Everything You Must Know About DeepSeek AI
Hangzhou, China. A place where a company has put a dent in the AI Universe.
We will know a good lot about DeepSeek as it may be of good use to you in the Tech industry, or simply brag in front of your companions. But before we do that, it is always good to know the history, isn't it? Here is a tiny bit:
DeepSeek was founded in 2023 by Liang Wenfeng, who also serves as the company’s CEO. Liang previously built AI in trading.?
What exactly does DeepSeek do?
It builds advanced AI Reasoning models, with the latest named: DeepSeek R1.
And what exactly is the AI Reasoning model you say?
Consider an AI reasoning model to be like a super-smart assistant that doesn’t just give you answers but also explains how it got there step by step so that you know two things: if your assistant is correct in reasoning, or if you could learn from its reasoning.
Imagine you’re baking a new cake, and instead of just giving you the recipe, this assistant shows you every step—mixing, baking, decorating—so you understand the process. This makes it great for solving tricky problems, whether it’s helping write a computer program, fixing a mistake, or solving a tough math puzzle. It’s like having a very patient and intelligent helper for anything complicated!
Another example of a reasoning model is Open AI's O1.
So, DeepSeek built a Reasoning model and it has become a major disruption force in the world of AI. Because DeepSeek built the model and is now offering high-performance, cost-effective, and open-source AI tools. It is open-source and it is quite effective.
So, it enables development companies like us to build advanced applications, solve complex problems, and innovate without the need for expensive infrastructure or proprietary restrictions.
To give you the exact price range right now:
And what are these things called "input and output tokens"?
Tokens are like tiny pieces of a sentence that an AI uses to understand and respond to you. Think of it like this:
1. Input Tokens: When you ask the AI something, like “What’s the capital of India?”, the AI breaks your question into smaller parts, like words or even parts of words, to figure out what you’re saying.
2. Output Tokens: When the AI answers, like “The capital of India is New Delhi,” it also sends the answer back in little pieces.
You can think of tokens like puzzle pieces that the AI puts together to understand you and to give you a response. Each piece (or token) costs a little bit of money to process, so the longer the question or answer, the more it costs.
This is only for now, and we may see pricing wars in the future. That would be good for the development companies like us and ultimately the whole world in general.
This is the dent we mentioned right at the start.
This affordability makes DeepSeek a strong choice for developers looking to build advanced applications at a lower cost, and its our high belief that many developing companies would start consulting the clients and building on DeepSeek.
Built by a high-quality team:
It is not easy to build a reasoning model, and on top of that, a model with the great advantage of reduced hardware costs.
DeepSeek's major achievement is that they have built it on hardware where their expenses are a fraction of the cost of bigger companies.
DeepSeek AI achieves its cost-efficiency and competitive performance through innovative approaches to infrastructure, training, and deployment.
Here’s how their server technology stands out and how it compares to companies like OpenAI or Meta. (If you are an Infrastructure Tech enthusiast, read these, or move to "8 Additional Takeaways on DeepSeek’s Strategy")
1. These are the key components of DeepSeek’s infrastructure:
DeepSeek has achieved its cost-efficiency and competitive performance through its approaches to infrastructure, training, and deployment.
Here’s how their server technology stands out and how it compares to companies like OpenAI or Meta:
? Optimized GPU Usage:
DeepSeek primarily uses NVIDIA H800 GPUs, a slightly less powerful version of NVIDIA H100 GPUs which are restricted for export to China. And despite this limitation, DeepSeek maximizes its efficiency through advanced training algorithms.
? Hybrid Architecture:
DeepSeek R1 employs a mixture-of-experts (MoE) architecture, allowing only specific parts of the model to activate depending on the task. This greatly reduces computational demands, so the load is less.
2. How Does DeepSeek Keep Costs Low?
? Efficient Hardware Utilization:
DeepSeek achieved its breakthroughs using approximately 2,048 NVIDIA H800 GPUs, which is equivalent to 1,000–1,500 H100 GPUs. This is 20–30x fewer GPUs compared to what OpenAI used to train GPT-4.
? Faster Training Cycles:
The reason why there is efficient hardware utilization is that DeepSeek developed R1 in just 2 months, whereas OpenAI and other companies typically take 12–18 months for similar models. This rapid iteration reduces infrastructure wear and costs.
3. Comparison with other AI Companies
And all of this is built by DeepSeek’s core technical team which primarily consists of recent graduates from top Chinese universities, such as Peking University and Tsinghua University. These individuals have been recognized for their academic achievements, including publications in leading journals and awards at international conferences.
This hiring strategy emphasizes technical abilities over extensive work experience, focused on research and development.
And here's the big news:
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DeepSeek’s AI Assistant app surpassed ChatGPT to become the top free app on the U.S. Apple App Store shortly after its release. This success indicates that the company has tapped into a global audience with a product that meets or exceeds user expectations.
DeepSeek’s rise symbolizes the intensifying competition between Chinese and Western companies in the AI space.
"What took Google and OpenAI years and hundreds of millions of dollars to build… DeepSeek says for them it took just two months and less than $6 million.”
8 Additional Takeaways on DeepSeek’s Strategy
1. Innovative Training Methodology
? Unlike traditional models that rely on supervised fine-tuning (providing examples with step-by-step solutions), DeepSeek R1 uses direct reinforcement learning:
? The model is given problems without solutions and learns autonomously by trial and error. Correct solutions are rewarded, reinforcing the model’s reasoning capabilities.
? This approach mimics human learning and enhances the model’s ability to solve complex, unstructured problems.
2. Focused Iteration:
DeepSeek built upon existing technologies, employing techniques like model distillation to make their systems efficient and competitive.
3. Numerical Stability:
The company solved challenging problems like irregular loss spikes in model training, showcasing advanced technical acumen. DeepSeek R1 has outperformed major AI models like OpenAI’s GPT-4 and O1, as well as Google’s Gemini, in specific tests and tasks.
4. Leveraging Existing Models:
By training on public datasets, including outputs from GPT-based systems, DeepSeek demonstrated how to refine models cost-effectively.
5. Global Developer Adoption:
DeepSeek’s open-source models are now being used by American developers, underscoring its global appeal.
6. Economic Implications:
The company’s success questions the sustainability of billion-dollar investments by other AI labs and highlights the value of lean innovation.
7. Collaboration Potential:
Open-source models like DeepSeek’s invite global collaboration, potentially fostering faster advancements in AI.
8. Rethinking AI Leadership:
DeepSeek’s rise forces other companies to reconsider strategies for staying ahead in the AI race, emphasizing creativity over sheer capital.
The Future of the AI Race
Open-source models, like DeepSeeks, are proving to be powerful disruptors, making advanced AI accessible and affordable for all.
Meanwhile, the rise of open-source AI raises questions about the future of ethical AI, as well as the geopolitical stakes of this rapidly advancing technology. As one expert noted, “The game is shifting. Staying on top may require as much creativity as capital.”
The world is watching to see how players like DeepSeek, OpenAI, and others will adapt to this new era of AI innovation.
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