大摩建模“AI推理工厂”：无论是英伟达还是华为芯片，都能盈利，平均利润率超50%

Core Insights - The profitability of AI inference is exceptionally high, with average profit margins exceeding 50% for standard "AI inference factories" regardless of the chip manufacturer used [1][4] - Nvidia's GB200 chip leads the market with a profit margin of nearly 78%, while Google's and Huawei's chips also show strong profitability [1][5] - AMD's AI platform, however, faces significant losses in inference scenarios, with profit margins of -28.2% and -64.0% for its MI300X and MI355X platforms respectively [1][7] Profitability Analysis - The report highlights a stark contrast in profitability among AI hardware giants, with Nvidia, Google, Amazon, and Huawei performing well [4] - Nvidia's flagship product, the GB200 NVL72, achieves a remarkable profit margin of 77.6%, attributed to its superior computational, memory, and network performance [5] - Google's TPU v6e pod follows closely with a profit margin of 74.9%, demonstrating the effectiveness of hardware-software synergy in building economically viable AI infrastructure [7] AMD's Financial Struggles - AMD's financial performance in inference scenarios is notably poor, with high costs and low output efficiency leading to significant losses [7] - The total cost of ownership (TCO) for an MI300X platform is approximately $774 million, comparable to Nvidia's GB200 platform at $806 million, yet AMD's revenue from token output is insufficient to cover these costs [7][9] 100MW AI Factory Model - Morgan Stanley's "100MW AI Factory Model" provides a standardized framework for evaluating different AI solutions, focusing on power consumption, total cost of ownership, and revenue generation [9] - The model estimates the annual TCO for a 100MW AI factory to range between $330 million and $807 million [9][11] - Revenue is directly linked to token output, with a fair price set at $0.20 per million tokens, considering a 70% utilization rate for devices [9] Future Competitive Landscape - The report indicates that the future AI landscape will focus on building technological ecosystems and next-generation product roadmaps [10] - A competition over "connection standards" is emerging among non-Nvidia players, with AMD advocating for UALink and Broadcom supporting a more open Ethernet approach [10] - Nvidia is solidifying its market position with its next-generation platform "Rubin," expected to enter mass production in Q2 2026, setting a high bar for competitors [10]