公众号记得加星标⭐️，第一时间看推送不会错过。 今天，微软宣布推出Azure Cobalt 200，这是微软专为云原生工作负载设计的下一代基于 Arm 的 CPU。 Cobalt 200 是微软持续优化云堆栈每一层（从芯片到软件）战略的一个重要里程碑。据微软所说， 该CPU的设计目标是：完全兼容使用现有 Azure Cobalt CPU 的工作负载；相比 Cobalt 100，性能 提升高达 50%；并与最新的 Microsoft 安全、网络和存储技术集成。 微 软 强 调 ， 与 前 代 产 品 一 样 ， Cobalt 200 针 对 常 见 的 客 户 工 作 负 载 进 行 了 优 化 ， 并 为 公 司 的 Microsoft 云产品提供了独特的功能。微软的首批生产级 Cobalt 200 服务器现已在微软的数据中心 上线，更广泛的部署和客户可用性将于 2026 年实现。 基于 Cobalt 100：领先的性价比 据结束，微软的 Azure Cobalt 之旅始于 Cobalt 100，这是他们首款专为云原生工作负载定制的处理 器。Cobalt 100 虚拟机自 2024 年 10 月起正式发布 ( ...

Core Insights - The discussion around ASIC growth has intensified following comments from NVIDIA CEO Jensen Huang, who stated that 90% of global ASIC projects are likely to fail, emphasizing the high entry barriers and operational difficulties associated with ASICs [2][3] - Despite Huang's caution, the market is witnessing a surge in ASIC development, with major players like Google and AWS pushing the AI computing market towards a new threshold [5][6] - The current market share shows NVIDIA GPUs dominate the AI server market with over 80%, while ASICs hold only 8%-11%. However, projections indicate that by 2025, the shipment volumes of Google’s TPU and AWS’s Trainium will significantly increase, potentially surpassing NVIDIA’s GPU shipments by 2026 [6][7] ASIC Market Dynamics - The ASIC market is expected to see explosive growth, particularly in AI inference applications, with a projected market size increase from $15.8 billion in 2023 to $90.6 billion by 2030, reflecting a compound annual growth rate of 22.6% [18] - ASICs are particularly advantageous in inference tasks due to their energy efficiency and cost-effectiveness, with Google’s TPU v5e achieving three times the energy efficiency of NVIDIA’s H100 and AWS’s Trainium 2 offering 30%-40% better cost performance in inference tasks [17][18] - The competition between ASICs and GPUs is characterized by a trade-off between efficiency and flexibility, with ASICs excelling in specific applications while GPUs maintain a broader utility [21] Major Players and Developments - Major companies like Google, Amazon, Microsoft, and Meta are heavily investing in ASIC technology, with Google’s TPU, Amazon’s Trainium, and Microsoft’s Azure Maia 100 being notable examples of custom ASICs designed for AI workloads [22][24][25] - Meta is set to launch its MTIA V3 chip in 2026, expanding its ASIC applications beyond advertising and social networking to include model training and inference [23] - Broadcom leads the ASIC market with a 55%-60% share, focusing on customized ASIC solutions for data centers and cloud computing, while Marvell is also seeing significant growth in its ASIC business, particularly through partnerships with Amazon and Google [28][29] Future Outlook - The ASIC market is anticipated to reach a tipping point around 2026, as the stability of AI model architectures will allow ASICs to fully leverage their cost and efficiency advantages [20] - The ongoing evolution of AI models and the rapid pace of technological advancement will continue to shape the competitive landscape between ASICs and GPUs, with both types of chips likely coexisting and complementing each other in various applications [21]

以下文章来源于明亮公司 ，作者主编24小时在线 明亮公司 . 追踪新商业、好公司，提供一手情报与领先认知。 作者：苏打 出品：明亮公司 ！ 近日，有消息称拼多多已组建电商推荐大模型团队，负责人为原百度凤巢的核心成员。尽管拼多多并未正面回应，但这一消息一度引发广 泛关注。 作为几乎唯一一个"缺席"AI大模型布局的万亿规模体量"大厂"，市场对拼多多AI战略规划的关注或许并非大模型乃至AI本身，而是起家于C 端的巨头公司们，对未来不同发展路径的判断模型。 我们的一个观察是，阿里实际上与美国几家大厂的模式更为接近——未来承诺更大规模的资本支出；而拼多多作为其中看似"异类"的代表， 仍专注于C端用户体验、供应链效率和出海。 值得一提的是，它们均拥有大量C端用户， 但有些选择最终将自己凝聚成具备"核心技术"的to B服务商 ，而有些选择持续深耕消费端，并于 其中攫取最强心智和竞争力。 而近期的资本市场表现，也一定程度上反映出其对两种不同方向的预期。截至发稿，阿里巴巴TTM市盈率约19.9倍；拼多多约11.6倍——市 场暂时写好了答案。 拼多多的克制：是「应用」还是做模型 大模型浪潮兴起后，阿里、百度、字节等是最先摆明态度 ...