成本暴降70%！谷歌TPU强势追赶，性价比已追平英伟达

Core Insights - The focus in the AI chip market is shifting from performance to cost efficiency, as commercial pressures mount and the cost of inference becomes a critical factor in determining competitive advantage [1][2][3] Group 1: Shift in Evaluation Criteria - The evaluation criteria for AI chips are transitioning from "who computes faster" to "who computes cheaper and more sustainably" as inference becomes a significant source of long-term cash flow [2][3] - High costs associated with inference are becoming more pronounced as deployment and commercialization of large models progress, leading to a reevaluation of chip performance metrics [3] Group 2: TPU's Cost Reduction - Google/Broadcom's TPU has significantly reduced its inference cost, with the transition from TPU v6 to TPU v7 resulting in a 70% decrease in unit token inference cost, making it competitive with NVIDIA's GB200 NVL72 [1][4] - The cost reduction in TPU v7 is attributed to system-level optimizations rather than a single technological breakthrough, indicating that future cost reductions will depend on advancements in adjacent technologies [4] Group 3: Competitive Landscape - Despite TPU's advancements, NVIDIA maintains a time-to-market advantage with ongoing product iterations, which are crucial for customer retention [5][6] - The investment outlook remains positive for both NVIDIA and Broadcom, with Broadcom's earnings forecast for FY2026 raised to $10.87 per share, reflecting its strong position in AI networking and custom computing [7] Group 4: Industry Dynamics - The report suggests a clearer division of labor within the industry, where GPUs continue to dominate training and general computing markets, while custom ASICs penetrate predictable inference workloads [7][8] - The significant drop in TPU costs serves as a critical stress test for the viability of AI business models, highlighting the importance of economic considerations in the ongoing GPU vs. ASIC competition [8]