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DeepSeek, a Chinese start-up, has made a significant impact in the open-source large language model (LLM) space with its latest release, DeepSeek V3. This model boasts 671 billion parameters and was developed at a fraction of the cost compared to larger tech firms like Meta and OpenAI. Jim Fan from Nvidia highlighted how resource constraints have driven DeepSeek to innovate effectively.
The training of DeepSeek V3 required only 2.78 million GPU hours, showcasing a remarkable efficiency compared to Meta's Llama 3.1 model, which needed over 30 million GPU hours. This achievement underscores the advancements of Chinese AI firms despite facing US sanctions on semiconductor access. The model's performance has sparked discussions about the future of AI development in a competitive landscape.
• DeepSeek V3 trained with 671 billion parameters at a low cost.
• DeepSeek's efficiency challenges larger firms like Meta and OpenAI.
LLMs are AI models that process and generate human-like text, crucial for applications like chatbots.
GPU hours measure the computational time used for training AI models, indicating efficiency and resource usage.
Open source allows public access to software code, enabling collaboration and innovation in AI development.
DeepSeek is a start-up that has developed an efficient LLM, DeepSeek V3, showcasing innovation in AI.
Nvidia provides the GPUs used for training DeepSeek's models, playing a critical role in AI development.
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