| | | 全球主要股票指数 | | | | --- | --- | --- | --- | --- | | 市场 | 名称 | 现价 | 涨跌 | 涨跌幅 | | 欧美 | 道琼斯工业平均 | 49499.2 | 17.05 | 0.03% | | | 纳斯达克 | 22878.38 | -273.7 | -1.18% | | | 标普500 | 6908.86 | -37.27 | -0.54% | | | 欧洲斯托克50 | 6161.56 | -11.76 | -0.19% | | | 英国富时100 | 10846.7 | 40.29 | 0.37% | | | 法国CAC40 | 8620.93 又 | 61.86 | 0.72% | | | College International Party of Children Company of | 25289.02 8 ogudata 3.08 | | 0.45% | | | 俄罗斯RTS 上证指数 | 1137.83 4146.63 | -13.78 -0.6 | -1.20% -0.01% | | | 深证成指 创业板指 恒生 清教 | 145 ...

Core Insights - The core argument emphasizes the importance of "model fine-tuning" and "model inference application" for companies to achieve high-quality development through AI technology [1][2]. Group 1: Model Development and Application - The AI model lifecycle includes data acquisition, preprocessing, training, fine-tuning, and inference, with model training being the most critical phase [1]. - Due to the lack of specialized domain data, foundational models require "model fine-tuning" to adapt to specific industry needs, transforming general capabilities into specialized applications for sectors like healthcare, finance, and manufacturing [1]. Group 2: Efficient Implementation Strategies - Companies are advised to leverage existing foundational models from specialized tech firms like DeepSeek and Huawei, rather than investing heavily in initial data acquisition and training [2]. - The architecture of AI PC, centered around GPUs, offers significant computational advantages, enabling the development of personalized AI assistants for individuals [2]. Group 3: AI's Role in Business Transformation - AI is positioned as a core infrastructure rather than a mere IT tool, with the next competitive battleground for companies being the integration of data algorithms and computational efficiency [3]. - AI serves as a second engine for growth, reshaping products, services, and operational models, thereby enhancing revenue and profit margins [3]. - By optimizing internal processes and reducing operational costs, AI creates significant competitive advantages and barriers to entry for businesses [3].

机器之心发布 机器之心编辑部 当前 AI 领域呈现「端云并发」的发展态势，端侧与云侧大模型各展所长，共同推动着智能发展与应用落地的边界。端侧模型实现本地毫秒级实时响应，云 侧模型依托强大算力支持复杂大规模推理，而两者都离不开高效的推理系统支撑。 在 GTC 2025 上，NVIDIA CEO 黄仁勋强调，大模型计算正从预训练转向推理优化阶段。 随着产业落地加速，推理计算需求正呈现爆发式增长，如何在性 能、成本和响应速度间取得平衡成为关键工程挑战，推理系统正是解决这一问题的核心 。 近日，无问芯穹发起了一次推理系统开源节，连续开源了三个推理工作，包括加速端侧推理速度的 SpecEE、计算分离存储融合的 PD 半分离调度新机制 Semi-PD、低计算侵入同时通信正交的计算通信重叠新方法 FlashOverlap，为高效的推理系统设计提供多层次助力。下面让我们一起来对这三个工作展开 一一解读： Day 1｜SpecEE：基于推测的 Early Exiting 机制，让 AI PC 推理速度起飞 随着 DeepSeek 等开源模型表现出越来越强悍的性能，在 PC 端本地部署大模型的需求持续增长。尽管许多情况下使用云端 ...