3 6 Ke·2026-01-22 02:42Core Insights - Goldman Sachs report highlights a significant 70% reduction in inference costs with the new TPU v7 chips from Google and Broadcom, indicating a major shift in the AI computing landscape [1][2][10]. Group 1: Cost Reduction and Implications - The 70% cost reduction signifies a fundamental change in the industry, moving beyond traditional hardware upgrades [2][5]. - The report emphasizes the importance of inference costs over training speeds, as the industry transitions from model training to deployment [4][10]. - The cost savings are attributed to three main factors: improved data transmission efficiency, tighter chip packaging, and specialized architecture of ASICs [7][8]. Group 2: Competitive Landscape - The TPU v7's cost is now comparable to NVIDIA's offerings, altering the competitive dynamics as companies reconsider their chip choices [9][10]. - The report suggests that the rise of ASICs represents a challenge to NVIDIA's dominance in the GPU market, indicating a shift towards customized solutions [11]. Group 3: Major Contracts and Market Movements - Anthropic's $21 billion order for custom ASICs marks a significant investment in dedicated AI infrastructure, reflecting a strategic shift in the industry [12][13]. - The funding for this order is backed by major players like Google and Amazon, highlighting the financial support for custom chip development [14][15]. Group 4: Role of Broadcom - Broadcom has transitioned to a key player in the AI chip market, acting as a contractor for major tech firms and providing essential interconnect technology [22][25]. - The company's business model, which includes upfront R&D fees and revenue sharing from chip sales, offers a more stable income compared to NVIDIA's model [24][27]. Group 5: Implications for China - The rise of ASICs and the reduction in inference costs may accelerate the development of China's own custom chip solutions, as companies seek alternatives to NVIDIA's GPUs [28][29]. - Chinese firms are increasingly investing in self-developed chips, aiming to create tailored solutions for their AI models [29][30]. - The report suggests that the focus should be on companies with core competencies in chip design and packaging technologies, rather than merely competing in low-cost chip production [31][34].