Seek .(US:SKLTY) 3 6 Ke·2025-10-21 07:22Core Insights - DeepSeek has made a significant breakthrough with its new model, DeepSeek-OCR, which fundamentally changes the input paradigm from text to visual data, suggesting that visual inputs may become the mainstream in AI applications [1][14][17] Performance Metrics - DeepSeek-OCR achieves approximately 2500 tokens per second on a single A100-40G card while maintaining a 97% OCR accuracy. It compresses visual context to 1/20 of its original size, with typical usage achieving a compression ratio of less than 1/10 [3][5] - The model can compress an entire page of dense text into just 100 visual tokens, achieving up to 60 times compression on the OmniDocBench benchmark [5][11] Technical Advantages - DeepSeek-OCR boasts fewer parameters, high compression rates, fast processing speeds, and support for 100 languages, making it both theoretically valuable and highly practical [7][11] - The model demonstrates that physical pages (like microfilm and books) are superior data sources for training AI models compared to low-quality internet text [11] Industry Implications - The shift from text to visual inputs could redefine how large language models process information, potentially eliminating the need for traditional tokenizers, which have been criticized for their inefficiencies [16][19] - Karpathy, a prominent figure in AI, emphasizes that the future may see all inputs for AI models being images, enhancing efficiency and information flow [15][25] Community Response - The open-source project has gained significant traction, receiving 4.4k stars on GitHub overnight, indicating strong community interest and support [10][46]