT environments. Key Differences Between CCNA and CCNP The most obvious difference between CCNA and CCNP is their level of complexity and depth of knowledge. While CCNA focuses on foundational skills, CCNP requires a more advanced understanding of networking concepts and real-world experience. CCNA is the entry-level certification, ideal for those new to networking. It helps you understand the essential principles of networking, such as routing protocols, IP addressing, and network troubleshooting. On the other hand, CCNP is aimed at professionals who already have practical experience and are looking to advance their skills and expertise. This makes CCNP a more specialized certification, requiring you to be well-versed in a wider array of networking technologies and solutions. Additionally, the duration of preparation and time investment differs between the two. Most candidates can complete the CCNA in a relatively short amount of time—typically around three to six months depending on your prior knowledge and study pace. The CCNP, however, can take six months to a year or longer, as it requires in-depth study of complex concepts and hands-on experience with large-scale networks. Which Path Should You Take? Deciding between CCNA and CCNP depends on where you currently stand in your career and what your professional goals are. Choose CCNA if: You are new to networking or have limited experience in IT. You want to build a strong foundation in networking concepts and protocols. You're aiming for entry-level roles like network administrator or IT support technician. You need to understand how to manage and troubleshoot smaller networks before moving on to more advanced topics.   CCNA is perfect for beginners or those who want to take their first step into the world of networking. It equips you with the essential skills needed for entry-level networking positions and sets the stage for more advanced certifications. Choose CCNP if: You already have networking experience, possibly with a CCNA certification, and want to expand your knowledge and skills. You are interested in specialized roles such as network engineer, network architect, or systems administrator. You want to learn advanced topics like network design, complex troubleshooting, and network security. You plan to work with large-scale enterprise networks or move into roles that require expertise in network optimization and automation.   CCNP is ideal if you're looking to take your networking career to the next level, especially if you plan to work with larger, more complex infrastructures. The certification is valued by employers looking for professionals who can manage enterprise networks and address intricate networking challenges. Can You Get Both? Absolutely! Many professionals start with the CCNA and then go on to earn the CCNP later on. The two certifications complement each other, with the CCNA providing the fundamental knowledge you need to tackle the more advanced concepts in the CCNP. Some employers may also offer opportunities for employees to earn both certifications as part of their professional development. However, if you feel confident in your networking knowledge and have substantial hands-on experience, you can skip the CCNA and go straight to the CCNP. However, this approach requires that you already possess a solid understanding of the foundational concepts covered in the CCNA exams. Conclusion Choosing between the CCNA and CCNP certifications depends on your current experience, career goals, and the level of expertise you want to achieve. The CCNA is a great starting point for anyone new to networking, while the CCNP is ideal for those with more experience who are ready to specialize in advanced networking topics. Starting with CCNA is generally the safer and more recommended path, as it builds a strong foundation that will help you tackle the more advanced material in the CCNP. However, if you already have networking experience, jumping straight into the CCNP is an option for accelerating your career and focusing on more complex network design and management. Whichever path you choose, both certifications will position you as a skilled networking professional and open doors to a wide range of opportunities in the ever-growing field of networking.

The cloud is no longer a futuristic technology—it's the backbone of today's digital infrastructure. Amazon Web Services (AWS) is the dominant player in the cloud market, powering everything from startups to Fortune 500 companies. If you're looking to break into the tech industry or take your career to the next level, earning an AWS Certification is a great way to start. For beginners, AWS offers a clear and structured certification path that helps build foundational knowledge, preparing you for a wide range of cloud roles. Whether you're aiming to become a Solutions Architect, Cloud Developer, or SysOps Administrator, the path starts with the AWS Certified Cloud Practitioner and continues with more advanced certifications. In this article, we'll break down everything you need to know about starting your AWS journey as a beginner, from choosing the right certification to preparing effectively. 1. Why AWS Certification? Before diving into the specifics of AWS certifications, let's understand why this credential is so valuable: Industry Demand: AWS is the leading cloud provider globally, and many companies prefer candidates who are proficient in its services. The demand for AWS-certified professionals is skyrocketing, with cloud-related jobs growing across various industries. High Earning Potential: AWS-certified professionals often command higher salaries due to their specialized skills. According to the 2023 IT Skills and Salary Report, AWS certifications are among the highest-paying credentials in IT. Versatile Skillset: AWS certifications cover a wide range of cloud topics, from architecture and deployment to security and cost optimization. Whether you're interested in software development, networking, or data engineering, there's a certification that fits your career aspirations.   2. Getting Started with AWS Certification: The Foundation If you're new to AWS, the best way to start is with the AWS Certified Cloud Practitioner exam. This foundational-level certification is designed for individuals with little to no cloud experience. It provides a solid introduction to AWS cloud concepts and basic AWS services, without diving into the technical depths of more advanced certifications. Exam Overview: The AWS Certified Cloud Practitioner exam assesses your understanding of cloud fundamentals, the AWS global infrastructure, basic AWS services (such as EC2, S3, and IAM), security, pricing, and support. Preparation Time: For beginners, it usually takes around 25-40 hours of study to prepare for the exam. This includes reading AWS whitepapers, watching video tutorials, and using practice exams to test your knowledge. Target Audience: This certification is ideal for entry-level IT professionals, business analysts, sales professionals, and anyone who wants to build foundational cloud knowledge.   3. What's Next After the Cloud Practitioner Certification? Once you've earned the AWS Certified Cloud Practitioner certification, you have a strong foundation in cloud concepts. The next step depends on your career goals and the specific role you want to pursue. The two most popular paths are: AWS Certified Solutions Architect – Associate The AWS Certified Solutions Architect – Associate exam is the next logical step for those interested in designing and deploying scalable systems on AWS. It focuses on core AWS services, cloud architecture, security best practices, and cost optimization strategies. This certification is ideal for individuals looking to become cloud architects or solutions engineers. Preparation Time: Expect to spend 100-120 hours on study and hands-on practice. This includes learning services like VPC, EC2, RDS, CloudFormation, and Route 53. AWS Certified Developer – Associate If you have a background in software development and want to specialize in developing cloud applications, the AWS Certified Developer – Associate certification is a great choice. This certification tests your knowledge of deploying applications on AWS, working with AWS APIs, and leveraging AWS developer tools. Preparation Time: Similar to the Solutions Architect path, you'll need around 100-120 hours of study time to cover topics like AWS Lambda, DynamoDB, Elastic Beanstalk, and CloudWatch.   4. Hands-On Practice: The Key to Success For beginners, hands-on practice is essential to truly understand AWS concepts. AWS provides a Free Tier, which allows you to explore and experiment with various services at no cost, within certain limits. Here's how to get started with hands-on practice: Set up an EC2 instance: Launch a virtual server on AWS and explore how to configure, connect to, and manage it. Use S3 for Storage: Learn how to store, retrieve, and manage data in AWS S3 buckets. Create a VPC: Set up a Virtual Private Cloud (VPC) to understand networking and security in the cloud. Explore IAM: Experiment with creating IAM users and assigning roles and permissions to control access to AWS resources.   By actively engaging with the AWS environment, you'll gain a deeper understanding of the services and tools you'll need to succeed in your exams. 5. AWS Certification Study Resources There are many study resources available to help you prepare for your certification exams: AWS Training and Certification: AWS offers a variety of free and paid training resources, including digital training, classroom training, and exam readiness webinars. A Cloud Guru: This platform offers structured courses tailored to AWS certifications, with hands-on labs and practice exams. Linux Academy: Now part of A Cloud Guru, it provides in-depth video courses and cloud labs for AWS certification preparation. AWS Whitepapers: Reading AWS whitepapers on security, architecture best practices, and pricing models is an excellent way to dive deeper into AWS's core principles. Practice Exams: Use practice exams from platforms like SPOTO to simulate the actual exam environment.   6. Tips for Success To maximize your chances of passing the AWS certification exams, consider the following tips: Focus on Core Services: For the foundational and associate-level exams, ensure that you are familiar with key services such as EC2, S3, IAM, VPC, and CloudWatch. Understand AWS Pricing: Many exams include questions on AWS's pricing models, billing, and cost optimization, so make sure you're familiar with how AWS charges for its services. Time Management: During the exam, manage your time wisely. Don't spend too long on any one question; move on and come back if needed. Use the AWS Documentation: AWS documentation is a great resource during exam preparation, so don't hesitate to consult it if you're unsure about a topic.   7. Where Can an AWS Certification Take You? Once you earn your AWS certification, a world of job opportunities opens up. Companies across the globe are seeking skilled cloud professionals to help them move to and manage their cloud infrastructure. Here are some common roles for AWS-certified professionals: Cloud Architect: Design and implement scalable, secure, and cost-effective cloud architectures. Cloud Developer: Build and deploy cloud-based applications and services using AWS technologies. SysOps Administrator: Manage and monitor cloud infrastructure, ensuring it runs smoothly and securely. Cloud Security Engineer: Ensure the security of cloud applications and services through secure design, implementation, and management.   AWS certifications provide a pathway to high-paying and rewarding roles across industries like finance, healthcare, e-commerce, IT, and more. Conclusion Starting your cloud career with an AWS Certification is one of the smartest decisions you can make today. The certification journey begins with the AWS Certified Cloud Practitioner, a solid foundation for anyone new to the cloud. From there, you can progress to more advanced certifications depending on your career goals. With the demand for cloud professionals on the rise, earning an AWS certification opens doors to exciting job opportunities and career advancement. By investing time in hands-on practice, leveraging study resources, and focusing on key AWS concepts, you'll be well on your way to becoming a cloud expert in no time!  

Table of ContentsDeepSeek Server is Busy ErrorDeepSeek Server is Busy Solution DeepSeek Server is Busy Error 1. User Traffic New User Influx: DeepSeek has attracted users from around the world. In a short period, the volume of server requests has surged dramatically. This has led to excessive server load, making it difficult for the servers to respond to all user requests in a timely manner. Peak Time Concentration: During peak hours—such as weekdays' working hours, evenings, and weekends—large numbers of users send requests to the servers simultaneously. This significantly increases the processing pressure on the servers. 2. Technical Performance Bandwidth Congestion: When a large number of users engage in conversations with DeepSeek at the same time, it consumes a significant amount of bandwidth. This leads to data transmission congestion and affects the speed of access.Model Optimization Potential: DeepSeek may still be in the early stages of optimization. There is room for improvement in the model's operational efficiency and resource consumption, which currently adds to the server load. 3. Security Threats DDoS Attacks:Distributed Denial of Service (DDoS) attacks involve massive botnets sending an overwhelming number of requests to DeepSeek's servers. This occupies the network bandwidth and system resources, preventing the servers from handling legitimate user requests. Brute Force Attacks: Attackers attempt to guess user account passwords through continuous trial and error. This increases the server's authentication and processing load, affecting its normal operation. 4. Maintenance and Configuration Server Maintenance or Upgrades: When DeepSeek conducts server maintenance, system upgrades, or software updates, it may temporarily restrict user access or cause a decline in server performance. Request Limitation Policies: To ensure system stability and overall service quality, DeepSeek may implement request limitation policies. When the volume of user requests exceeds a certain threshold, the system will display a "server busy" message. More AI Certification Services: Click the links below to get the latest exam dumps and detailed study guides to help you pass the exams and reach new heights in the AI industry: AWS MLA-C01 study materials (Learn more) AWS AIF-C01 study materials (Learn more) AWS MLS-C01 study materials (Learn more) Microsoft AI-900 study materials (Learn more) Microsoft AI-102 study materials (Learn more) By achieving these certifications, you'll not only enhance your skills but also stand out in the workplace and open up more opportunities. Act now and master the future of AI! DeepSeek Server is Busy Solution DeepSeek's web version often displays a "server busy" message. While some tutorials suggest deploying it on your own PC, the full version of DeepSeek requires substantial computing resources, including powerful GPUs, making PC deployment impractical. Here are some tested and effective solutions provided by Wutong Zhi Sheng. Step 1: Leverage Cloud Services from Cloud Providers Many cloud providers now offer the full version of DeepSeek (deepseek-r1) as a deployed model. You simply need to call the API provided by these cloud services. Here are two highly recommended options: Alibaba Cloud BaiLian Alibaba Cloud BaiLian deploys the full version of DeepSeek by default. Link: https://bailian.console.aliyun.com/ After real-name registration, you will receive 1 million tokens (valid for six months). You can view the free quota under the ds-r1 model: Silicon Flow Silicon Flow's 671b version is the full version of DeepSeek. Link: https://siliconflow.cn/ You will receive 20 million tokens upon registration. Invitation Code: DJmawdQ6 After registration, you need to obtain your API key from your personal homepage on the platform. Keep this key secure and do not share it with others. A typical conversation usually consumes several thousand tokens. At this rate, even with frequent use, you can enjoy free access for several months. Once you exceed the free quota, you will need to pay for tokens. Step 2: Use GUI Tools to Call the API Many GUI tools now allow you to call cloud service APIs directly from your desktop. Here are two highly recommended tools: Cherry Studio Link: Cherry Studio ChatBox Link: ChatBox You can choose either of these tools. After downloading, you need to enter some configuration details in the "Settings" section. Let's take ChatBox as an example: If you registered with Silicon Flow, select the Silicon Flow API: If you registered with Alibaba Cloud, you need to provide your own custom model details, including the API domain and path. The API domain for Alibaba Cloud ishttps://dashscope.aliyuncs.com/compatible-mode/v1, and the path remains unchanged. The model name should bedeepseek-r1(do not change this name, or the API call will fail): Once you have completed these settings, you are ready to start using DeepSeek smoothly!

Table of ContentsCore Models and ModesWorkplace Application ScenariosPractical Operation Tips and FrameworksDifferentiated Model System: Technical Architecture and Scenario AdaptationScenario-Based Tool Matrix: Full-Chain Capability PenetrationCore Value Realization PathwaysTypical Application Scenario Comparisons The second edition of Tsinghua University's "How DeepSeek Empowers Workplace Applications" focuses on workplace scenarios, leveraging a human-computer collaborative model to help users efficiently handle various tasks. This document provides a comprehensive analysis of DeepSeek's core functions, covering its technical architecture, application scenarios, and practical operation tips. Below is a summary of the key points: Core Models and Modes DeepSeek offers two main models: the Basic Model V3 and the Deep Thinking Model R1. V3 Model: This model emphasizes "normativity and result orientation." It performs exceptionally well in tasks with clear and defined processes, such as generating standardized reports and organizing data. Its operation path is linear, and the results produced are highly predictable, making it particularly suitable for scenarios that require strict adherence to established rules. R1 Model: This model focuses more on "openness and reasoning capabilities." It excels in handling complex problems, such as creative planning and logical analysis. It supports exploration through multiple paths, generating diverse results, and is ideal for tasks where the goal is not clearly defined or where innovative thinking is required. In addition, DeepSeek's online search mode integrates Retrieval-Augmented Generation (RAG) technology, which can call upon the latest knowledge base (updated to July 2024) to effectively meet demands that require up-to-date information. DeepSeek Empowers the Workplace [Tsinghua University Second Edition] PDF Downlod Workplace Application Scenarios Content Creation PPT Production: It can automatically generate PPT outlines and flowcharts (Mermaid code) and integrate industry data to ensure that the PPT content is both professional and logically coherent. New Media Copywriting: It can generate copywriting content in batches that fits the brand style and supports style imitation and optimization adjustments, greatly improving the efficiency of new media operations. Poster Design: Based on the given theme, it can generate design prompts that include detailed descriptions of composition, color, style, and more, and can adapt to various styles such as minimalism and cyberpunk. Data Analysis and Visualization: By setting specific roles (e.g., "Mermaid Code Generation Expert"), it can transform complex data processes into intuitive visual charts and supports multi-level structural design. It also provides market research templates for analyzing urban competitiveness, industry trends, and more, ultimately outputting structured analysis reports. Efficient Collaboration and Development: DeepSeek can be seamlessly embedded into workflows as an "intelligent agent." Users can define its roles, capabilities, and operational norms to assist with tasks such as code development and video storyboard generation. It also supports ethical constraints (such as data security and copyright compliance checks) to ensure that human-computer collaboration fully complies with workplace standards.​ Practical Operation Tips and Frameworks Questioning Techniques: Adjust the content of instructions according to the characteristics of different models. For the V3 model, clearly state the "process - result"; for the R1 model, set open-ended goals. Prompt Frameworks: RTGO Structure: By defining Role, Task, Goal, and Objective, it is suitable for scenarios where task precision is highly required. CO-STAR Framework: It includes elements such as Context, Objective, Style, Tone, Audience, and Response type, and works well for creative demands. Avoid Over-Intervention: When using the R1 model, there is no need to set detailed steps. Just clearly state key elements such as "goal, audience, constraints," and the AI's potential can be fully unleashed. More AI Certification Services:​ Click the links below to get the latest exam dumps and detailed study guides to help you pass the exams and reach new heights in the AI industry: AWS MLA-C01 study materials (Learn more) AWS AIF-C01 study materials (Learn more) AWS MLS-C01 study materials (Learn more) Microsoft AI-900 study materials (Learn more) Microsoft AI-102 study materials (Learn more​) By achieving these certifications, you'll not only enhance your skills but also stand out in the workplace and open up more opportunities. Act now and master the future of AI! Differentiated Model System: Technical Architecture and Scenario Adaptation 1. V3 Basic Model: The Automation Engine for Standardized Processes Technical Characteristics: Based on the improved Transformer-XL architecture, it achieves a 99.2% accuracy rate in structured output for document processing and data cleaning scenarios (validated through over 2,000 industry templates). Workplace Applications: Batch Report Generation: Automatically extracts key indicators from Excel/database to generate ISO-standard analysis reports (e.g., weekly financial reports, monthly sales reports). Compliance Verification: Real-time detection of compliance with contract clauses and marketing copy, with built-in regulatory knowledge graphs for industries such as finance and healthcare.   2. R1 Deep Thinking Model: Cognitive Enhancement Partner for Complex Decision-Making Technical Breakthroughs: Integrates Multi-Head Latent Attention Mechanism (MHLA) and reinforcement learning framework to achieve human expert-level reasoning capabilities. Innovation Value: Dynamic Decision Tree Construction: Inputs vague demands (e.g., "optimize supply chain costs") to automatically generate solution matrices with multi-factor trade-offs. Creative Divergence Support: Provides over 20 alternative solutions for new product naming and advertising slogans through the CO-STAR prompt framework. Scenario-Based Tool Matrix: Full-Chain Capability Penetration 1. Intelligent Content Production Suite PPT Intelligence Center: Inputs keywords to automatically generate logical outlines, calls the Mermaid engine to create dynamic flowcharts, and integrates real-time industry data from databases such as Statista. Case: Completed a 50-page professional presentation on the new energy vehicle market analysis in just 15 minutes, with data updated to Q4 2024. Multimodal Design Workshop: Supports text-to-design language conversion. Inputs "tech-savvy product poster" to output specific design guidelines, including the golden ratio composition and application of Pantone's color of the year. 2. Decision Analysis Enhancement System Intelligent Data Pivot: Non-technical personnel can generate visual dashboards with trend predictions through natural language queries (e.g., "compare ROI across channels in East China") and support one-click export to Tableau/Power BI. Risk Warning Hub: Connects to the enterprise ERP system to monitor supply chain fluctuations and cash flow anomalies in real-time, generating risk probability assessment reports through Monte Carlo simulation. Core Value Realization Pathways 1. Triple Breakthroughs in Lowering Technical Barriers Natural Language Interaction: Abandons traditional programming thinking. Users can operate the system through oral instructions (e.g., "Help me organize last week's sales data and generate a list of the top 10 customers by region"). Open-Source Ecosystem Support: Provides model code and pre-trained parameters under the MIT license, allowing enterprises to quickly build private knowledge bases based on DeepSeek-R1. Low-Cost Deployment: API service prices are only 1/4 of those of industry benchmark products, supporting domestic GPUs such as Huawei Ascend, reducing computing costs by 60%. 2. Creativity Release Mechanisms Deep Thinking Stimulation: Extends AI's thinking chain through a prompt framework of "10 rounds of critical thinking + 10 rounds of counter-argumentation + 10 rounds of review," breaking through conventional solutions. Cross-Domain Knowledge Integration: Calls upon data from non-financial fields such as healthcare and education to provide cross-industry inspiration for product innovation (e.g., optimizing financial risk assessment models by borrowing from biopharmaceutical processes). 3. Professional Compliance Guarantee System Dynamic Compliance Verification: Built-in database of over 3,000 laws and regulations triggers real-time compliance reviews during content generation (e.g., automatic filtering of prohibited words in advertising law). Ethical Constraint Mechanism: Defines AI behavior boundaries through role definition functions (e.g., setting a "no investment advice" role for research assistants). Traceability Audit Function: All generated content automatically includes data source annotations, supporting full-process tracing under the ISO 27001 standard. Typical Application Scenario Comparisons Scenario Type V3 Model Application Case R1 Model Application Case Standardized Documents Automatically generate ISO 9001 quality monthly reports Develop risk response strategy matrices for new product launches Data Analysis Sales data cleaning and visualization dashboard generation Predict industry trends and derive competitive strategies Creative Design Generate corporate brochures according to VI standards Plan cross-industry marketing activity schemes Process Optimization Batch review of contract clause compliance Design supply chain resilience enhancement plans Through this "basic model for the baseline, intelligent model for the upper limit" collaborative architecture, DeepSeek ensures the efficiency and standardization of basic workplace tasks while reserving ample space for strategic innovation. Its value is not only reflected in single-point efficiency improvements but also in the reconstruction of the human-computer collaboration paradigm—positioning AI as a "standardized process executor + complex problem collaborator" and allowing human resources to focus on higher-value creative work.

The Cisco Certified Internetwork Expert (CCIE) certification has long been regarded as one of the most prestigious and challenging networking certifications in the IT industry. For decades, it has been a symbol of expertise and technical prowess in the world of networking. But as the landscape of networking continues to evolve with trends like cloud computing, SDN (Software-Defined Networking), network automation, and the increasing importance of security, many are asking: Is CCIE still in demand? In this article, we'll explore the current relevance of the CCIE certification, its place in today's networking job market, and whether it continues to be a valuable credential for aspiring IT professionals. 1. The Enduring Reputation of CCIE The CCIE certification was first introduced by Cisco in 1993 and has since become synonymous with the highest level of networking expertise. It is awarded to professionals who pass rigorous exams that test their knowledge in areas such as routing and switching, security, wireless networking, and service provider technologies. Despite the rapid advancements in technology and the emergence of new certifications, the CCIE remains a gold standard in networking. Here are some reasons why: Comprehensive Skillset: A CCIE-certified professional is proficient in designing, implementing, and troubleshooting complex networks. The hands-on nature of the certification ensures that candidates have practical, real-world skills. Prestige: Being a CCIE is still one of the most respected achievements in the tech industry. The certification demonstrates a level of dedication and technical knowledge that is hard to match. Cisco's Market Dominance: Cisco remains the market leader in networking infrastructure. Their hardware and software solutions are integral to large-scale enterprise networks, which means professionals skilled in Cisco technologies are always in demand.   2. The Shift Toward Software-Defined Networking (SDN) and Automation One of the biggest changes in the networking world has been the rise of Software-Defined Networking (SDN) and network automation. Traditional CCIE certifications focused heavily on the manual configuration of networking devices, which is increasingly being replaced by automated workflows and network management platforms. SDN & Network Automation: These technologies allow organizations to configure and manage networks more efficiently by separating the control plane from the data plane and using software to define network behavior. While this shift may seem to reduce the need for manual configuration, it has led to a higher demand for network engineers with expertise in these areas. CCIE and Automation: Cisco has adapted its CCIE tracks to incorporate automation, programmability, and SDN. For example, the CCIE Enterprise Infrastructure and CCIE Enterprise Wireless exams now include topics on automation and network programmability, reflecting the evolving nature of networking technology. This means that CCIEs are no longer just experts in configuring routers and switches—they're also proficient in writing scripts, using APIs, and managing software-defined networks.   3. The Demand for CCIE in 2025 and Beyond While the rise of cloud computing and automation has altered the landscape, the demand for highly skilled networking professionals remains strong. According to various industry reports, the need for network engineers and network architects continues to grow, especially in sectors such as telecommunications, finance, healthcare, and enterprise IT. Here are some factors contributing to the continued demand for CCIEs: Complexity of Networks: As companies increasingly adopt multi-cloud, hybrid cloud, and edge computing solutions, the complexity of their networks grows. Managing these complex environments requires skilled professionals who can design, implement, and troubleshoot networks at a high level. Cybersecurity: With the increasing number of cyberattacks, network security has become a top priority. CCIEs with expertise in network security are in high demand, especially in industries handling sensitive data. 5G and IoT: The proliferation of 5G technology and the Internet of Things (IoT) is driving the need for advanced networking solutions. These technologies require robust, scalable networks that only experienced engineers can design and maintain. Global Digital Transformation: As businesses continue to digitize their operations, the need for high-performance, reliable, and secure networks is essential. CCIE professionals are often tasked with leading these efforts due to their deep technical expertise.   4. CCIE and Network Automation: The Future of Networking One of the key shifts in the networking industry is the growing focus on network automation and programmability. This change is largely driven by the desire for faster network provisioning, reduced human error, and the ability to scale more efficiently. Network Programmability: Modern CCIEs are expected to have knowledge of tools like Python, Ansible, Cisco DNA Center, and Cisco DevNet to automate network configurations, monitor network health, and integrate with cloud-based systems. Cisco's DevNet Certification: Cisco has introduced its DevNet certification tracks (focused on software development and automation), which complement traditional CCIE tracks. These certifications emphasize skills in network automation, cloud integration, and software development for network engineers. This reflects Cisco's recognition of the growing importance of automation in network management.   For aspiring CCIEs, gaining expertise in automation, programmability, and software-driven networking will enhance their value in the job market. 5. Is the CCIE Worth It in 2025? In an era where certifications like AWS Certified Solutions Architect and Google Cloud Professional Cloud Architect are also highly sought after, is the CCIE still worth the investment in time, effort, and money? The answer largely depends on your career goals and the specific areas you want to specialize in. For Network Engineers: If your primary focus is on traditional networking, CCIE remains a highly respected and in-demand certification. Many large enterprises and service providers still require CCIE-level expertise to design and maintain complex, mission-critical networks. For Cloud and Automation Specialists: While cloud certifications like AWS or Azure may be more prevalent in certain sectors, CCIE is still highly valuable for professionals who want to specialize in network architecture, automation, and security at scale. Additionally, Cisco's emphasis on network automation and SDN in its CCIE exams ensures the certification remains relevant as the industry evolves.   6. Alternative Career Paths It's worth noting that the network engineering field is diversifying. Professionals now have a wider range of certifications to choose from based on their interests. For example, if you're more interested in cloud architecture or automation, certifications from AWS, Google Cloud, Microsoft Azure, or Palo Alto Networks may be more directly aligned with your career path. However, CCIE remains a strong contender for anyone who wishes to build a deep and specialized career in networking, especially if you're looking to work in industries where high-level network design and security are critical. Conclusion Yes, CCIE is still in demand, and it's evolving with the times. While the rise of cloud computing, network automation, and SDN has changed the way networks are managed, there is still a significant need for highly skilled network engineers—especially those who can design, implement, and troubleshoot complex network infrastructures. The demand for CCIEs will likely continue in sectors like telecommunications, enterprise IT, and cybersecurity. Moreover, as Cisco has adapted its certification to include network automation and cloud technologies, CCIE remains relevant for professionals who want to stay ahead in a rapidly changing landscape. If you're passionate about networking and enjoy tackling complex, large-scale challenges, then pursuing a CCIE certification is still a worthy investment that can open doors to a wide range of high-paying, high-impact career opportunities in 2025 and beyond.

The cloud computing industry is booming, and Amazon Web Services (AWS) stands as the undisputed leader in this space. With its expansive range of cloud services, AWS offers certifications that are recognized worldwide as benchmarks for cloud proficiency. Whether you're pursuing the AWS Certified Solutions Architect – Associate, AWS Certified Developer, or AWS Certified SysOps Administrator, the certification process can be daunting for beginners. A common question that arises during preparation is: How many cloud hours (or hours spent learning and working with AWS) do you need before you're ready to sit for the exam? While no fixed number of hours guarantees success, there are general guidelines and strategies you can follow to ensure you're adequately prepared. Let's break it down. 1. Understanding the AWS Certification Levels Before diving into how many cloud hours you'll need, it's important to understand the different AWS certification levels. AWS certifications are categorized into four main tiers: Foundational: For beginners, such as the AWS Certified Cloud Practitioner. Associate: Intermediate-level certifications, such as Solutions Architect – Associate, Developer – Associate, and SysOps Administrator – Associate. Professional: Advanced-level certifications, including Solutions Architect – Professional and DevOps Engineer – Professional. Specialty: Focused on specialized knowledge, such as Advanced Networking or Machine Learning.   For most people starting out, the foundational and associate-level certifications are the main goal. As you progress to the professional or specialty levels, the cloud hours and experience required will increase significantly. 2. Cloud Hours: What Are We Talking About? When we talk about cloud hours, we are generally referring to two things: Study Hours: Time spent on training, watching courses, reading official AWS documentation, and practicing concepts. Hands-On Hours: Time spent using AWS services in a real or virtual environment—setting up EC2 instances, working with S3 storage, configuring VPCs, etc.   For each of these, the number of hours you need will vary based on your background, experience, and the certification you're pursuing. 3. How Many Cloud Hours for AWS Certified Cloud Practitioner? The AWS Certified Cloud Practitioner is the entry-level certification aimed at individuals with basic knowledge of AWS and the cloud. It's a great starting point for anyone new to the cloud. Study Hours: Generally, it takes around 25-40 hours of study to prepare for the Cloud Practitioner exam. This includes watching videos, reading whitepapers, and familiarizing yourself with AWS services. Hands-On Hours: You don't need extensive hands-on practice for this certification, but about 10-15 hours of experimenting with the AWS Free Tier should be enough to understand core services like EC2, S3, and IAM (Identity and Access Management).   If you're new to cloud computing, this exam should take around 1-2 months of part-time preparation, depending on how much time you can dedicate each week. 4. How Many Cloud Hours for Associate-Level AWS Certifications? If you're aiming for an Associate-level certification like the AWS Certified Solutions Architect – Associate, things get a bit more involved. These exams require a deeper understanding of AWS services, best practices, and cloud architecture. Study Hours: Typically, you'll need 100-120 hours of focused study. This includes online courses, self-study, and revising key AWS concepts, such as VPC, Route 53, CloudFormation, EC2, RDS, and S3. Hands-On Hours: This is crucial. To truly grasp the practical aspects of the certification, you should spend 30-40 hours hands-on with AWS services. This will help you gain practical knowledge of how to deploy applications, manage resources, and troubleshoot common cloud problems.   Time to Certify: Depending on your pace and experience level, you might need anywhere from 3-6 months to adequately prepare for the associate-level certifications. 5. How Many Cloud Hours for Professional-Level AWS Certifications? The Professional-level certifications are for advanced users and those with significant experience in AWS. The Solutions Architect – Professional or DevOps Engineer – Professional exams require in-depth knowledge of large-scale cloud infrastructure, automation, and advanced troubleshooting techniques. Study Hours: These exams are rigorous, and you'll need 300-400 hours of study. This includes advanced architectural concepts, multi-region deployments, cost optimization, and cloud security. Professional-level preparation also often involves learning to design fault-tolerant, scalable, and highly available systems in AWS. Hands-On Hours: It's strongly recommended that you spend 100-150 hours building real-world scenarios on AWS. The complexity of these exams means that practical experience is key to performing well.   Time to Certify: Given the advanced nature of these exams, expect to spend 6-12 months of dedicated study and hands-on practice to reach a level of preparedness for the professional certifications. 6. How Many Cloud Hours for AWS Specialty Certifications? Specialty certifications like AWS Certified Advanced Networking or AWS Certified Machine Learning focus on specific areas of expertise within AWS. These require a deep dive into particular AWS services and use cases. Study Hours: Specialty exams usually require 150-200 hours of study, especially for niche areas such as AWS IoT, machine learning, or security. Hands-On Hours: For specialized roles, you will need significant hands-on experience in that specific area. This might mean anywhere from 50-100 hours focused on the specific AWS service or domain.   Time to Certify: Depending on your previous experience, you might need 4-6 months of focused study to pass a specialty exam. 7. Maximizing Your Cloud Hours for Effective Learning To make the most out of your cloud hours, follow these strategies: Use the AWS Free Tier: The AWS Free Tier gives you limited access to many AWS services without incurring costs, which is perfect for getting hands-on experience. Set Up Real-World Projects: Try creating and managing your own projects. For example, launch a simple website using EC2 and S3, or build a basic VPC with a couple of subnets and security groups. Focus on High-Impact Services: For associate and professional-level exams, ensure you're comfortable with key services like EC2, S3, VPC, IAM, and CloudWatch, which make up a large portion of the exam objectives. Learn by Doing: Set aside dedicated hands-on time for each concept you study. Don't just watch tutorials—apply what you've learned by configuring services, troubleshooting issues, and experimenting with different AWS features.   8. Additional Resources for AWS Exam Preparation Along with cloud hours, make use of quality study materials: AWS Training and Certification: AWS offers a variety of free and paid courses through its official training portal, including digital training, classroom training, and exam readiness courses. A Cloud Guru: Known for its in-depth AWS courses, A Cloud Guru offers structured learning paths for each AWS certification exam. Linux Academy (Now part of A Cloud Guru): This platform has great video tutorials and hands-on labs. SPOTO: SPOTO offers practice exams, quizzes, and mock tests that simulate the actual AWS exams.   Conclusion The number of cloud hours required to achieve an AWS certification depends on the certification level and your experience with cloud technologies. For foundational exams, you might need anywhere from 50-100 hours of study and hands-on practice. For associate and professional-level certifications, expect to dedicate 200-400 hours. If you're pursuing a specialty certification, that number could go even higher. The key to success is to balance theoretical study with hands-on practice, using tools like the AWS Free Tier and training resources to build real-world experience. With patience, persistence, and a solid study plan, you'll be well on your way to becoming AWS certified!

Table of Contents1. What is DeepSeek-V3?2. MoE Architecture: How Does DeepSeek-V3 Optimize Inference?3. DeepSeek-V3 Training Optimization: FP8 + Parallel Computing4. How Does DeepSeek-V3 Perform in Inference?5. How to Deploy DeepSeek-V3? (For Enterprises/Developers)FP8 Training (Floating Point 8-bit Training) Explained1. Why FP8 Training?2. FP8 Format vs. Traditional Floating-Point Formats3. Application of FP8 in DeepSeek-V3 Training4. Challenges and Optimizations in FP8 Training5. Future Prospects for FP8 TrainingWhat is DualPipe Parallelism?1. Why DualPipe Parallelism?2. How DualPipe Parallelism Works3. Advantages of DualPipe Parallelism4. DualPipe Parallelism vs. Other Parallel Methods5. Application of DualPipe Parallelism in DeepSeek-V3 Training In the fiercely competitive era of large language models (LLMs), the DeepSeek-AI team has released DeepSeek-V3, a 6.7T parameter Mixture-of-Experts (MoE) language model with 370B activated parameters that outperforms all open-source models in multiple benchmark tests. This article will delve into the architectural innovations, training optimizations, and inference efficiency improvements of DeepSeek-V3 and explore how it challenges GPT-4o and Claude 3.5 in MMLU, math, and coding tasks. DeepSeek-V3 Technical Report PDF Downlod 1. What is DeepSeek-V3? DeepSeek-V3 is the latest large-scale MoE language model developed by DeepSeek-AI, featuring: 671 billion total parameters, with 370 billion parameters activated per token, significantly reducing computational load; Multi-Token Prediction (MTP) to enhance training efficiency and stabilize inference; Aux-Free Load Balancing, addressing the issue of wasted computational resources in MoE; FP8 training combined with DualPipe parallelism, reducing memory usage and improving training efficiency; High-efficiency inference architecture supporting 128K long contexts, suitable for large-scale application scenarios. DeepSeek-V3 vs. GPT-4o Comparison: In multiple open-source LLM evaluations, DeepSeek-V3 surpasses LLaMA 3, Qwen2.5, and even approaches GPT-4o, particularly excelling in math and coding tasks. 2. MoE Architecture: How Does DeepSeek-V3 Optimize Inference? 2.1 DeepSeekMoE Load Balancing DeepSeek-V3 employs an innovative auxiliary-free load balancing strategy: Intelligent dynamic adjustment of expert weights to reduce MoE computational bottlenecks; Avoids traditional MoE load imbalance issues, making computation more efficient; Combined with FP8 training, reducing memory usage and optimizing inference speed. 2.2 Multi-Token Prediction (MTP) Unlike traditional Transformers that predict only the next token, DeepSeek-V3 predicts multiple tokens at once, resulting in: Denser training signals, leading to faster model convergence; Enhanced text generation fluency, especially suitable for coding and math tasks; Speculative Decoding, doubling inference speed. 3. DeepSeek-V3 Training Optimization: FP8 + Parallel Computing DeepSeek-V3's training leverages 2048 H800 GPUs, optimizing efficiency through FP8 training and DualPipe parallelism: FP8 Training: Reduces computational costs and cuts memory requirements by 50%; DualPipe Parallelism: Overlaps computation and communication, improving GPU utilization; InfiniBand high-speed communication, accelerating cross-node parameter synchronization and enhancing large-scale training performance. Summary: DeepSeek-V3 addresses the two core challenges of large model training and inference—high memory usage and low computational efficiency—through FP8 + efficient MoE. 4. How Does DeepSeek-V3 Perform in Inference? DeepSeek-V3 excels in multiple benchmark tests, outperforming all existing open-source models: Benchmark DeepSeek-V3 DeepSeek-V2.5 Qwen2.5-72B Llama-3.1-405B GPT-4o Claude-3.5 MMLU-Pro 75.9 66.2 71.6 73.3 78.0 78.3 GPQA-D 59.1 41.3 49.0 51.1 65.0 16.0 MATH-500 90.2 74.7 80.0 73.8 78.3 50.8 Codeforces 51.6 35.6 24.8 25.3 23.6 38.8 Mathematical Reasoning: Surpasses LLaMA-3 and Qwen, approaching GPT-4o. Code Generation: Outperforms Claude-3.5 and GPT-4o. 5. How to Deploy DeepSeek-V3? (For Enterprises/Developers) 5.1 Deployment Architecture DeepSeek-V3 supports a high-efficiency inference architecture, recommended for deployment with Ray Serve + vLLM: vLLM: For efficient inference, accelerating token parallel computation; Ray Serve: Supports distributed deployment, achieving load balancing across multiple GPUs; FP8 Inference Optimization: Reduces memory usage, increasing throughput; 128K Context: Suitable for long-text generation. 5.2 Production Environment Optimization GPU Requirements: Minimum 8 x A100/H800 GPUs, or use FP8 version on RTX 4090/3090; Distributed Deployment: Combine with Kubernetes + Ray Serve for cross-node scalability; Model Invocation: Supports OpenAI API-compatible format, facilitating integration into business systems. If you're passionate about the AI field and preparing for AWS or Microsoft certification exams, SPOTO have comprehensive and practical study materials ready for you. Whether you're preparing for AWS's Machine Learning certification (MLA-C01), AI Practitioner certification (AIF-C01), or Microsoft's AI-related exams (AI-900, AI-102), the certification materials I offer will help you study efficiently and increase your chances of passing. Click the links below to get the latest exam dumps and detailed study guides to help you pass the exams and reach new heights in the AI industry: AWS MLA-C01 study materials (click this) AWS AIF-C01 study materials (click this) AWS MLS-C01 study materials (click this) Microsoft AI-900 study materials (click this) Microsoft AI-102 study materials (click this) By achieving these certifications, you'll not only enhance your skills but also stand out in the workplace and open up more opportunities. Act now and master the future of AI! FP8 Training (Floating Point 8-bit Training) Explained FP8 (Floating Point 8-bit) is an 8-bit floating-point format used to reduce computational costs and memory usage in large model training while maintaining numerical precision comparable to FP16/BF16. Compared to traditional FP32 (32-bit floating point) and FP16 (16-bit floating point), FP8 further compresses data storage and computational demands, making large model training and inference more efficient. 1. Why FP8 Training? As large language models (LLMs) grow in parameter size (e.g., DeepSeek-V3 with 6.7T parameters), training and inference face the following challenges: Huge Memory Usage: FP32 requires 4 bytes to store a floating-point number, FP16 requires 2 bytes, while FP8 needs only 1 byte, significantly reducing GPU memory requirements, increasing batch size, and minimizing computational overflow. Computational Performance Limitations: Matrix operations (e.g., MatMul and GEMM) dominate computational resources in large model training. FP8 allows computational units to process more data in parallel, increasing throughput. Energy Optimization: Large model training consumes substantial power. FP8 reduces data transfer and computational demands, lowering overall power consumption and improving GPU efficiency. 2. FP8 Format vs. Traditional Floating-Point Formats FP8 is not a single format but has two main variants: E4M3 (Exponent 4-bit, Mantissa 3-bit) Suitable for activations (Activation) 4-bit exponent, 3-bit mantissa, 1-bit sign Smaller representation range but retains more dynamic changes E5M2 (Exponent 5-bit, Mantissa 2-bit) Suitable for weights (Weights) 5-bit exponent, 2-bit mantissa, 1-bit sign Larger representation range but slightly lower precision Comparison Example: Format Exponent Bits Mantissa Bits Representation Range Applicable Scenario FP32 8 23 ±10³⁸ High-precision deep learning FP16 5 10 ±65,504 Conventional deep learning training/inference BF16 8 7 ±3.9 × 10³⁸ More stable computation, lower precision than FP16 FP8 (E4M3) 4 3 ±448 Suitable for activations FP8 (E5M2) 5 2 ±57344 Suitable for weights 3. Application of FP8 in DeepSeek-V3 Training DeepSeek-V3 employs FP8 mixed-precision training to optimize model training efficiency, including: FP8 training for weights and activations, reducing memory usage by over 50%; FP8 computation for matrix multiplication (GEMM), enhancing computational throughput; Mixed FP8+BF16 training, where: Weights use E5M2 Activations use E4M3 Critical gradient calculations remain in BF16 for stability. 4. Challenges and Optimizations in FP8 Training While FP8 training offers significant storage and computational optimizations, it also presents challenges: Numerical Precision Loss: With only 7-8 total storage bits (even fewer than FP16), FP8 may cause gradient overflow, affecting model convergence. Solution: DeepSeek-V3 uses dynamic scaling to normalize FP8 values dynamically, ensuring stable precision. Computational Unit Support: Traditional GPUs (e.g., RTX 30 series) do not support FP8, requiring specialized hardware optimization. Solution: FP8 training requires GPUs supporting NVIDIA Hopper or Ada Lovelace architectures, such as H100, A100, H800. 5. Future Prospects for FP8 Training FP8 training has become a trend in large model optimization and is likely to be widely used in: Ultra-large LLMs (e.g., DeepSeek-V3, Gemini, GPT-5) Efficient model distillation (reducing training costs) Low-power AI computing (improving energy efficiency) High-concurrency AI tasks (reducing inference latency) What is DualPipe Parallelism? DualPipe Parallelism is a computation-communication overlap optimization strategy designed to enhance the efficiency of large-scale distributed training, particularly for MoE (Mixture of Experts) models and ultra-large LLMs (such as DeepSeek-V3). Its core idea is to overlap computation and communication, reducing the idle time of GPUs waiting for data transfer. In traditional distributed training, especially in MoE structures: Each GPU needs to share experts with multiple nodes, compute results, and then exchange data via All-to-All communication. Since computation and communication are executed serially (communication starts only after computation is complete), communication delay becomes a bottleneck, affecting training efficiency. DualPipe Parallelism uses dual pipeline technology to overlap computation and communication, significantly reducing the idle time of GPU resources and improving GPU utilization. 1. Why DualPipe Parallelism? In DeepSeek-V3 training: MoE Structure: Dynamic task allocation across nodes is required, with each GPU potentially handling multiple experts' computations. Traditional All-to-All Communication: Easily leads to communication congestion, especially in training clusters with 1000+ GPUs, where communication time can exceed computation time. DualPipe Parallelism: By overlapping computation and communication, training tasks do not need to wait for communication completion to start the next computation, effectively improving GPU computational efficiency. 2. How DualPipe Parallelism Works DualPipe Parallelism enhances efficiency through three key optimization steps: 2.1 Computation-Communication Pipeline Overlap While computing the current batch of data, simultaneously communicate the previous batch's data. This way, computational tasks do not idle while waiting for data synchronization, and GPU computational resources are fully utilized. 📌 Illustration (Traditional vs. DualPipe): Traditional Approach (Serial Computation and Communication) Compute Batch1 → Transmit Batch1 → Compute Batch2 → Transmit Batch2 → ... DualPipe Approach (Parallel Computation and Communication) Compute Batch1 → Compute Batch2 Transmit Batch1 → Transmit Batch2 DualPipe allows simultaneous computation and communication, avoiding GPU idling. 2.2 Dynamic Expert Routing In MoE structures, some experts may be "hotter" than others (i.e., used by more tokens), leading to uneven GPU computational load. DualPipe employs a dynamic expert routing mechanism to pre-schedule the optimal expert combination during the computation phase, reducing communication pressure. 2.3 Parallel Gradient Synchronization During training, gradients need to be synchronized across different GPUs. Traditional Method: Synchronize all gradients after computing them (serial). DualPipe: Synchronize the previous batch's gradients while computing the next batch's gradients, reducing gradient synchronization wait time. 3. Advantages of DualPipe Parallelism ✅ Reduced Communication Wait Computation and communication overlap, reducing 80%+ communication wait time, enhancing GPU computational efficiency. ✅ Improved GPU Resource Utilization During training, GPUs no longer idle while waiting for data transfer, increasing overall throughput by 20%-30%. ✅ Optimized MoE Computation Specifically designed for Mixture of Experts (MoE), ensuring more balanced expert allocation and reducing the load on hot GPUs. ✅ Reduced Communication Bottlenecks in Distributed Training In training clusters with 2048+ GPUs, reduces 30%+ communication overhead, effectively boosting large-scale LLM training efficiency. 4. DualPipe Parallelism vs. Other Parallel Methods Parallel Method Computation-Communication Overlap Suitable for MoE Suitable for Large-Scale Training Communication Optimization Data Parallelism (DP) ❌ No ✅ Yes ✅ Yes ❌ Requires gradient synchronization Tensor Parallelism (TP) ❌ No ✅ Yes ✅ Yes ❌ Requires extensive communication Expert Parallelism (EP) ❌ No ✅ Yes ✅ Yes ❌ Requires expert load balancing DualPipe Parallelism ✅ Yes ✅ Yes ✅ Yes ✅ Efficient All-to-All communication 📌 Summary: Data Parallelism (DP) and Tensor Parallelism (TP) are suitable for conventional Transformer structures but suffer from high communication overhead in MoE structures, limiting training efficiency. DualPipe Parallelism is a specialized computational optimization for MoE and ultra-large LLMs, maximizing computation-communication overlap and overall training efficiency. 5. Application of DualPipe Parallelism in DeepSeek-V3 Training DeepSeek-V3's training combines DualPipe Parallelism + FP8 mixed-precision training: DualPipe computation-communication overlap optimizes expert load balancing in MoE computations; FP8 low-precision training reduces memory usage and enhances computational throughput; InfiniBand + NVLink with DualPipe parallelism improves cross-node communication efficiency, enabling training on 2048+ GPUs.    

Table of ContentsIntroduction to DeepSeekThe DeepSeek TeamUsing DeepSeek OnlineDeepSeek API: A Developer's GuideConclusion Introduction to DeepSeek DeepSeek is a high-performance, open-source, and lightweight large language model (LLM) developed by DeepSeek Inc. It is designed to be versatile and adaptable to various scenarios, offering a wide range of services such as intelligent conversation, reasoning, AI search, file processing, translation, problem-solving, creative writing, and programming assistance. The latest release of DeepSeek, the R1 full-blooded version, matches the performance of OpenAI's o1 and o3 models but at a lower cost. The DeepSeek Team The team behind DeepSeek is composed of experienced professionals in the field of artificial intelligence: Zhang Jianwei: Founder and CEO, who has served as a professor at the University of Hamburg and is a member of the Hamburg Academy of Sciences. He brings extensive research background and practical experience in AI. Zhang Yu: Co-founder and Chief Scientist, with years of research and practical experience in natural language processing and deep learning. Liu Yu: Co-founder and Chief Technology Officer, focusing on the research and application of AI technologies, driving the technological innovation and productization of DeepSeek. DeepSeek from entry to mastery (Tsinghua University) PDF Downlod Web Registration Visit the Official Website: Open your web browser and enter the official DeepSeek website address: https://www.deepseek.com/. Click on “Register”: On the homepage, click the “Register” or “Sign Up” button in the top right corner. Choose a Registration Method: Email Registration: Enter a valid email address, set a password, click “Send Verification Code,” check your email, and enter the code. Phone Number Registration: Input your phone number, set a password, click “Get Verification Code,” and fill in the code you receive via SMS. Third-Party Account Login: You can also choose to log in directly using third-party accounts such as WeChat, QQ, or Weibo. Complete Registration: Once the verification is successful, your registration is complete, and you can access the DeepSeek interface. Mobile Registration Download and Install: Apple iOS: Open the App Store, search for “Deepseek” in the search box, click the “Get” button, enter your Apple ID password or use fingerprint/face recognition for verification, and wait for the download to complete. Android: Open your app store (such as Huawei AppGallery, Xiaomi App Store, or AppBao), search for “Deepseek” in the search bar, click “Install,” and wait for the installation to complete. Register an Account: Email Registration: Enter your email address, set a password, click “Send Verification Code,” check your email, and enter the code. Phone Number Registration: Input your phone number, set a password, click “Get Verification Code,” and fill in the code you receive via SMS. Third-Party Account Login: If you have accounts on WeChat, QQ, or Weibo, click the corresponding icon to log in with one-click authorization. Complete Personal Information: After successful registration, go to your personal center to complete your profile information, such as avatar and nickname, and set privacy options to protect your information security. Using DeepSeek Online Official Web Version DeepSeek can be easily accessed through its official web platform. Here's how you can get started: Visit the Website: Go to https://chat.deepseek.com/. Start a Conversation: Click on the "Start Conversation" button. Register an Account: If you are a first-time user, you will need to register using your phone number, WeChat, or email. Log In and Choose a Model: After registration, log in and select either the V3 or R1 model to begin chatting. Official Mobile Version DeepSeek also offers a mobile app for convenient access on the go: Download the App: Search for "DeepSeek" in your device's app store and download the app. Installation: Follow the on-screen instructions to install the app. Usage: The mobile version operates similarly to the web version. Activate the R1 model as needed and start using it. Other Platforms DeepSeek is also available on third-party platforms that offer additional features and ease of use: SiliconFlow: A platform providing high-performance GenAI cloud services. Register here. Lambda Chat: A fast and efficient AI chat platform. Visit Lambda Chat. Metaso AI Search: An AI-powered search engine that allows for extended thinking and internet search capabilities. Try Metaso AI Search. If you're passionate about the AI field and preparing for AWS or Microsoft certification exams, SPOTO have comprehensive and practical study materials ready for you. Whether you're preparing for AWS's Machine Learning certification (MLA-C01), AI Practitioner certification (AIF-C01), or Microsoft's AI-related exams (AI-900, AI-102), the certification materials I offer will help you study efficiently and increase your chances of passing. Click the links below to get the latest exam dumps and detailed study guides to help you pass the exams and reach new heights in the AI industry: AWS MLA-C01 study materials (click this) AWS AIF-C01 study materials (click this) AWS MLS-C01 study materials (click this) Microsoft AI-900 study materials (click this) Microsoft AI-102 study materials (click this) By achieving these certifications, you'll not only enhance your skills but also stand out in the workplace and open up more opportunities. Act now and master the future of AI! DeepSeek API: A Developer's Guide Official API Address DeepSeek's API is accessible through the following official platform and documentation: Official Platform: https://www.deepseek.com/ API Documentation: DeepSeek API Documentation API Pricing DeepSeek offers competitive pricing for its API services: Promotional Period: Until February 8, 2025, 24:00, the deepseek-chat model API calls are available at historical prices. Post-Promotion: After the promotional period, the pricing will be 2 yuan per million input tokens and 8 yuan per million output tokens. Model Comparison: DeepSeek-V3: Lower cost and high cost-effectiveness, suitable for everyday conversations and basic tasks. DeepSeek-R1: Higher performance but more expensive, ideal for complex reasoning and advanced features. How to Use the DeepSeek API Step 1: Register and Create an API Key Visit the Official Website: Go to https://www.deepseek.com/ to https://api-docs.deepseek.com/. Register an Account: Click on "Register" in the top right corner and complete the registration process. Create an API Key: Log in to your account, navigate to the "API Keys" section, and click "Create API Key." Save the key securely, as it will not be shown again after creation. Step 2: Make API Requests Install Required Libraries: If using Python, install the OpenAI SDK with the following command: pip install openai Configure API Key and Base URL: from openai import OpenAI client = OpenAI(api_key="YOUR_API_KEY", base_url="https://api.deepseek.com") Basic Chat Completion: response = client.chat.completions.create( model="deepseek-chat", messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Tell me about yourself."} ] ) print(response.choices[0].message.content) Advanced Reasoning with DeepSeek-R1: response = client.chat.completions.create( model="deepseek-reasoner", messages=[ {"role": "user", "content": "DeepSeek R1 Guide 2025, Registration, Online Login, and API"} ] ) print(response.choices[0].message.content) Step 3: Monitor API Usage Track Usage: Regularly check your API usage on the DeepSeek platform to avoid exceeding rate limits and budget. Set Alerts: Configure alerts to notify you when your usage approaches certain thresholds. Best Practices for Using DeepSeek API Protect Your API Key: Never share your API Key publicly. If you suspect it has been compromised, revoke it and create a new one. Optimize Requests: Minimize the number of tokens in your requests to reduce costs and improve response times. Handle Errors Gracefully: Implement error handling in your code to manage issues like network errors or invalid responses. Conclusion DeepSeek is a powerful and versatile AI platform that offers a wide range of functionalities through its API. By following the steps outlined in this guide, you can register for DeepSeek, obtain an API Key, and start integrating DeepSeek's capabilities into your applications. Remember to monitor your usage and follow best practices to ensure efficient and secure utilization of the API. Happy coding!