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Time)",{"id":29,"title":30,"content":31,"publishTime":32,"important":9,"sourceName":26,"language":19,"dateString":33},196111,"Jina魔改DeepSeek-OCR，自测14款里页面吞吐第一","动察 Beating AI 快讯，Jina AI 发布文档解析模型 jina-ocr-v1，可以把 PDF、扫描件、表格和图表直接转成 Markdown。它基于 DeepSeek-OCR 做后训练，沿用其约 34 亿总参数、解码时每个 Token 激活约 5.7 亿参数的 MoE 架构，并加入 FastMTP 推测解码。\nJina 自测中，jina-ocr-v1 在 OmniDocBench v1.6 得 91.14，比 DeepSeek-OCR-2 高 0.89 分；在 olmOCR-Bench 得 83.4，比它实际基于的 DeepSeek-OCR 底模高 7.4 分。\n吞吐量是 Jina 主打的卖点之一。单张 A100、并发 32 时，它达到 2.57 页\u002F秒，在 Jina 测试的 14 个系统中最高，比 DeepSeek-OCR 的 2.10 页\u002F秒高约 22%。\n模型权重已经放上 Hugging Face，采用 CC BY-NC 4.0，商业使用需要联系 Jina。",1789789680000,"Sat Sep 19 2026 03:48:00 GMT+0000 (Coordinated Universal Time)",{"id":35,"title":36,"content":37,"publishTime":38,"important":9,"sourceName":26,"language":19,"dateString":39},187208,"FreeToken开源：一张5090跑DeepSeek-V4-Flash，最高25 Token\u002Fs","动察 Beating AI 快讯，伯克利、MIT 等研究者开源 FreeToken。这是一个专门为本地设备设计的 MoE（混合专家模型）推理引擎，主要解决超大模型显存装不下的问题。它会把部分权重放在系统内存，并让 CPU 和 GPU 一起参与推理。\nFreeToken 会把模型的大量专家权重放在系统内存，只把一部分缓存在显存。遇到显存里没有的专家时，它会根据 PCIe 和 CPU 内存带宽，动态决定是把权重搬到 GPU，还是直接交给 CPU 计算。这样 CPU、GPU、系统内存和 PCIe 可以一起参与推理。\n论文实测，一台配备 RTX 5090 的游戏台式机运行 284B 参数的 DeepSeek-V4-Flash，可以达到 22–25 Token\u002Fs。这款模型每生成一个 Token 实际只激活约 13B 参数，因此特别适合这种 CPU、GPU 混合推理。\nFreeToken 还在 8GB 显存的 RTX 4060 笔记本上把 35B 模型跑到 39.3 Token\u002Fs；单张 96GB RTX PRO 6000 则可以运行 753B 参数的 GLM-5.2，速度达到 llama.cpp 的约两倍。项目目前支持超过 20 款 MoE 模型，并已采用 Apache 2.0 许可证开源。",1788248220000,"Tue Sep 01 2026 07:37:00 GMT+0000 (Coordinated Universal Time)",{"id":41,"title":42,"content":43,"publishTime":44,"important":9,"sourceName":26,"language":19,"dateString":45},185594,"腾讯混元Hy4重做底层：DeepSeek稀疏注意力+智谱IndexCache一起上","动察 Beating AI 快讯，腾讯混元 Hy4 preview 这次除了把模型做大，底层架构也大改了一轮。上一代 Hy3 使用传统全注意力，Hy4 改成了 Gated DSA，也就是 DeepSeek Sparse Attention 的门控版本。模型面对超长文本时，不再把前面所有内容都重新算一遍，而是先挑出最相关的部分重点处理。\nHy4 还给 DSA 加上了 IndexCache。模型每一层原本都要重新判断「哪些内容最重要」，现在部分层可以直接复用前面已经挑好的结果，少做很多重复计算。\n腾讯明确表示，这套设计受到 DeepSeek 和 GLM 启发。\n另一处变化是残差结构。传统 Transformer 可以粗略理解成只有一条信息主干，Hy4 用 iHC 把它扩成了 4 条，让模型在很深的网络里能同时保留更多信息。MoE 专家数量也从 Hy3 的 192 个增加到 256 个路由专家 + 1 个共享专家，每个 Token 选择 8 个路由专家，同时始终经过共享专家。",1787902320000,"Fri Aug 28 2026 07:32:00 GMT+0000 (Coordinated Universal Time)",{"id":47,"title":48,"content":49,"publishTime":50,"important":9,"sourceName":10,"language":19,"dateString":51},184197,"DeepSeek API毛利率高达82.9%，公司整体毛利率达44.6%","动察 Beating AI 快讯，据 The Information 报道，DeepSeek 正寻求以约 5000 亿元人民币估值进行第二轮融资，计划再融资 500 亿元人民币。数据显示，2026 年 1 月至 7 月，DeepSeek 营收达到 4.75 亿元人民币（约 7070 万美元），约为其 2025 年全年营收的 10 倍；同期净亏损收窄至 7.15 亿元人民币，低于 2025 年全年的 9.35 亿元人民币。 盈利能力方面，DeepSeek 整体毛利率达 44.6%，其中 API 业务毛利率高达 82.9%，主要得益于 AI 基础设施优化，使其能够以更少芯片运行模型并维持较低推理成本。随着 V4 系列，尤其是 V4-Flash 获得全球开发者广泛采用，公司营收增长加速，上月年化营收据称已达到 4 亿至 5 亿美元。 此外，DeepSeek 近期上调 API 价格以强化商业化，V4-Pro 高峰期输出价格从 0.87 美元升至 3.96 美元，非高峰期为 1.98 美元，但仍低于 Kimi K3 的 15 美元和 Claude Opus 5 的 25 美元。与此同时，公司 AI 基础设施投入从 2025 年的 12 亿元人民币大幅增至今年前 7 个月的 110 亿元人民币，主要用于服务器租赁及 AI 芯片、算力设备采购。 DeepSeek 此前今年 6 月完成首轮外部融资，融资规模约 500 亿元人民币，目前已聘请投行筹备明年在上海 IPO。",1787729040000,"Wed Aug 26 2026 07:24:00 GMT+0000 (Coordinated Universal 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