Core Insights - The North American large CSPs are the main drivers of AI Server market demand expansion, with a forecasted 24.3% year-on-year growth in global AI Server shipments for this year, slightly revised down due to international circumstances [1][4] Group 1: North American CSPs - Microsoft is focusing on AI investments, which has somewhat suppressed the procurement of general-purpose servers, primarily utilizing NVIDIA's GPU AI solutions for AI Server deployment [1] - Meta has significantly increased its demand for general-purpose servers due to new data center openings, primarily using AMD platforms, and is actively expanding its AI Server infrastructure with self-developed ASICs expected to double in shipments by 2026 [1] - Google has benefited from sovereign cloud projects and new data centers in Southeast Asia, significantly boosting server demand, and has begun mainstream production of its TPU v6e for AI inference [2] - AWS is focusing on its self-developed Trainium v2 platform, with plans for Trainium v3 development expected to launch in 2026, anticipating a doubling of its self-developed ASIC shipments by 2025 [2] - Oracle is emphasizing the procurement of AI Servers and In-Memory Database Servers, actively integrating its core cloud database and AI applications [3] Group 2: Market Outlook - Due to international circumstances, many Server Enterprise OEMs are reassessing their market plans for the second half of 2025, with an overall forecast of approximately 5% year-on-year growth in total server shipments, including both general-purpose and AI Servers [4]

Core Insights - The article discusses the accelerating trend of AI server demand driving major North American Cloud Service Providers (CSPs) to develop their own Application-Specific Integrated Circuits (ASICs) to reduce reliance on external suppliers like NVIDIA and AMD [1][2][3][4][5]. Group 1: North American CSP Developments - Google has launched the TPU v6 Trillium, focusing on energy efficiency and optimization for large AI models, with plans to significantly replace the TPU v5 by 2025 [2]. - AWS is collaborating with Marvell on the Trainium v2, which supports generative AI and large language model training, and is expected to see substantial growth in ASIC shipments by 2025 [2]. - Meta is developing the next-generation MTIA v2 in partnership with Broadcom, emphasizing energy efficiency and low-latency architecture for AI inference workloads [3]. - Microsoft is accelerating its ASIC development with the Maia series chips, optimizing for Azure cloud services, and is collaborating with Marvell for the Maia v2 design [3]. Group 2: Chinese AI Supply Chain Autonomy - Huawei is actively developing the Ascend chip series for domestic markets, targeting applications in LLM training and smart city infrastructure, which may challenge NVIDIA's market position in China [4]. - Cambricon's MLU AI chip series is aimed at cloud service providers for AI training and inference, with plans to advance its solutions to the cloud AI market by 2025 [4]. - Chinese CSPs like Alibaba, Baidu, and Tencent are rapidly developing their own AI ASICs, with Alibaba's T-head launching the Hanguang 800 AI inference chip and Baidu working on the Kunlun III chip [5].