GoonDeepSeek: The AI Reasoning Model Revolution
DeepSeek is a startup based in China that has caught everyone's attention by releasing an open-source model that can match or surpass the performance of other industry-leading models at a fraction of the cost. In this article, we will explore the evolution of DeepSeek models, focusing on DeepSeek-R1, and how it uses chain of thought reasoning, reinforcement learning, and expert architectures to achieve top-tier performance efficiently.
Introduction to DeepSeek
Introduction to DeepSeek, a startup that has taken the AI world by storm
DeepSeek's rise to fame began when its open-source model took over OpenAI's coveted spot for the most downloaded free app in the US on Apple's App Store. But how did it achieve this feat? The answer lies in its innovative AI reasoning model, DeepSeek R1.
Evolution of DeepSeek Models
DeepSeek R1 is not the first model developed by the company. In fact, there are many DeepSeek models that brought us to this point. The evolution of these models is a fascinating story. DeepSeek version one, a traditional transformer with a focus on feedforward neural networks, was released in January 2024. This was followed by DeepSeek version two, a much larger model with 236 billion parameters, released in June 2024. DeepSeek version two introduced two novel aspects: multi-headed laden attention and the DeepSeek mixture of experts. These innovations made the model fast and performant.
DeepSeek R1: The Reasoning Model
DeepSeek R1 is a reasoning model that performs as well as some of the other models, including OpenAI's own reasoning model, called o1. It can match or even outperform o1 across a number of AI benchmarks for math and coding tasks. What's more remarkable is that DeepSeek R1 is trained with far fewer chips and is approximately 96% cheaper to run than o1. Unlike previous AI models that produce an answer without explaining the why, a reasoning model like DeepSeek R1 solves complex problems by breaking them down into steps.
Chain of Thought Reasoning
Chain of thought reasoning in DeepSeek R1
Before answering a user query, DeepSeek R1 spends time "thinking" by performing step-by-step analysis through a process known as chain of thought. This process involves breaking down the problem, generating insights, backtracking when necessary, and ultimately arriving at an answer.
Reinforcement Learning
DeepSeek R1 combines chain of thought reasoning with reinforcement learning, a capability that arrived at the V3 model of DeepSeek. Reinforcement learning is a process where an autonomous agent learns to perform a task through trial and error without any instructions from a human user. The key hypothesis here is to reward the model for correctness, no matter how it arrived at the right answer, and let the model discover the best way to think all on its own.
Mixture of Experts Architecture
DeepSeek R1 also uses a mixture of experts (MoE) architecture, which is considerably less resource-intensive to train. The MoE architecture divides an AI model into separate entities or sub-networks, which can be thought of as individual experts. Each expert is specialized in a subset of the input data, and the model only activates the specific experts needed for a given task.
Efficiency of DeepSeek R1
Efficiency of DeepSeek R1 compared to other models
So, how does DeepSeek R1 operate at such a comparatively low cost? The answer lies in its use of a fraction of the highly specialized Invidia chips used by its American competitors to train their systems. For example, DeepSeek engineers said that they only need 2000 GPUs to train the DeepSeek V3 model, compared to the 100,000 Nvidia GPUs used by Meta to train its latest open-source model, Llama 4.
Conclusion
DeepSeek R1 is an AI reasoning model that is matching other industry-leading models on reasoning benchmarks while being delivered at a fraction of the cost in both training and inference. Its use of chain of thought reasoning, reinforcement learning, and expert architectures makes it an exciting development in the field of AI. As the field of AI continues to evolve, it will be interesting to see how DeepSeek R1 and other models like it shape the future of artificial intelligence.
