Core Viewpoint - The AI training market is expected to grow rapidly, driven by the increasing adoption of AI GPUs from companies like NVIDIA and AMD, as well as the expansion of the ASIC market propelled by major American CSPs [2][3]. Group 1: ASIC Market Growth - The ASIC market is projected to experience a compound annual growth rate (CAGR) of up to 70% from 2024 to 2026, with an expected shipment of over 5 million ASIC units this year, reflecting a year-on-year growth of over 20% [2]. - The shipment ratio of AI GPUs to ASICs is expected to shift from 62:38 this year to 60:40 by 2026, indicating a growing importance of ASICs in the AI server category [2]. Group 2: Major CSP Developments - AWS plans to launch the Teton 2 cabinet using Trainium 2/2.5 chips in the second half of the year, which is expected to boost its ASIC chip shipments by over 40% [3]. - Meta is set to begin mass production of its Minerva cabinet using its MTIA chips, benefiting its main assembly partners, including Celestica and Quanta [3]. Group 3: ASIC vs. GPU - ASIC chips are custom-designed for specific applications, while NVIDIA's GPUs are general-purpose processors suitable for a wider range of functions [4]. - The sales of ASIC chips, led by Broadcom, are expected to reach between $60 billion and $90 billion by 2027, highlighting their growing significance in the AI chip market [4]. Group 4: Market Dynamics - Experts believe that the AI chip market is not a zero-sum game; both ASICs and GPUs can coexist and share the growth of the AI industry [5][6]. - Morgan Stanley forecasts that the ASIC chip market for AI will grow from $12 billion in 2024 to $30 billion by 2027, with a CAGR of 34% [6]. Group 5: Cost Efficiency - Amazon's Trainium chips reportedly reduce inference task costs by 30% to 40% compared to NVIDIA's H100 GPUs, while Google's latest TPU v6 shows a 67% improvement in energy efficiency over its predecessor [7]. Group 6: NVIDIA's Perspective - NVIDIA's CEO Jensen Huang acknowledges the value of ASICs but emphasizes their limitations in flexibility, arguing that GPUs are better suited for the rapidly changing AI landscape [8][9]. - Huang believes that NVIDIA's strategy focuses on leveraging the versatility of GPUs and maintaining a comprehensive software ecosystem, which is crucial for its competitive edge [10].