BABA(US:BABA) 是说芯语·2025-08-30 07:46Core Viewpoint - Alibaba is developing a new generation of AI chips focused on multifunctional inference scenarios, aiming to fill the market gap left by NVIDIA's H20 exit [1][3]. Chip Development and Specifications - The new chip utilizes domestic 14nm or more advanced processes, supported by local foundries like Yangtze Memory Technologies, integrating high-density computing units and large-capacity memory with an expected LPDDR5X bandwidth exceeding 1TB/s, targeting a single-card computing power of 300-400 TOPS (INT8), comparable to H20's approximately 300 TOPS [1][3]. - Compared to NVIDIA's H20, Alibaba's chip offers full-scene compatibility, supporting FP8/FP16 mixed precision computing and seamless integration with the CUDA ecosystem, reducing migration costs by over 70% [3]. - Alibaba has urgently increased its order for the Cambricon Siyuan 370 chip to 150,000 units, which is based on a 7nm process and utilizes Chiplet technology, integrating 39 billion transistors and achieving a measured computing power of 300 TOPS (INT8) with a 40% improvement in energy efficiency [5]. Market Strategy and Production Capacity - The Cambricon Siyuan 370 chip is expected to cover 60% of Alibaba Cloud's inference demand by Q2 2025 and supports multi-card interconnection via PCIe 5.0, facilitating user growth for Tongyi Qianwen [5]. - Alibaba collaborates with Yangtze Memory Technologies to develop AI chips focusing on overcoming storage bottlenecks, achieving a storage density of 20GB/mm² and read/write speeds of 7000MB/s, a 40% improvement over the previous generation, expanding local storage capacity to 128GB [5][6]. - To ensure mass production, Alibaba employs a dual-foundry backup strategy, with SMIC's 14nm production line handling basic chip production, achieving a stable yield of over 95% and a monthly capacity of 50,000 units [6]. Future Roadmap - Alibaba's three-step strategy includes: - Short-term (2025-2026): Focus on 7nm/14nm inference chips to quickly capture market share through ecosystem compatibility [10]. - Mid-term (2027-2028): Launch 4nm training chips targeting a computing power of 1 EFLOPS, competing with NVIDIA's H100 [10]. - Long-term (post-2030): Explore disruptive technologies like photonic computing and integrated storage-computing solutions, with the first commercial photonic AI chip already released, promising a speed increase of 1000 times and a 90% reduction in power consumption compared to GPUs [10]. - Alibaba's path to domestic computing power is characterized as a dual battle of technological breakthroughs and ecosystem reconstruction, aiming to disrupt NVIDIA's monopoly through a "compatibility-replacement-surpassing" strategy [10][11].