DeepSeek is a relatively new player in the field of artificial intelligence, but it has quickly made waves with its innovative models. Founded in 2023 by Liang Wenfeng, who previously ran a successful hedge fund using AI, DeepSeek has leveraged substantial resources and infrastructure to develop cutting-edge AI models. One of its standout creations is the DeepSeek R1 model, a reasoning model that competes with the best from established companies like OpenAI.
DeepSeek R1 is a reasoning model, which distinguishes it from traditional language models like GPT-4. Reasoning models are designed to solve complex problems by exploring different approaches, and they offer transparency by showing their thought processes. This makes them potentially less prone to errors or “hallucinations” compared to other AI models. The R1 model is particularly notable for its size, boasting 671 billion parameters, with 37 billion active at any given time. This requires substantial computing power, with about 1.3 TB of RAM needed to run it efficiently. Despite these requirements, the model has been downloaded 189,000 times from Hugging Face, indicating strong interest from the AI community.
DeepSeek has also made the R1 model available as a service, allowing users to access its capabilities without needing to invest in expensive hardware. This service has become popular, with an associated iOS app becoming a top download on Apple’s AppStore. The cost-effectiveness of DeepSeek’s service compared to competitors like OpenAI and Google has contributed to its growing popularity and has caused concern among major tech companies.
One of the key features of the DeepSeek R1 model is its ability to provide transparent answers. When faced with a complex question, the model tries different strategies and displays them, unlike some models that only show the final answer. For example, when tasked with finding the prime factors of \(2^{20} + 1\), DeepSeek R1 systematically worked through the problem, although it took 88 seconds to reach a solution. The model’s ability to reveal its reasoning process is a significant advantage, as it allows users to understand how the model arrived at its conclusions.
DeepSeek has also released smaller models that emulate the capabilities of the R1 model but require less hardware. These models, which range from 1.5 billion to 70 billion parameters, can run on consumer GPUs and even CPUs, although their reasoning capabilities are not as strong as the full R1 model. For instance, a smaller model like Qwen-32B struggled with the same prime factorization task, providing an incorrect answer. This highlights the challenges and limitations of smaller models, even when they are designed to mimic larger, more powerful ones.
Despite these challenges, the accessibility of DeepSeek’s models allows a broader audience to experiment with advanced AI capabilities without needing high-end hardware. This democratization of AI technology is one of DeepSeek’s key contributions to the field, enabling more researchers and developers to explore and innovate.
In summary, DeepSeek has emerged as a formidable competitor in the AI landscape, offering powerful reasoning models that challenge established players. Its commitment to transparency and accessibility has made it a popular choice among developers and researchers. As the field of AI continues to evolve, DeepSeek’s innovative approach and cost-effective solutions are likely to play a significant role in shaping the future of AI technology.