Good day, and thank you for standing by. Welcome to Kingsoft Cloud Third Quarter 2025 Earnings Conference Call. At this time, all participants are in a listen-only mode. After the speakers' presentation, there will be a question and answer session. To ask a question during the session, you will need to press star 11 on your telephone. You will then hear an automated message advising your hand is raised. To withdraw your question, please press Please be advised that today's conference is being recorded. I would now like to hand the conference over to your speaker today, Nicole Shan, IR Director of Kingsoft Cloud. Please go ahead.

Thank you, operator. Hello, everyone. And thank you for joining us today. Kingsoft Cloud third quarter 2025 earnings release was distributed earlier today and is available on our IR website at ir.ksyulin.com as well as on the PR Newswire services. On the call today from Kingsoft Cloud, we have our Vice Chairman, CEO, Mr. Zhou Tao, and the CFO, Ms. Li Yi. Mr. Zhou will review our business strategies, operations, and other company highlights followed by Ms. Li, who will discuss the financial performance. They will be available to answer your questions during the Q&A session that follows. There will be conductive integration. Our are for your convenience and the reference purpose only. In case of any discrepancy, management statement in original language will prevail. Before we begin, I'd like to remind you that this conference call contains forward-looking statements within the meaning of Section 21E of the Securities Exchange Act of 1934 as amended and as defined in The U.S. Private Securities Litigation Reform Act of 1995. These forward-looking statements are based upon management's current expectations and current market and operating conditions. And relate to events that involve known or unknown risks, uncertainties, and other factors. All of which are difficult to predict and many of which are beyond the company's control. Which may cause the company's actual results, performance, or achievements to differ materially from those in the forward-looking statements. Further information regarding these and other risks, uncertainties, or factors are included in the company's filings with the U.S. SEC. The company does not undertake any obligation to update any forward-looking statements. As a result of new information, future events, or otherwise. Except as required under applicable law. Finally, please note that unless otherwise stated, all financial figures mentioned during this conference call are denominated in RMB. It's now my pleasure to introduce our Vice Chairman and CEO, Mr. Zhou. Please go ahead, Zhou.

Hello, everyone. Thank you, and welcome to Kingsoft Cloud third quarter 2025 earnings call. I am Zhou Tao, CEO of Kingsoft Cloud. In the era that artificial intelligence is implemented across various industry verticals, and reshaping the technological landscape, Kingsoft Cloud firmly established its strategic positioning and defined its development orientation. On the premise of steadily meeting the demands of model training, we have made adequate technical and resource reserves for the explosive growth of inference. In the face of the dual trends of rapid model iteration and increasing adoption of artificial intelligence, we have provided our clients with stable and efficient integrated training and inference intelligent cloud computing services. And have laid out model API business to turn inference scenarios into new growth engines. The substantial high growth in revenue and the stable profit margin level validates the steady execution of our strategic measures achieving high quality and sustainable development. First, our revenue in the third quarter reached RMB 2,480,000,000.00, with year-over-year growth rate accelerating from 24% in the previous quarter to 34 to 31% this quarter. Both public cloud and enterprise cloud achieved year-over-year and sequential growth. Among which public cloud revenue increased significantly by 49% year-over-year, reaching RMB 1,750,000,000.00. Second, intelligent computing cloud business remains on a fast development track. This quarter, gross billings of intelligent computing reached RMB 782,000,000, with a year-over-year growth around 122%. It accounted for 45% of the public cloud revenue, realizing a significant increase from 31% in the same period last year. Generative artificial intelligence and cloud are symbiotically integrated in many aspects, including technology, products, and customer cross-sales. The demand for artificial intelligence not only drives the rapid development of intelligent cloud, but also leads to the growth and technological innovation of basic public cloud and accelerates the iterative process of…

And thank you all for joining the call today. Before we go through the details of financial results for the third quarter, I would like to highlight the following aspects. First, revenue has consistently achieved year-over-year growth for six quarters, reaching RMB 2,478 million this quarter. This represents an accelerated year-over-year growth rate of 31% up from 24% in the previous quarter. Revenue from public cloud service stood at RMB 1,752,300,000.0, a significant increase of 49% from RMB 1,165,500,000.0 in the same quarter last year. Meanwhile, robust demand from our intelligent cloud, which is also called AI cloud business, drove around 120% year-over-year billing growth, which totaled RMB 782,400,000.0. Second, profitability has seen substantial improvement. Our adjusted gross margin rose to 16% up from 15% in the previous quarter. And adjusted EBITDA margin improved to 33% compared with 17% last quarter. Notably, we turned quarterly adjusted operational and adjusted net loss into profit simultaneously for the first time. These gains validate our strong execution in pursuing high-quality, sustainable development as well as our ability to monetize opportunities in the intelligent cloud space. Third, I would like to express our gratitude to shareholders for their support during our risk to equity financing in September. We successfully raised HKD 2,800,000,000.0. And 8% of the fund will be allocated to further investment in AI infrastructure and transfer them to general operational needs. This funding will fully underpin the growth of our intelligent cloud business and enable us to create long-term value for all stakeholders. Now I will walk you through our financial results for 2025. And use RMB as currency. Total revenues were RMB 2,478 million. Of these, revenues from public cloud services were RMB 1,752,300,000.0, up 49% from RMB 1,175,500,000.0 in the same quarter last year. Revenues from enterprise cloud services reached…

Thank you. As a reminder, to ask a question, you will need to press 1 and one on your telephone and wait for your name to be announced. To withdraw your question, please press 11 again. Our first question comes from the line of Xiaodan Zhang from CICC. Please go ahead. Your line is open.

So thanks management for taking my questions. And, first of all, has there been any structural change in the demand of your ecosystem and external clients for the past quarter? And secondly, how does management see the margin trend in the coming quarters? And what's the expected mix of different computing resources acquisition models? Thank you.

So basically, the core of the reason behind the AI revenue growth in Q3 is that we have some clusters that, you know, partially delivered in the previous quarters, for example, like the 2025, and these clusters and these services have only been partially accounted for revenues from a full quarter basis. But now in Q3, they are starting to be recognized as full quarter revenues. And, also, there's the factor of partially delayed revenue as well. Some of the revenue which we had in Q2 but was not accounted for, and then this revenue is delayed into the third quarter. Yeah. So regarding the second part of your first question, which is about the structure of internal and external customers, I think I used to say that from a large trend general trend perspective, currently in the phase of transitioning from large and top customers' training demand to general and wider spread customers' inference demand. Most of at the current stage, we still see, you know, majority of our demand coming from the larger customers in their training demand. However, especially in the latest quarter, we are increasingly seeing the trend of our customers adopting artificial intelligence models into their diverse industries. So in face of this general trend, we have also, as we mentioned in the prepared remarks, we have launched our StaffLoad platform to meet the demands of such general trend. And this also goes back to the margin that you also asked about. We generally think that in the future, the inference demand will tend to exhibit a higher margin profile than the current stage of training. And therefore, we think that when that wave of demand comes, we expect to have higher margins.

Thank you, Xiaodan. I think because level as a proportion of the AI business continues to rise and its cost structure is mainly dominated by depreciation, we expect this EBITDA margin will still remain above 20%. But I have to mention that the significant quarter-on-quarter improvement in this quarter was mainly driven by a one-time other income, which will return to the normal level next quarter. Thank you, Xiaodan. Operator, next question, please.

Thank you. Our next question comes from the line of Wenting Yu from CLSA. Please go ahead. Your line is open.

The first question is, could management share the outlook and guidance on the revenue outlook for next year? And beyond the Internet companies' post-model training and in-body intelligence scenarios that are already underway this year, which other industry and application scenarios are expected to have strong computing power demand that could drive the revenue growth next year? And the second question is with multiple providers in both China and the US increasing the proportion of server leasing in their computing resource mix, how does management view the current market dynamics for procurement versus leasing? And from a cost-effectiveness and profit margin perspective, how would the company allocate the resources between these two approaches?

Wenting, thank you for your question. The company's budget process is currently underway and expected to be completed around the beginning of the next year. We will share the specific details with you once it is finalized. However, regarding the demand for our AI business, we are fully confident in the subsequent demand growth. And for your second question about the procurement method, we primarily align our capital channels with actual customer needs, including cluster scale, delivery time, and supply inventory level. There's no rigid total allocation target from the cost-effectiveness perspective. Both approaches have their own pros and cons. The leasing model is to find our supply chain channels and provide a certain degree of flexibility in resource allocation, with the flexibility also offered through short-term and long-term contracts. Self-procurement, on the other hand, gives us great autonomy in control delivery time rates and managing plus. It also reduces the profit sharing with suppliers, thereby, elevating our pressure on profit margin.

Yeah. You know, as you mentioned that the robotics companies in China are a growth environment partly. So, you know, as you this year, we have covered most of the robot companies in China, and we can see the revenue is increasing very rapidly. In the next year, we believe the increase of the robotic companies will also be fast. Meanwhile, you know, as more and more Internet companies in China using talking token services, which is the API services, we are seeing the increase of the business is very quickly. So we believe in the next year, this will be a very important factor to driving the revenue to increase. Thank you.

So this is the CEO. He added that yes, that is your question. Your second question is really about the choice between the leasing model and the CapEx model. So we've talked about that before. So, generally, there's a general rule of thumb. When you're looking at the larger customers, especially the customers that have solid profiles, have solid fundamentals, and are trustworthy. Premium customers, for example, like Xiaomi. We would tend to choose the CapEx model. While in other growth stage companies, medium and small-sized companies, we generally tend to adopt the leasing model. Which is also a way a meaningful way to reduce our own risk. So as we rightly mentioned, there's no kind of a top-down target for the split between these two different methods. And we also talked about in the last quarter as well that the impact of these two different methods have different impacts on our gross margins. However, we have seen the financial results for the past three quarters. Which we have adopted various combinations of these two different models. You know, especially when you compare the gross margin for the third quarter versus the second quarter, it actually also improved sequentially. So I would say that at the current stage, we do not expect material changes to the current status. But generally speaking, in the future, we do expect the margin to improve. Thanks, Anthony. Next question, please.

Thank you. Our next question comes from the line of Timothy Zhao from Goldman Sachs. Please go ahead. Your line is open.

Thank you, management, for taking my question. My question is regarding the differences between AI training versus inferences. Could management share what is the pricing methodology between these two kinds of demand and what has been the part pricing trend over the past few months or year to date? And, in terms of the utilization rate of the chips of GPUs, pricing, and profitability, can you share more color on the gap between training and inferences? Thank you.

Okay. Let me answer these questions. You know, we're not talking about the price strategy for inference and training. You know, there's not too much difference between two things. So the price is based on the qualities. How many resources to use, which is the most important factor. And also comparing, you know, the margin rate, you know, there are two kinds of inference services, which one is, you know, customer by resource and use our platform to influence. So that margin ratio is very similar to the training margin ratios, but another one is, you know, customers do directly by our API talking services. That we think that will have a better margin ratio. But, you know, this business is just in the beginning, so we have we need time to see what is the big difference between the two things. Thank you.

Sounds good. Thank you. Due to time constraints, this concludes our question and answer session. So I'll hand the call back to Nicole for closing remarks.

Thank you. Thank you all once again for joining us today. If you have any questions, feel free to contact us. Look forward to speaking with you again next quarter. Have a nice day. Bye-bye.