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❓ What do Google’s external sales of TPUs mean for Google and the semiconductor industry? 📢 Reports indicate that Google may begin selling Tensor Processing Units (TPUs) to external data centers. Currently, NVIDIA sells approximately 8 million chips annually; can TPUs achieve sales of 500,000 to 1 million units per year? The answer may be affirmative. Each sale of 500,000 TPUs is expected to increase Google’s cloud business revenue by about 10% in 2027, boost earnings per share (EPS) by 3%, and drive up valuation multiples. This report also elaborates on the key impacts of this event on the semiconductor industry.What has happened?🗣️ Reports show that Google is in talks with metaverse platform companies (META) and other cloud service customers to allow them to use Google’s TPUs in their own data centers. Notably, there are reports that META plans to invest billions of dollars in 2027 to deploy TPUs in its data centers, while also renting TPUs from Google Cloud next year. It is particularly important to note that the discussions between META and Google focus on using TPUs for model training, rather than supporting its existing inference workloads.🔧 The background of this development is that Google has developed software called “TPU Command Center” designed to lower the barrier for customers to use TPUs—customers can interact with TPU server clusters through the PyTorch framework and the command center software. Additionally, Anthropic has previously reached an agreement with Google Cloud to gain access to approximately 1 million TPUs, laying the groundwork for this collaboration.💰 Each sale of 500,000 proprietary TPUs could increase EPS by 3% in 2027.🤝 The recent agreement between Anthropic and Google for the use of 1 million TPUs, along with current discussions with META regarding procurement, marks a significant breakthrough in Google’s strategy to deploy and sell TPUs externally after about 12 years of innovation in the TPU field. It is estimated that Broadcom produced approximately 1.8 million TPUs for Google in 2025, most of which were used internally by Google; Morgan Stanley’s semiconductor team predicts that TPU production will continue to expand, potentially reaching 3 million units by 2027. 📈 Reports suggest that positive feedback from external markets may further increase Google’s TPU procurement volume in 2027, a metric worth monitoring. Sensitivity analysis shows that the scale of Google’s external sales of TPUs through a proprietary model (1P) will have a significant impact on performance: each sale of approximately 500,000 chips is expected to bring $13 billion (about 11%) growth to Google’s cloud business revenue in 2027 (higher than market expectations) and increase EPS by 3% (about $0.37).✨ The accelerated growth of Google’s cloud business and its expansion in the TPU market may also drive up its valuation multiples (similar trends have been observed in recent months).Analysts from Morgan Stanley and NVIDIA predict that NVIDIA will ship approximately 8 million graphics processing units (GPUs) in 2027. Considering two factors—first, production capacity, and second, Google’s TPUs continuously overcoming historical pain points of developer familiarity and limited programming flexibility— achieving annual sales of 500,000 to 1 million TPUs is reasonable.❓ Three Unknown Factors to Watch in Google’s TPU External Strategy📌 TPU Business Model: Google has expressed a willingness to flexibly meet customer needs by providing various TPU acquisition methods (including Google Cloud rentals, licensing, chip sales, equipment rentals, etc.). This will affect the revenue recognition methods of related transactions, potentially impacting Google’s profit margins and capital expenditures. Although the above calculations assume that Google sells chips directly to META for use in its data centers, the specific pricing model, contract terms, and additional services remain unclear.💰 TPU Pricing Strategy: There is a significant difference in gross margins across the chip industry, with NVIDIA around 75% and AMD around 50%, which has a crucial impact on the pricing of Google’s TPUs. In sensitivity analysis, we assume that Google’s TPU gross margin is at the lower end of industry peers, as Google needs to first validate product competitiveness and gain initial customer acceptance. Furthermore, the calculations are based on single-chip pricing, but if Google sells rack-level products to META, the pricing logic may change.🛠️ TPU Application Scenarios: Although META is considering using TPUs for model training, the training process can be broken down into multiple stages, and it is currently unclear how much META relies on TPUs for training its Llama model. If TPUs become a core component of the Llama model training infrastructure, it will be significant—this means that two of the five leading large language models (LLMs) will be trained based on TPUs.🤔 What does this mean for META?—Achieving more cost-effective computing solutions.💸 While META invests in building cutting-edge large language models (LLMs) and optimizing its core business, it has been striving to control the growth of capital expenditures (capex) and operating expenses (opex). The high cost-performance ratio of TPUs (outstanding performance per dollar) is a key driver for META’s consideration of adopting this chip. Given the excellent cost-effectiveness of TPUs, deploying them in 2027 is expected to help META offset some capital expenditure pressures.The ultimate degree of impact will depend on two aspects:First, the scale of META’s procurement (the internet industry team currently predicts that META will procure over 1 million NVIDIA chips in 2026),Second, the efficiency advantages of TPUs compared to other chips.🔧 Impact on the Semiconductor Industry ✅ The expansion of Google’s TPU applications will benefit Broadcom (AVGO), but whether it can be widely adopted by other customers remains uncertain. Currently, Broadcom has disclosed five dedicated processor (XPU) customers, which we speculate to be Google, META, ByteDance, Anthropic, and OpenAI. If META confirms the adoption of TPUs, it will mean that three out of five customers (potentially four, including OpenAI) are using this product.🏆 From the perspective of industry competition, TPUs are becoming another commercial competitor to NVIDIA in the dedicated processor (XPU) market, but for Broadcom, this does not equate to its dedicated integrated circuit (ASIC) strategy gaining industry recognition.⚠️ If META’s application scale for TPUs exceeds previous expectations, it may delay the rollout of its self-developed dedicated integrated circuit (MTIA) planned for the second half of 2026, thereby reducing the potential growth space for EPS. This could be a positive signal for NVIDIA: given that ASIC development must cope with NVIDIA’s annual product iteration pressure, customers may prefer to choose the more mature TPU ecosystem—especially since product development involves not only the chip itself but also rack-level connectivity solutions, where NVIDIA leads in commercialization.📌 In the short term, although news of META adopting TPUs may raise market concerns about NVIDIA, the overall demand in the artificial intelligence (AI) industry continues to rise, which will not significantly change our view on NVIDIA. Broadcom is expected to achieve growth advantages next year (noting that some growth comes from lower-margin rack business), but current valuation multiples are already at a high level. We are optimistic about both companies, as the TPU supply chain is clearly accelerating, while NVIDIA’s supply chain also maintains growth momentum.🤝 In the large language model (LLM) field, NVIDIA has gained more market share from Google this year—Google’s spending on NVIDIA-related products is about $20 billion this year, while spending on TPUs is about several billion dollars. This ratio may adjust next year, but we expect the market will not present a “winner-takes-all” scenario. Currently, even if the Gemini model surpasses GPT-5 to become the industry-leading model, competitors will not slow down their investments—technology industry leaders have repeatedly emphasized the importance of this competition, and the laws of industry scaling remain effective, ensuring that market competition will remain fierce. However, if competitors’ market shares are impacted, their subsequent financing and ongoing investment capabilities will also be affected.🤔 What about AMD’s situation?📌 We believe AMD’s overall situation remains largely unchanged. In the short term, one of its core customers, META, seeking alternative solutions may affect market confidence in the upcoming launch of AMD’s MI450 chip. However, META’s strategic goal has not changed: to diversify its computing infrastructure by investing in self-developed ASICs, collaborating with Broadcom to develop external ASICs, and procuring GPUs from NVIDIA and AMD, now adding TPU options.💹 In the long term, return on investment (ROI) will be a key decision factor; AMD needs to demonstrate the ROI advantages of its products to gain a significant share in this market. Previously, a notable advantage of AMD over ASICs was adaptability to all cloud platforms, but as TPUs gradually become a commercial alternative, this advantage has diminished. We have high expectations for the performance of AMD’s MI450 chip, but without clear ROI validation data, market confidence is unlikely to significantly improve.———————-
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