Good morning, ladies and gentlemen, and thank you for standing by for Kingsoft Cloud's second quarter 2026 earnings conference call. All participants are currently in listen-only mode. Following management's prepared remarks, we will open the call for questions. Please note that today's call is being recorded. I will now turn the call over to Mr. Jackie Zou, Senior Director of Capital Markets at Kingsoft Cloud. Jackie, please go ahead.

Thank you, operator. Hello, everyone, and thank you for joining us today. Kingsoft Cloud's second quarter 2026 earnings release was issued earlier today and is available on our IR website and through PR Newswire. Joining us today are Mr. Zou Tao, Chairman and CEO, Ms. Li Yi, CFO, Mr. Liu Tao, Senior Vice President, Mr. Tian Kaiyan, Senior Vice President, Ms. Yu Jun, Vice President, Mr. Zhou Ruilong, Associate Vice President, and Mr. Clark Tian, Board Secretary and Associate Vice President. Mr. So will discuss our business performance and key developments, followed by Ms. Li with a review of our financial results. Management will then take your questions. Contemptive interpretation will be provided for convenience and for reference only. In the event of any discrepancy, mention statements in the original language will prevail. Before we begin, I would like to remind you that today's call contains forward-looking statements made under the safe harbor provisions of U.S. Private Securities' litigation reform text of . These statements involve risks and uncertainties and actual results may differ materially from those expressed or implied by the forward-looking statements. Additional information concerning factors that would cause actual results to differ materially is included in the company's filing with the U.S. SBC. The company undertakes no obligation to update any forward-looking statements except as required by applicable law. Unless otherwise stated, All financial figures discussed on today's call are denominated in Renminbi. With that, it is my pleasure to turn the call over to our Chairman and CEO, Mr. Zou. Mr. Zou, please go ahead.

Hello, everyone. Welcome to the second round of the Business Development Conference of J&J in 2026. I am Zou Tao, the CEO of J&J. In this quarter, we have seen AI cloud business model close to our eyes. Kaiyan's large-scale ecosystem of space prosperity brings a huge space of development to Chinese cloud manufacturers. Over the past few years, we have always believed in AI landing on the top of the sky and coming to us in the form of model-based service, AI-based service, and FDE integration. Facing this profound change in the industry, Jinan Cloud continues to monitor the development strategy of technical industry and high-capacity sustainable development, to promote the development of AI cloud services, MaaS business, and FDE business. They have achieved remarkable results. First of all, AI business continues to drive the company's revenue growth. This quarter, the total revenue reached 30.7 billion yuan. The single quarter's historical growth increased by 31 percent. Among them, AI Cloud Holdings' revenue reached 13.3 billion yuan, which increased by 82 percent. and Sun Yat-sen have increased their revenue by 56%. The revenue of MaaS business is growing rapidly. The revenue of MaaS in the second quarter has increased by 12%. Next, the level of profit has significantly increased. This quarter's net profit has increased by 15.4%. The net profit has increased by 2.4%. The first turnover of business profits has adjusted its operating profit to 4% and created a new high. This is a remarkable achievement in terms of capturing AI waves, increasing revenue, and implementing powder reduction effects. Third, the customer structure continues to optimize, and the business opportunities in and out of the ecosystem are accelerating. In the ecosystem, the income from Xiaomi and Jinshan Ecosystems is 8.1 billion yuan, which is 28% of the total increase. with a total revenue of 226 million yuan. The revenue of the top five clients outside the ecosystem has increased by 51% in comparison. AI cloud business has covered the Internet, AI lab, smart technology, autonomous driving, AI for Science, financial technology, games, video games, and other wide-ranging industries. The diversified client structure and business layout not only bring about a continuous increase in revenue,

Hello everyone and welcome to Kingsoft Cloud second quarter 2026 earnings call. I am Zou Tao, CEO of Kingsoft Cloud. This quarter, we saw further evolution in the AI cloud market. The rapid growth of the open source model ecosystem is creating significant opportunities for neutral cloud providers. At the same time, our long-held vision of bringing AI to every industry is becoming a reality through a combination of model as a service, agent as a service, and FDE services. Against this backdrop, King of Cloud remains committed to technology leadership and high-quality sustainable growth. We are accelerating the development of our AI Cloud math and FDE businesses with encouraging progress. First, AI continues to drive strong revenue growth. Total revenue reached a record of RMB 3.07 billion, up 31% year over year. AI Cloud Growth Billings increased 82% to RMB 1.33 billion and accounted for 56% of public cloud revenue. Mass revenue also grows strongly with Q2 revenue up more than 12 times from the Q1 level. Second, profitability improved significantly. Adjusted gross margin rose to 15.4% up 2.4 percentage points quarter over quarter. Operating profits turned positive for the first time with adjusted operating margin reaching a record high of 4.0%. This reflects our continued efforts to capture AI opportunities, improve revenue quality, and drive greater operating efficiency. Third, our customer mix continued to improve, with stronger momentum both within and outside our ecosystem. Revenue from the Xiangyan Kingsoft ecosystem reached RMB $810 million, up 28% year-over-year, and accounting for 26% of total revenue. Revenue from our top five non-ecosystem customers grew 51%. Our AI cloud business now serves a broad range of sectors, including internet services, frontier AI labs, embodied AI, autonomous driving, AI for science, fintech, gaming, and online video, to name a few. This diversified customer base supports continued growth while allowing us to allocate computing resources more flexibly and strengthen our pricing power and business resilience.

Jin Shan.psai Business Promotion As the only strategic cloud platform for Xiaomi Jin Shan Ecology The cloud service business growth brought by the AI era is significant In June 2016, the股东大会 officially approved our re-improvement of the upper limit amount from Xiaomi related income In 2026 and 2027, the upper limit sum from Xiaomi related income was 100 billion yuan have increased by 39% compared to the previous year. In the first half of this year, from Xiaomi and Jinshan, Gong Youyun received 25% of the same rate. In addition, the mass service capability of the new wave platform has been further strengthened. The new wave model API service continues to improve the multi-model service and corporate entry capability. Currently, the new wave platform has deployed 120 models online. Mainstream new models are launched at the same time to expose more than 230 customers. Third, we are working with head customers on the new channels. Face-to-face head smart customers and head self-driving customers to complete large-scale algorithmic group delivery, stable support, new model efficient delivery, and deep cooperation with AI4Size industry head customers to ensure that their new business is quickly deployed.

Now, let me walk you through our business progress in the second quarter of 2026. In public cloud, revenue reached RMB 2.36 billion, up 45% year over year. First, Xiaomi continues to expand AI across its human car home ecosystem, while WPS AI continues to advance. As the only strategic cloud platform for the Xiaomi and Kingsoft ecosystem, we see substantial AI-driven growth opportunities. In June, our shareholders approved a further increase in the annual caps to connect the transactions with Xiaomi. The combined caps for 2026 and 2027 now total RMB 10 billion, 39% higher than before the adjustment. In the first half, public cloud revenue from Xiaomi and Teamsoft grew 54% year over year. Second, we further strengthened the mass capabilities of our Starflow platform. Starflow now supports 120 models with major new models launched on the platform in sync with their market release and serves more than 230 enterprise customers. Third, we deepened cooperation with leading customers in emerging sectors. We delivered large-scale computing clusters to leading embodied AI and autonomous driving customers, supporting rapid model iteration and expanded our cooperation with the leading AI for science customers to support the growth of this new business.

In terms of industry, this quarter we have achieved a revenue of 7.1 billion yuan. In the field of public service, we have signed a contract with Changjiang Nanjing Telecommunications Management Bureau to build Jiang Haiyun, which is a dedicated digital base suitable for the needs of Changjiang Airlines. We also have a contract with Wuhan Digital Bureau Wuhan Yun, Dacheng Strategic Cooperation In the establishment of the digital government, the smart and smart application industry ecosystem development is expected to start cooperation. At the same time, the digital government in Wuhan is competing with the industrial ecosystem. In the field of digital health, we lead the country's key research and development program, Biomaterials and Information Integration Specialization, to build a cloud-based integrated medical virtual surgery platform. The results have also been implemented in more than 30 hospitals across the country, establishing a cloud-based In enterprise cloud, revenue reached RMB 710 million. In public services, we signed an agreement with the Nanjing Communications Administration of the Yangtze River

to build Jianghai Cloud, a dedicated digital infrastructure platform for Yangtze River shipping. We also formed a strategic partnership with the Wuhan Municipal Data Bureau and Wuhan Cloud across computing resource interconnection, digital governance, intelligent computing applications, and ecosystem development. In digital health, we are leading a project under the national key R&D program on biology and information integration to develop a cloud-based virtual server platform, which has been deployed in more than 30 hospitals nationwide. In enterprise services, we developed our cooperation with Yun Shang Gansu to jointly build and operate the Gansu Provincial Public Services Cloud under an integrated investment, construction, and operations model.

In terms of product technology, we closely cover the needs of self-calibration landing and AI application, and upgrade the full-time AI capability. This quarter, the new Mars platform is surrounded by high-frequency reasoning needs to continue the optimization of model deployment. The ability to absorb multiple core models is significantly improved, and supports the fine-tuning management of capacity and model dimension according to the whole line. We officially launched the new AgentKit brand, providing a comprehensive set of tools that cover safety, knowledge and memory, as well as evaluation and governance, to help companies quickly build production-level AI agent applications. At the same time, we will AIize cloud products, transform database storage and other components into platform-based products that can be used as agents and interfaces. We have optimized the cloud training platform, improved the flexibility of resource allocation, surrounded by training and regulation scenarios, added physical training, resource borrowing and recovery, and the flexible configuration capability of resource allocation, greatly improved the resource utilization rate under multi-task conditions, and reduced the training and environment threshold and operating cost of the development team. Facing the need for private and nationalized computing,

In products and technology, we continued to upgrade our full stack AI capabilities for intelligent computing and AI application deployment. This quarter, we further optimized the model deployment on Starflow Max for high concurrency inference. significantly improving throughput for several core models and enabling more granular access usage and model level management. We also launched Agent Kit, providing secure sandbox knowledge and memory management and evaluation and governance tools to help enterprises build production grade AI agents. At the same time, we are making general purpose cloud products such as database and storage We enhanced the Starflow training and inference platform with more flexible resource scheduling, sharing, and allocation for training and fine-tuning workloads, improving utilization and reducing development and operating costs. For private deployment of domestic AI infrastructure, our Galaxy Stack platform completed deep integration and full lifecycle visual management for multiple mainstream domestic AI chips.

Looking ahead,

We will continue to capture opportunities both within and outside our ecosystem, improve the operating efficiency of our computing assets, and strengthen our profitability and cash generation capabilities amid AI industry tailwinds. We remain committed to creating long-term sustainable value for customers, shareholders, and society. With that, I will hand the call over to our CFO, Li Yi, who will review our second quarter financial results. Thank you.

Thank you, Mr. Zhou and Mr. Tian. and thank you all for joining the call today. I will now discuss the second quarter financial results used as BN currency. Before we go through the details of the financial results for the second quarter, I would like to highlight the following aspects. First, our quarter revenue reached over $3 billion for the first time in our company's history. Up year over year for the last consecutive quarter, In particular, our AI cloud worth billing increased 82% year-over-year to $1.33 billion, accounting for over 43% of our total revenue errors, 31% a year ago. This reflects a continued structural shift in our business needs towards AI. Our probability has improved. Our adjusted worth margin was 15.4%, up 2.4 percentage point per quarter and 0.5 percentage points year-on-year. Our adjusted EBITDA margin reached 36% up from 70% in the same quarter last year and 82% last quarter. Notably, we returned to a great even at operating income level this quarter and recorded an adjusted operating profit margin of 4% These outcomes validate our ability to turn strong AI business demand into healthy profit growth. Third, we continue to invest to accelerate the build-out of our AI computing capacity. Capital expenditures together with right-of-use assets obtained through third-party financing and finance leases reached $3.3 billion this quarter. Further, $2.9 billion in last quarter and $2.8 billion in the same quarter last year. Now, let me walk you through our financial results for the second quarter of 2026. This quarter, total revenue was 3,072 million, up 31% year-over-year, or 40% quarter-over-quarter. Of these, revenues from public cloud services were 2,358 billion, up 45% from 1,625 million in the same quarter last year. Renews from enterprise cloud services reached $740 million, compared with $724 million in the same quarter last year, down slightly by 1% year-over-year. Total cost of renews was $3,606 million, representing a 30% year-over-year-only increase, mainly due to a continued investment in AI cloud infrastructure. IDC costs increased by 23% year-over-year, from $803 million to $990 million this quarter. This increase was mainly due to the increase of RAC services. Depreciation and amortization costs increased by 75% year-over-year, from $552 million in the same quarter of 2025 to $964 million this quarter, largely due to the depreciation of newly acquired and leased air infrastructure. including servers and network equipment. Solution development and service costs increased by 4% year-over-year from $564 million in the same quarter of 2025 to $586 billion this quarter. The modest increase was mainly due to higher costs incurred in AI transformation in solution development and delivery. 4-minute costs and other costs were approximately $66 million in total this quarter versus $92 million in the same quarter last year. Our adjusted gross profit for the quarter was $472 million, increased by 35% year-over-year and 34% quarter-over-quarter. Adjusted gross margin was 15.4%. up from 14.9% in the same quarter last year and from 13% last quarter. The increase was driven by higher growth margin in public cloud business thanks to strong AI demand per week. On the expense side, excluding short-based compensation costs expenses, our total adjusted operating expense was $391 million. a decrease from $561 million in the same quarter last year and from $455 million last quarter. Many reflections are displaying cost and expense control, of which our adjusted research and development expenses were $184 million, up 1% year over year. Adjusted selling and market expenses were $102 million, down 7% year-over-year. Adjusted general and administrative expenses were $105 million, down 61% year-over-year, largely due to lower credit loss expenses. Our adjusted operating profit was $124 million, totaling profit from adjusted operating loss of $166 million in the same carriage last year. This improvement was primarily driven by the expansion of our revenue field, higher work margin, and enhanced operating efficiency. Adjusted operating profit margin was 4% this quarter, compared with minus 7.1% in the same period last year, and minus 2.2% the last quarter. Our adjusted net loss was 6%. from $136 million, down from $300 million in the same quarter last year, and $237 million in previous quarters. Our non-GAAP EBITDA profit was $1,100 million, increased by 171% from $406 million in the same quarter last year. Our non-GAAP EBITDA margin achieved 36%. compared with 70% in the same quarter last year and 82% last quarter. It was mainly due to our improving worth-for-feat as well as higher duration cost in our cost of debt as we accelerate our AI computing capacity build-out. As of June 13th, 2026, our cash and attachment equivalents totaled $4,674 million. compared with 4,904 million as of March 31st, 2026. The modest decrease was mainly due to our continued investment in AI infrastructure to support business growth. Looking ahead, we aim to capitalize on the existing growth in AI demand by further investing in infrastructure, expanding our product and service offerings, managing credit and liquidity rates and Improving Operating Efficiency. We remain committed to our all in AI strategy and continue to deliver high quality growth to our shareholders. Thank you all.

So this concludes our prepared remarks. We will now begin the Q&A session. If possible, please ask your questions in both Mandarin and English. So operator, please proceed.

Thank you. We will now begin the question and answer session. If you wish to ask a question, you will need to press star 1, 1 on your telephone and wait for your name to be announced. To withdraw your question, please press star 1, 1 again. We will take our first question. Your first question comes from Liping Zhao from CICC. Please go ahead, your line is open.

Good evening, Mr. Su, Mr. Li. Thank you for accepting my question. First of all, congratulations to the company for obtaining a transfer of OPEC level this quarter. I have two questions here. Actually, they are all about our new MaaS platform. Recently, there has been a lot of discussion about the ability to improve the open source model. May I ask about the impact of this on the company's MaaS business? Let me translate by myself. So, good evening, Ms. Zhou and Ms. Li. Thanks for taking my questions. I got two questions on your mass business. First, how well improvements in open source model capabilities affect the company's mass business? Based on your observations, what's the current usage growth trend and which use cases are driving it most? And second, given the payback period for the mass business might be shorter, Will the company allocate more resources to it? Thank you.

As the ability of models such as GRM and Q3 is improved, we can see that in the field of web coding, there is a replacement effect of domestic models against overseas models. So this is an increase in demand for domestic models. On the other hand, the penetration of agent tech is increasing. More and more scenarios are using agent tech, including our many clients' agents are starting to be produced. So it leads to our agent tech products. On the other hand, in the use of agents, we can see the needs of two divisions. On the one hand, when doing complex problem analysis, So, this is the first question So, Kaiyan Tian So, Kaiyan Tian So, Kaiyan Tian So, in general, it's a balance development strategy.

Okay, so just to quickly translate, so this answer comes from our SVP, Mr. Liu Tao So, in relation to your first question, the development in open source large language models has mainly three impacts. Number one is that we're seeing very big demand coming from BIPOC, and traditionally, foreign models have been taking the lead in this area. However, once we have seen the launch of GLM and K3, these kind of high-performance models, we're seeing increasingly users from mainland China adopting and using this made-in-China large language model. and secondly, the increasing use of agentics scenarios also brought change to our business. And with that, we have launched, as mentioned in the prepared remarks, the agent kit product to satisfy such needs. And certainly, it is worth mentioning that in terms of day-to-day routine tasks and workloads, the choice usually is the price for value kind of models, which are essentially the Chinese models. So that is why this development, open source, large range model is actually beneficial for our business. And your second question regarding the balance between mass business and the computing power. So we basically have different business models for this two business. For computing power business, essentially once we sell the computing power, the utilization is by nature 100% and we usually come with long term and many other contracts to secure the utilization throughout a prolonged period of time and therefore it's relatively safe, so to speak. But for the mass business, it is subject to quite a few factors, including the fluctuation of the token price, the launching of new models, which the customers might prefer to use, and also the operating efficiency that we're able to achieve in doing a mass business. So therefore, we generally balance these two business models and hope to have each one of them complement the other one. So, we generally dynamically evaluate these two business and try to decide how much resources to allocate.

Thank you. Thank you, Tao Zou. That's very helpful.

We will take our next question. Next question comes from Wenjing Vu from CISA. Please go ahead, your line is open.

Hello, Mr. Tao Zou and Mr. Li. Good evening, everyone. Thank you for giving me the opportunity to ask this question. Congratulations to the company for its strong business. I have two questions to ask. The first question is, from June to the current month, how is the mining progress of the chip? How do we think about the development of the latest full-year capacity? The second question is about the entrepreneur. We see that the revenue in the past two months has dropped a bit. How should we expect the entrepreneur this year, that is, the entrepreneur in the industry, and the AI transformation and long-term positioning of the company for this project? Let me translate my question. The first question is that since June, how has the chip procurement progressed in recent months and what's your latest full-year capex guidance? And the second question is about the enterprise cloud. This segment of revenue has decelerated in the past two quarters. How should we think about the full-year enterprise cloud growth and what's the AI transformation and medium-term positioning for this segment? Thank you.

The first question I would like to answer is about supply chain. In fact, we are very concerned about the supply chain every time we release a new product. I believe that you can observe that three years ago, when AIGC started to develop, the supply chain has been running for half a year. And this supply chain does not seem to be a short-term one. It should be a long-term one. So we should see it as a new form of supply chain. The second point is that after so many years of actual development, we can observe that the entire AI industry and the development of the computing industry in China has been advancing at a very fast pace, including our industry. So from our point of view, the most important feature is that we are constantly increasing the number of our cooperative partners and suppliers. At the same time, we are also accelerating the speed of our market. So, Kaiyan Tian So, Kaiyan Tian So, Kaiyan Tian So, allow me to quickly translate. So, the answer comes from our SEP, Mr. Tian Taiyan.

So three points. Number one, actually since 2023, it's been three years and the market has always been hearing voices about the limited supply. So I would say this is actually a new norm. The fresh supply difficulty is actually a long-term kind of situation. But secondly, we should also be aware of the fact that Despite of those constraints, financial constraints, the Chinese cloud computing or AI industry development has not been restricted or largely restricted by that. And the way that we actually tackle with such situations is that we try to increase the number of business partners that we work with. We try to increase the number of suppliers we work with. And we also work with increasing the compatibility of made-in-China chips. You are all very well, very much aware of the, you know, recently many of the Made in China chips are becoming public and they are particularly good in use cases such as model inference. Now, number three, I would like to say that when you look at the TAPEX number from a month to month basis, it is usually quite volatile. And I have to say that the purchasing number because of its usually a large chunk of money in relatively small number of purchases. So the purchase number, if you look at it by monthly basis, it's actually not a linear number. So I would say that for our whole year CapEx estimate, it should still be in line with what we have been expecting and our CFO, Li Yi, should be able to give you more details in that regard.

Hi, Qinxin. Our CapEx venture includes Capital Bank assets who lead the arrangement, reaching $6.2 billion in the first half of 2026, accounting for over 75% of all four-year CapEx last year. Where July's data cannot fully represent third quarters of all trends, it clearly shows tangible growth acceleration. Accordingly, we maintain our four-year CapEx-based case Unchanged at $15 billion. Thank you, Tingtian.

Thank you. We will take our next question.

Oh, sorry. We need to continue for another question. Apologies.

Okay. Okay. But we also think that it is not easy to use two-month income benefits to continue to pay off the whole year's business situation. In this round, we think that the overall benefit change includes three factors. The first category is the increase in the entire memory and storage price since the second half of last year. This increase will lead to the government and corporate customers needing to adjust their budget repeatedly. This kind of reverse adjustment will lead to The contract signing period and the budget period will be longer than before This is a very obvious trend in the market in the first half of the year The second one is that the government and foreign companies themselves have this kind of signing, hand-off, and receipt It's a record-breaking trend in terms of income confirmation Because as you all know, especially large-scale projects So, Kaiyan Tian So, Kaiyan Tian We also take the initiative to adjust the income structure of the industry cloud. In the past, the industry cloud mainly relied on the construction of cloud technology facilities, system integration, and project delivery. The income scale is relatively large, but the characteristics of its project quality are relatively obvious. Then there will be technical fluctuations. So this year, with this, especially the small lobster, this wave of fire in the Chinese market, this year we took the initiative to reduce this low hair power. . . . Okay, so this answer comes from our VP, Yujin. So generally, I don't think, although we're seeing relatively slow growth in the enterprise cloud segment,

I would say it is not the right way to look at it from the linear extrapolation perspective. I will give you three reasons. I think number one, just to explain why we're looking at relative weakness in this regard, is that the upstream supply pricing hike, which changed quite significantly in recent quarters, has affected are prospective customers, essentially the SOE companies and also the government agencies. They have to frequently adjust their budgeting quota, which delays their decision-making process. So that's number one. And number two, you're all quite aware that the seasonality in enterprise cloud business is quite strong. Usually, the delivery and the revenue recognition are concentrated in the second half of every year. So we have actually quite a strong pipeline to deliver in the second half of the year And thirdly, this is actually a result of a proactive adjustment of our business structure Namely, proactively from the project-based business model to operating-based business model Where operating business model from a financial reporting perspective is automatically classified into a public cloud So this is not simply as it would seem as a weakening of the enterprise cloud of business. So that's the three points I'd like to offer. Thank you.

Thank you. Thank you. We will take our next question. Your question comes from Timothy Zell from Goldman Sachs. Please go ahead. Your line is open.

Good evening, Mr. Zou. Thank you for accepting my question. I have two questions I would like to ask. The first one may be back to our new MASS business. I would like to ask about our Jinshan Yun platform. Compared to other competitors in the market, what are our positioning, scenarios, and competitive advantages? Can you share more details about the income confirmation and profit rate of our MASS service? This is the first question. The second question is about some changes in the price of our AI cloud services. Can you share with us the trend of price changes in the price of AI cloud services and the price situation of the industry that we have seen in the past few months? We also announced some actions to increase the price or reduce discount. What is the response of our customers now? Can you help us clarify some of the price factors that affect the income increase of AI cloud? Thank you, Benjamin, for taking my question. My first question is regarding the mass banish. Just wondering, compared to the peers in the market, how do you think about the Kingcell Cloud competitive advantage in the mass services in terms of the application scenarios, et cetera? And could you share more about the revenue recognition and the profitability profile of the mass service banish? Our second question is regarding the overall pricing trend in the AI Cloud business. Just wondering if you can share what is the latest trend over the past couple months and what have you heard from the customers after you announced certain price hikes or discount reduction over the past few months and whether you are able to quantify the impact from the price hike to your overall AI Cloud revenue growth. Thank you.

Let me answer these two questions. The first one is about the positioning advantage of our new MARS platform. First of all, our positioning is quite unique in the market. First of all, we don't have a large-scale model. Second, we are a cloud marketer. I think there are several customers in the MARS scene that we see in the market. One is a cloud marketer who makes large models, such as Aria and Baidu. This is the so-called pure cloud holding factory that you may have seen recently. I think we may have some similarities with them. First of all, we don't have a large self-proclaimed model. So in terms of policy guidance for the selling model, we always guide our sales and business team to sell the best model, the model that customers like the most. For example, JM5.3, for example, KMI 7. Including maybe in the video production field, such as C-Dance or MiniMax H3. So, in this aspect, we will sell the models that our customers prefer based on the needs of our customers. We will not be tied to the political task of selling a certain model. I think this is a kind of flexibility in terms of competitive strategy. Secondly, for Cloud Holdings Limited, there is a very important production element in the production of holdings, which is computing power. Only companies with low-cost computing power can make money on the whole mass production chain. If it is purely for the production of software to strengthen the computing power, then a large amount of profits may be occupied by the computing power providers. I think everyone can pay attention to this. For example, a card of an overseas NCP, and then look at the price, you can calculate that a purely non-resource manufacturer can earn a lot of money. This is what I think is the advantage of the March platform. In addition, about... Why don't you go back first and ask this question first?

So, in relation to your question about the positioning, we do have a unique positioning in the en masse business. Mainly, we're different from some of the full stack of providers, which they have their in-house or proprietary models. We do not have such models, and therefore, correspondingly, we do not have to sell those large numbers models that our affiliated companies have to offer. And as a result, we're able to actually sell and we actually encourage our sales team to sell the models that our customers like the most. For example, the GLM, etc. So that's number one. And secondly, it's quite important in today's market to have your proprietary or your own computing power, which is the only way that you can actually secure significant profitability in this business. Thank you.

I'm very concerned about it. So I think we can see that it is very easy to accept our price. So in the process of this price increase, on the one hand, we catch up with our cost increase, and on the other hand, we still maintain our profits or improve our profits. On the other hand, we are of course using the same principle to calculate the price of the machine of the business. It must be growing, but because of our unique ability, On the one hand, we will provide a price guide that follows the growth of the profit price. However, we will maintain the profit price and even increase our profit price. On the other hand, we will also accept the profit from the customer support to organize and operate. In this episode, we have seen many such scenarios. Under such a scenario, the profit price of our business is obviously higher.

So, in relation to your question about the price hike, so there are basically two products or solutions that we have employed increasing price. Number one, that is storage, and number two, that is computing power. So, I'll talk about them separately, respectively. So, in terms of storage, storage is usually, the incremental amount of storage actually comes with the intelligent computing demand. That is a relatively small portion. of the intelligent computing overall ticket size. And therefore, in the vast majority of the customers that we negotiated with, they relatively easily accepted such a price hike. In which case, as a result, we're actually able to not only, in some cases, not only pass through the increase in our cost, but also increasing our profitability in that scenario. Number two, in terms of computing power, because of our specific capabilities, including path capabilities as well as the operating, maintenance, and network capabilities. Again, we are able to pass through that cost hike into our customers. In some of the cases, we also increased our profitability. And in this quarter, we have also some projects which we are doing manage the services, which is an SLI business model. We look forward to seeing more of that coming to and reflecting the financial statements.

Thank you. We will take the next question. Your next question comes from Wei Xiong from UBS. Please go ahead, your line is open. Okay, thank you.

Good evening, everyone. Congratulations on the very strong performance of this quarter. Thank you for accepting my question. I have one question to ask. We just talked about the difference in mass positioning on our platform. Good evening, management. Congrats on a solid quarter and thank you for taking my question. Considering the proprietary models and user ecosystem of other cloud providers, how should we think about our long-term positioning? in the cloud market and the sustainable margin level down the road. Thank you.

So, as a medium-sized business, we will always maintain a good relationship with each factory, and we will launch these model online services as soon as possible. And our own resources and the resources of the factory are combined to provide customers with a highly reliable cloud-based March service. I think we don't have any psychological burden in this regard. On the other hand, we can see that in terms of the total amount of resources we have in hand, In most cases, we can provide customers with higher coverage in terms of SLA. I think this is a better support for Mark Yeo for large-scale infrastructure for cloud manufacturers. On the other hand, we will cooperate with the original factory. For example, we can cooperate with the original factory in the model push frame, including some models that are not exposed to the public at the time of release, to improve our push efficiency. On the other hand, we have also been investing in pushing and optimizing our own work for a long time. At present, our team has actually verified that we can reach or even get close to many open source models. So, we believe that for us, for our AI cloud service provider, it is important to be able to offer the top models which the customers like and also stable services to our customers.

So, as mentioned, as a neutral cloud slayer, we are able to be in good relations with all of the top model providers, large-language model labs, and be able to provide the best model according to our customers' demands. And also, we're able to, based on our technology capabilities, we're able to provide highly available and highly reliable services to them out of the SLAs that we sign with them. I think, thirdly, in relation to the profitability question you asked, it is important to work closely with the LLM firms, labs, to, for example, to optimize the inference of those models. And that would include, for example, working with them based on the undisclosed weighting of the models to increase our inference, model inference efficiency. in some of the cases were able to get to very close level or even reach the same level of the inference efficiency coming out from the LLM companies themselves. Thank you.

Thank you. We will take our final question. And your final question comes from Ying Lu from Morgan Stanley. Please go ahead. Your line is open.

Thank you very much for the opportunity to ask a question. First of all, congratulations to the company for its excellent performance. My question is to ask about the level of investment return or RIC in the two business models of Math and Calculate. Let me translate my question. I would like to ask under the two business model, computing power leasing and model as a service, what is ROIC for these two business models and what is marginal change for the ROIC? Thank you.

Thank you, Liu Yang. At this stage, we don't disclose separate IOSD on mass and AI computing power services. Because IOSD varies across projects, joined by feedback cycles, force margins, fixed assets, and depreciation policies. Overall, must be with much better probability than AI computing power services at this stage. We have seen continued improvement in operating leverage as our AI business scales up fixed costs are steadily diluted and our trailing travel monsters adjusted operating profit has turned positive, driven a gradual recovery in our overall ROIC. We adhere to a demand driven and disciplined AI investment strategy with a strong focus on capital efficiency. with the continuous business structure optimization and the material AI optimization. I think our overall LSE will keep improving steadily.

Okay, thank you.

Thank you. There are no further questions. Apologies to the question and answer session. I will hand back for closing remarks.

Okay, thank you all for joining us today. If you have any further questions, please contact our IR team. So have a good evening. You may now disconnect. Thank you.

This concludes today's conference call. Thank you for participating. You may now disconnect.