Liang Wen Fung, a Chinese entrepreneur, built a successful quantitative trading firm, leveraging AI and custom-built supercomputers. His subsequent startup, DeepSeek, achieved a breakthrough in AI development, creating highly effective models using significantly less computing power and resources than competitors like OpenAI. This cost-effective approach, achieved through innovative techniques, challenged the industry’s assumptions about the resources needed for advanced AI and democratized access to powerful AI tools. DeepSeek’s success serves as a wake-up call for established tech companies, highlighting the potential for smaller, more agile teams to compete effectively. The story underscores the importance of innovative engineering and efficient resource management in AI development.

AI Revolution: A Study Guide

Quiz

Instructions: Answer each question in 2-3 sentences.

1. What is the significance of DeepSeek’s V3 model, and what hardware was it trained on?
2. Describe Leang Wen Fung’s early life and how it influenced his career choices.
3. How did Leang Wen Fung utilize his math skills during the 2008 financial crisis?
4. Explain the concept of quantitative trading and how Leang Wen Fung applied it.
5. What was the significance of High Flyer’s Firefly supercomputers?
6. Why did DeepSeek shift its focus from finance to general artificial intelligence (AGI)?
7. How did DeepSeek V2 achieve comparable performance to GPT-4 Turbo at a fraction of the cost?
8. Describe DeepSeek’s “mixture of experts” approach.
9. What was unique about DeepSeek’s approach to team building and company structure?
10. How did DeepSeek’s success serve as a wake-up call for the American tech industry?

Quiz Answer Key

1. DeepSeek’s V3 model is significant because it achieved performance comparable to top models like GPT-4 using only 248 Nvidia h800 GPUs, considered basic equipment, challenging the notion that advanced AI requires massive resources. This breakthrough demonstrated efficient AI development is possible with limited hardware.
2. Leang Wen Fung showed an early talent for math, spending hours solving puzzles and equations. This passion for numbers and problem-solving shaped his entire career, leading him to pursue electronic information engineering and algorithmic trading.
3. During the 2008 financial crisis, Leang Wen Fung used his math skills to develop AI-driven programs that could analyze markets faster and smarter than humans, focusing on machine learning to spot patterns in stock prices and economic reports.
4. Quantitative trading uses mathematical models to identify patterns in financial data, like stock prices and economic reports, to predict market trends. Leang Wen Fung developed computer programs based on this approach, using algorithms to make fast, data-driven trading decisions.
5. The Firefly supercomputers were crucial for High Flyer because they provided the massive computing power required to train their AI trading systems. Firefly One and Two enabled faster and more sophisticated AI models to make smarter, quicker trades.
6. DeepSeek shifted its focus from finance to general artificial intelligence (AGI) to pursue AI that can perform a wide range of tasks as well as humans, going beyond the narrow applications of AI in the finance sector.
7. DeepSeek V2 achieved comparable performance to GPT-4 Turbo at a fraction of the cost by using a new multi-head latent attention approach and a mixture of experts methodology, which optimized information processing, reduced the need for extensive resources and made the AI more efficient.
8. DeepSeek’s “mixture of experts” approach involves using only specific AI models to answer particular questions, rather than activating the entire system, thus saving significant resources and making it much cheaper to operate.
9. DeepSeek focused on hiring young, bright talent, especially recent graduates, and implemented a flat management structure to encourage innovation and give team members more autonomy, allowing for rapid decision-making and a bottom-up approach to work.
10. DeepSeek’s success served as a wake-up call for the American tech industry by demonstrating that innovation and clever engineering can allow smaller companies to compete effectively with well-funded competitors, highlighting the need for US companies to be more efficient and competitive.

Essay Questions

1. Analyze the factors contributing to DeepSeek’s rapid rise in the AI industry. Consider their technological innovations, business strategies, and team-building approaches.
2. Compare and contrast DeepSeek’s approach to AI development with that of traditional tech giants. How do their different strategies impact their ability to innovate and compete?
3. Discuss the broader implications of DeepSeek’s achievements for the AI industry and global technological competition. How might their breakthroughs influence the future of AI research and development?
4. Explore the role of Leang Wen Fung’s background and personal vision in shaping the success of both High Flyer and DeepSeek.
5. Evaluate the significance of DeepSeek’s open-source approach and its potential to democratize access to advanced AI technologies.

Glossary of Key Terms

• AI (Artificial Intelligence): The theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
• AGI (Artificial General Intelligence): A type of AI that can perform any intellectual task that a human being can, capable of understanding, learning, and applying knowledge across a wide range of domains.
• Algorithm: A set of rules or instructions that a computer follows to solve a problem or perform a task.
• Deep Learning: A type of machine learning that uses artificial neural networks with multiple layers (deep networks) to analyze data and identify complex patterns, improving with experience.
• GPU (Graphics Processing Unit): A specialized electronic circuit originally designed to accelerate the creation of images but is now used for data processing and machine learning due to its capacity to perform multiple calculations simultaneously.
• Machine Learning: A subfield of AI that focuses on the development of systems that can learn from and make predictions based on data, without being explicitly programmed.
• Mixture of Experts: An AI technique that combines multiple specialized models, using the most appropriate one to answer a given query, resulting in more efficient and cost-effective computation.
• Multi-head Latent Attention: An AI technique that allows a model to focus on different parts of the input data, enabling it to understand context and relationships more effectively.
• Open Source: A method of software development and distribution that allows anyone to access, modify, and share the source code.
• Quantitative Trading: A trading strategy that uses mathematical and statistical models to analyze financial data and make automated decisions.
• Recession: A significant decline in economic activity spread across the economy, lasting more than a few months, normally visible in real GDP, real income, employment, industrial production, and wholesale-retail sales.

DeepSeek: A Chinese AI Disruption

Okay, here’s a detailed briefing document summarizing the key themes and ideas from the provided text, along with relevant quotes:

Briefing Document: DeepSeek and the Shifting AI Landscape

Executive Summary: This document analyzes the rise of DeepSeek, a Chinese AI startup that has disrupted the established AI development paradigm. Led by Leang Wen Fung, DeepSeek has achieved groundbreaking results in AI performance while utilizing significantly fewer resources than its Western counterparts, prompting a reevaluation of development strategies and challenging the dominance of established tech giants. The company’s success highlights the power of innovative engineering, efficient resource management, and a unique approach to talent acquisition and organizational structure.

Key Themes and Ideas:

1. Disruptive Innovation with Limited Resources:

• DeepSeek’s V2 and V3 models have demonstrated that top-tier AI performance can be achieved without massive budgets or the most advanced hardware.
• Quote: “deep seek just taught us that the answer is less than people thought you don’t need as much cash as we once thought”
• DeepSeek V3 was trained on only 2,000 low-end Nvidia H800 GPUs, outperforming models trained on much more expensive hardware.
• Quote: “deep seek V3 was built using just 248 Nvidia h800 GPU news which many consider basic equipment in AI development. this was very different from Big Silicon Valley companies which usually use hundreds of thousands of more powerful gpus.”
• This challenges the conventional wisdom that AI breakthroughs require massive computational power and immense financial investment.
• DeepSeek’s approach highlights the importance of innovative algorithms, efficient training methods, and smart resource allocation.
• Quote: “deep seek V3 success came from smart new approaches like FPA mixed Precision training and predicting multiple words at once. these methods helped deep seek use less computing power while maintaining quality.”

1. The Rise of Leang Wen Fung:

• Leang Wen Fung’s background in mathematics, finance, and AI provides a unique perspective and understanding of the technological landscape.
• Quote: “raised in a modest household by his father a primary school teacher leang showed an early talent for mathematics while other kids played games or Sports he spent hours solving puzzles and equations finding joy in untangling their secrets”
• His early experience in algorithmic trading during the 2008 financial crisis shaped his belief in AI’s transformative power beyond finance.
• His decision to turn down a lucrative offer at DJI to pursue AI demonstrates his visionary thinking.
• His journey from quantitative trading to AGI reflects his long-term strategic thinking and his willingness to take risks.
• His emphasis on innovation led him to build the powerful “Firefly” supercomputers, later used to develop DeepSeek’s AI models.

1. The Power of Efficient Training and Architecture:

• DeepSeek’s AI models achieve high performance with lower computational cost through innovative techniques.
• Quote: “deep seek V2 combined two breakthroughs the new multi-head latent attention helped to process information much faster while using less computing power”
• The “mixture of experts” method allows models to activate only the necessary parts for specific tasks, reducing resource consumption.
• Quote: “when someone asks a question the system figures out which expert model is best suited to answer it and only turns on that specific part”
• FPA mixed precision training and predicting multiple words at once contributed to the efficient training of DeepSeek V3.
• The lower cost of training and processing for DeepSeek models has democratized access to advanced AI.

1. Lean Team Structure and Talent Strategy:

• DeepSeek’s small, young team of engineers and researchers has achieved remarkable results, challenging the notion that bigger teams are always better.
• Quote: “deep seek stood out for its small young team they had just 139 engineers and researchers much smaller than their competitor open AI”
• Leang Wen Fung prioritized hiring young talent with fresh perspectives, fostering innovation and a collaborative work environment.
• The flat organizational structure, characterized by minimal management layers and bottom-up decision-making, promotes quick action and creativity.
• Quote: “leang said the company worked from the bottom up letting people naturally find their roles and grow in their own way without too much control from above.”

1. Challenging the Status Quo:

• DeepSeek’s breakthroughs have shaken the established AI landscape, forcing established tech giants to re-evaluate their strategies.
• Quote: “scale ai’s founder Alexander Wang shared his honest thoughts about it he said deep seek succcess was a tough wakeup call for American tech companies while the US had become too comfortable China had been making progress with cheaper and faster methods”
• The success of a smaller player highlights the power of strategic planning and efficient resource allocation in a competitive market.
• DeepSeek’s open-source approach further contributes to its impact by enabling collaboration and dissemination of its breakthroughs.
• Quote: “Mark Anderson a prominent investor called Deep seek R1 one of the most amazing breakthroughs he had ever witnessed he was especially impressed that it was open source and could transform the AI industry”

Impact and Implications:

• DeepSeek’s success demonstrates that innovation and efficiency are key to AI development, potentially leading to a more democratized and competitive industry.
• Its focus on low-resource solutions could have important implications for AI deployment in resource-constrained environments.
• The company’s open-source approach fosters wider collaboration within the AI community, potentially accelerating the pace of innovation.
• The emergence of DeepSeek represents a shift in the global AI landscape, potentially challenging the dominance of established Western tech companies.

Conclusion:

DeepSeek’s rise is a significant development in the AI world. It demonstrates that revolutionary progress can be achieved by focusing on innovation, efficient resource management, strategic team building, and a willingness to challenge the status quo. Leang Wen Fung’s leadership and his team’s groundbreaking work have not only disrupted the industry but have also set a new benchmark for AI development. This has profound implications for how AI technologies are developed and deployed in the future.

DeepSeek: A Chinese AI Revolution

Frequently Asked Questions about DeepSeek and its Impact on AI

1. What is DeepSeek and why has it gained so much attention recently? DeepSeek is a Chinese AI startup founded by Liang Wen Fung, initially focusing on quantitative trading and later pivoting to general AI development. It gained notoriety for its impressive AI models, notably the V2 and V3, which achieved comparable or better performance than models from major tech companies (like OpenAI’s GPT-4) but with significantly lower costs and resource requirements. This has led to a re-evaluation of how AI is developed and deployed.
2. How did DeepSeek achieve comparable AI performance with significantly fewer resources than its competitors? DeepSeek achieved breakthroughs by employing several key strategies. First, they used “multi-head latent attention,” which allows their models to process information faster and more efficiently. They also implemented a “mixture of experts” approach, where the model only activates the specific parts needed to answer a question, reducing computational load. Furthermore, DeepSeek utilized “FPA mixed precision training” and optimized training methods to minimize computing power needs. This allowed them to create high-performing AI models with far less hardware and cost than rivals.
3. Who is Leang Wen Fung, and what is his background? Leang Wen Fung is the founder of DeepSeek, a Chinese AI pioneer. Born in 1985 in China, he displayed early aptitude in mathematics. He studied electronic information engineering at Xiang University. His early career involved using math and machine learning to develop advanced quantitative trading systems. He later moved into general AI development, applying his problem-solving skills to create DeepSeek and its groundbreaking AI models. He is known for his focus on innovation and his ability to assemble a talented and agile team.
4. How did DeepSeek’s approach to team building contribute to its success? DeepSeek’s success is partly attributed to its unique approach to team building. They intentionally assembled a small team of young, talented individuals, often recent graduates from top universities. This lean structure with few management layers, empowered team members to take ownership and innovate without excessive bureaucracy. They encouraged a bottom-up approach, where team members naturally found their roles, creating an agile and efficient development process.
5. How did DeepSeek disrupt the AI industry, and what was the reaction from other companies? DeepSeek disrupted the AI industry by demonstrating that top-tier AI performance could be achieved with significantly lower costs and resources. Their approach challenged the prevailing notion that massive budgets and computational power were necessary for advancements in AI. This forced major tech companies, especially in the US, to re-evaluate their strategies. Industry leaders like Scale AI’s founder, Alexander Wang, acknowledged that DeepSeek was a “wakeup call” to the sector. The breakthrough promoted the “democratization of AI,” making it accessible to smaller businesses and startups.
6. What are the key technologies or methods DeepSeek developed that make them stand out? DeepSeek is known for several advanced technologies and approaches that set them apart. Key innovations include the “multi-head latent attention” mechanism for more efficient information processing, the “mixture of experts” method to activate only relevant model sections, and the “FPA mixed precision training” technique that reduces computational demands. These technical innovations allowed DeepSeek to train high-performing models using significantly less hardware and energy compared to its competitors.
7. Why did DeepSeek choose to open-source its AI model and how does that impact the AI community? DeepSeek adopted an open-source approach to its AI models to foster collaboration and innovation within the AI community. By making their model accessible, they enabled researchers and developers worldwide to experiment, learn, and contribute to AI advancements. This move helped democratize access to advanced AI technology and further accelerate the overall pace of innovation in the field. This openness created opportunities for smaller companies and new players to enter the space.
8. What impact does DeepSeek’s success have on the future of AI development and its accessibility? DeepSeek’s success demonstrated that cutting-edge AI development can be achieved without the vast resources traditionally associated with it, potentially lowering the barrier to entry for smaller businesses, research institutions, and startups. Their efficient techniques also underscored that future AI development can be more sustainable, as it reduces energy consumption and the environmental footprint of data centers. This has paved the way for more equitable access to AI technologies, making advanced models usable by various organizations and on diverse platforms.

DeepSeek’s AI Breakthrough

DeepSeek, a relatively unknown Chinese startup, made a significant breakthrough in the AI world with their V3 model, challenging tech giants and redefining AI development.

Here are key aspects of their achievement:

• Model Performance: DeepSeek’s V3 model, trained on only 2,000 low-end Nvidia h800 GPUs, outperformed many top models in coding, logical reasoning, and mathematics. This model performed as well as OpenAI’s GPT-4, which was considered the best AI system available.
• Resource Efficiency:DeepSeek V3 was trained with significantly fewer resources than other comparable models. For example, its training took less than 2.8 million GPU hours, while Llama 3 needed 30.8 million GPU hours.
• The training cost for DeepSeek V3 was about 5.58 million Yuan, compared to the $63 to $100 million cost of training GPT-4.
• DeepSeek achieved this efficiency through new approaches such as FPA mixed precision training and predicting multiple words at once.
• Cost-Effectiveness: DeepSeek’s V2 model matched giants like GPT-4 Turbo but cost 1/70th the price at just one Yuan per million words processed. This was made possible by combining multi-head latent attention with a mixture of experts method. This allowed the model to perform well without needing as many resources.
• Team and Approach:DeepSeek had a small team of 139 engineers and researchers, much smaller than competitors like OpenAI, which had about 1,200 researchers.
• The company focused on hiring young talent, especially recent graduates, and had a flat organizational structure that encouraged new ideas and quick decision-making.
• DeepSeek also embraced open-source ideals, sharing tools to collaborate with researchers worldwide.

DeepSeek’s success demonstrates that innovation and clever engineering can level the playing field, allowing smaller teams to compete with well-funded competitors. Their work challenges the notion that advanced AI requires massive resources and budgets. Their focus on efficient methods also addresses the environmental concerns associated with AI development by reducing energy consumption. DeepSeek’s accomplishments serve as a wake-up call for the industry, particularly for American tech companies.

DeepSeek’s Cost-Effective AI

DeepSeek’s approach to AI development has demonstrated that cost-effective AI is not only possible but can also be highly competitive. Here’s a breakdown of how DeepSeek achieved this:

• Resource Efficiency: DeepSeek’s V3 model achieved high performance with significantly fewer resources compared to other top AI models. It was trained on only 2,000 low-end Nvidia h800 GPUs, while many larger companies use hundreds of thousands of more powerful GPUs. This shows that advanced AI does not necessarily require massive computing power.
• The training of DeepSeek V3 took less than 2.8 million GPU hours, compared to the 30.8 million GPU hours needed for Llama 3.
• The training cost of DeepSeek V3 was about 5.58 million Yuan, whereas training GPT-4 cost between $63 to $100 million.
• Innovative Methods: DeepSeek employed several innovative methods to reduce costs and increase efficiency.
• FPA mixed precision training and predicting multiple words at once allowed them to maintain quality while using less computing power.
• Multi-head latent attention and a mixture of experts method enabled the V2 model to process information faster and more efficiently. With the mixture of experts method, the system only activates the specific expert model needed to answer a question, reducing overall computational load.
• Cost Reduction:
• DeepSeek’s V2 model matched the performance of models like GPT-4 Turbo but cost only one Yuan per million words processed, which is 1/70th of the price.
• The company’s Firefly system included energy-saving designs and custom parts that sped up data flow between GPUs, cutting energy use by 40% and costs by half compared to older systems.
• Impact on the Industry: DeepSeek’s approach has challenged the idea that only well-funded tech giants can achieve breakthroughs in AI. Their success has demonstrated that smaller teams with clever engineering and innovative methods can compete effectively. This has led to a re-evaluation of AI development strategies in the industry and a focus on more cost-effective approaches. The reduced cost and resource needs also open up opportunities for smaller businesses and researchers to work with advanced AI tools.
• Environmental Benefits: The reduced energy consumption of DeepSeek’s AI models also addresses growing concerns about the environmental costs of AI, by showing how to make AI more environmentally friendly. This is significant because data centers use more electricity than entire countries.

In summary, DeepSeek has demonstrated that cost-effective AI is achievable through innovative methods, efficient resource utilization, and a focus on smart engineering. This has significant implications for the industry, making advanced AI more accessible and sustainable.

DeepSeek: Efficient Chinese AI Innovation

Chinese AI innovation, exemplified by DeepSeek, is making significant strides and challenging the dominance of traditional tech giants. Here’s a breakdown of key aspects:

• Resource Efficiency: DeepSeek has demonstrated that top-tier AI can be developed with significantly fewer resources. Their V3 model was trained on only 2,000 low-end Nvidia h800 GPUs, outperforming models trained on far more powerful hardware. This contrasts with the resource-intensive methods of many Western companies. This is a significant innovation because it shows that it is possible to achieve top-tier AI without enormous computing power.
• DeepSeek V3’s training took less than 2.8 million GPU hours, compared to 30.8 million GPU hours for Llama 3, while costing around 5.58 million Yuan compared to the 63 to $100 million for training GPT-4.
• Cost-Effectiveness: DeepSeek’s models are not only resource-efficient, but also highly cost-effective. Their V2 model matched the performance of models like GPT-4 Turbo but at 1/70th of the cost, demonstrating that advanced AI can be made more accessible. This cost-effectiveness was achieved through methods like:
• Multi-head latent attention which processes information faster, and a mixture of experts method, which uses only the necessary parts of the system to answer a question.
• DeepSeek’s Firefly system, used for financial trading, also incorporated energy-saving designs and custom parts which cut energy use by 40% and costs by half compared to older systems.
• Innovative Approaches: DeepSeek employs innovative methods in their AI development. This includes techniques like FPA mixed precision training and predicting multiple words at once, which help maintain quality while using less computing power. These methods represent a departure from the traditional “bigger is better” approach, demonstrating the value of clever engineering and efficient algorithms.
• Team Structure and Culture: DeepSeek’s small, young team of 139 engineers and researchers, much smaller than its competitors, is a key aspect of their success. The company fosters a flat organizational structure that encourages new ideas and quick decision-making, which enables them to be nimble and innovative. This approach contrasts sharply with the larger, more bureaucratic structures of many tech giants.
• Open Source and Collaboration: DeepSeek embraces open-source ideals, sharing tools and collaborating with researchers worldwide. This collaborative approach helps accelerate innovation and promotes wider accessibility to advanced AI.
• Impact on the Global AI Landscape: DeepSeek’s achievements serve as a wake-up call for the global AI industry, particularly for American tech companies. Their success has shown that smaller teams with innovative methods can compete effectively with well-funded competitors, and has challenged the idea that only large companies with massive resources can achieve breakthroughs in AI. This demonstrates that Chinese AI firms are not just keeping pace with, but are actively pushing the boundaries of AI innovation.
• Financial Innovation: The company initially focused on developing AI for financial trading and developed the Firefly supercomputers, demonstrating how AI can be applied to quantitative trading. This background provided a foundation for their later push into general AI.

In summary, Chinese AI innovation, as represented by DeepSeek, is characterized by a focus on resource efficiency, cost-effectiveness, innovative methods, and a unique team structure. This has allowed them to achieve significant breakthroughs that are reshaping the global AI landscape and challenging established industry norms.

DeepSeek’s Efficient AI Development

Efficient AI development is exemplified by DeepSeek’s approach, which prioritizes resourcefulness, cost-effectiveness, and innovative methods to achieve high performance. This approach challenges the traditional notion that advanced AI requires massive resources and large teams. Here’s a breakdown of how DeepSeek achieves efficiency in AI development:

• Resource Optimization: DeepSeek has demonstrated that top-tier AI can be developed with significantly fewer resources.
• Their V3 model was trained using just 2,000 low-end Nvidia h800 GPUs. This is in stark contrast to many large companies that use hundreds of thousands of more powerful GPUs.
• The training of DeepSeek V3 required less than 2.8 million GPU hours, while Llama 3 needed 30.8 million GPU hours, showing the significant reduction in computing resources.
• The cost to train DeepSeek V3 was approximately 5.58 million Yuan, whereas training GPT-4 cost between $63 to $100 million.
• Cost-Effectiveness: DeepSeek’s AI models are not only resource-efficient, but also highly cost-effective.
• Their V2 model matched the performance of models like GPT-4 Turbo but at just 1/70th of the cost, at one Yuan per million words processed.
• The company’s Firefly system cut energy use by 40% and costs by half compared to older systems by using smarter cooling methods, energy-saving designs, and custom parts that sped up data flow between GPUs.
• Innovative Techniques: DeepSeek employs several innovative methods to enhance efficiency.
• They use FPA mixed precision training and predict multiple words at once to maintain quality while using less computing power.
• Their V2 model uses multi-head latent attention to process information faster and a mixture of experts method to activate only the necessary parts of the system, reducing computational load.
• Team Structure and Culture: DeepSeek’s small, young team of 139 engineers and researchers promotes efficiency. This is a key difference from competitors with much larger teams.
• The company fosters a flat organizational structure that encourages new ideas and quick decision-making, which allows them to be more nimble and innovative.
• They prioritize young talent, especially recent graduates, who bring fresh perspectives and a willingness to challenge established norms.
• Impact on the AI Industry: DeepSeek’s approach has had a significant impact on the AI industry.
• Their success has demonstrated that smaller teams with clever engineering and innovative methods can compete effectively with well-funded competitors.
• This approach has challenged the idea that advanced AI development is only possible for large companies with vast resources.
• The reduced cost and resource needs make advanced AI more accessible to smaller businesses and researchers.
• The focus on energy efficiency addresses environmental concerns associated with AI development.
• Open Source and Collaboration: DeepSeek embraces open-source ideals and shares tools to collaborate with researchers worldwide. This promotes faster innovation and wider accessibility to advanced AI technology.

In summary, efficient AI development, as demonstrated by DeepSeek, involves optimizing resource use, employing innovative methods, fostering a nimble team structure, and embracing collaboration. This approach is reshaping the AI landscape by showing that high-performance AI can be achieved cost-effectively and sustainably.

DeepSeek: Democratizing AI Through Efficiency

AI democratization, as evidenced by DeepSeek’s achievements, is the concept of making advanced AI technology more accessible to a wider range of individuals and organizations, not just the large tech companies with vast resources. DeepSeek’s innovative approach has shown that high-quality AI can be developed with fewer resources and at a lower cost, thereby breaking down barriers to entry in the AI field.

Key aspects of AI democratization, based on DeepSeek’s example, include:

• Reduced Costs: DeepSeek’s models are significantly cheaper to train and operate than those of many competitors.
• Their V2 model matched the performance of models like GPT-4 Turbo but at only 1/70th of the cost, at one Yuan per million words processed.
• The training cost of DeepSeek V3 was about 5.58 million Yuan, compared to the 63 to $100 million it cost to train GPT-4.
• By using methods such as the mixture of experts, they reduce computational load and costs.
• The Firefly system cut energy use by 40% and costs by half compared to older systems by using smarter cooling methods, energy-saving designs, and custom parts that sped up data flow between GPUs.
• Resource Efficiency: DeepSeek’s models demonstrate that top-tier AI can be developed with significantly fewer resources.
• DeepSeek V3 was trained on just 2,000 low-end Nvidia h800 GPUs, while many larger companies use hundreds of thousands of more powerful GPUs.
• The training of DeepSeek V3 required less than 2.8 million GPU hours, while Llama 3 needed 30.8 million GPU hours, which shows a significant reduction in computing resources.
• Innovative Methods: DeepSeek employs innovative methods to enhance efficiency and reduce costs.
• Techniques like FPA mixed precision training and predicting multiple words at once help maintain quality while using less computing power.
• Multi-head latent attention and a mixture of experts method, enable DeepSeek’s V2 model to process information faster and more efficiently.
• Accessibility: By making AI more affordable and less resource-intensive, DeepSeek has made advanced AI tools more accessible to smaller businesses, researchers, and startups.
• This shift has challenged the idea that advanced AI is only attainable by well-funded tech giants.
• The ability to achieve high performance with fewer resources means that more organizations can now afford to use advanced AI technologies.
• Open Source and Collaboration: DeepSeek embraces open-source ideals, sharing tools and collaborating with researchers worldwide. This helps to accelerate innovation and allows more people to benefit from advanced AI.
• Team Structure and Culture: DeepSeek’s success is partly attributed to its small, young team of 139 engineers and researchers, which contrasts sharply with the larger teams of its competitors.
• The company’s flat organizational structure encourages new ideas and quick decision-making.
• The focus on young talent enables the company to innovate quickly and efficiently.
• Environmental Benefits: DeepSeek’s focus on efficient AI development has resulted in models that consume less energy, thus contributing to more environmentally sustainable AI practices.

In summary, AI democratization, as illustrated by DeepSeek, involves making AI more accessible, affordable, and sustainable. This is achieved through innovative methods, efficient resource utilization, and a collaborative approach, which is leveling the playing field and creating opportunities for a wider range of individuals and organizations to participate in the AI revolution.

By Amjad Izhar
Contact: amjad.izhar@gmail.com
https://amjadizhar.blog

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