E2E Networks Limited — Call Transcript 2025

Jan 28, 2025

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E2E Networks Limited

CIN- L72900DL2009PLC341980

1st Floor, A-24/9, Mohan Cooperative Industrial Estate Mathura Road, New Delhi-110044, Phone No. +91-11-4084-4964

Email : cs@e2enetworks.com, website https://www.e2enetworks.com/

Date: January 28, 2025

Listing & Compliance Department The National Stock Exchange of India Limited Exchange Plaza, C-1, Block G, Bandra Kurla Complex, Bandra (E), Mumbai, Maharashtra-400051

Ref: NSE Symbol- E2E

Sub: Transcript of Analysts/Investor Earnings Conference Call for Q3FY25

Dear Sir/Madam,

Pursuant to Regulation 30 of Securities and Exchange Board of India (Listing Obligations and Disclosure Requirements) Regulations, 2015 and other applicable Regulations, please find enclosed herewith the Transcript of Analysts/Investor Earnings Conference Call which was held on Wednesday, January 22, 2025 at 05:00 P.M. (IST) to discuss the Company’s Q3FY25 earnings.

The aforesaid information shall also be made available on the website of the Company at https://www.e2enetworks.com/.

Kindly take this on record.

Thanking You,

Yours faithfully,

For E2E Networks Limited

RONIT

Digitally signed by RONIT Date: 2025.01.28 17:20:02 +05'30'

Ronit Company secretary & Compliance Officer ICSI M. No.: A59215

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“E2E Networks Limited Q3 FY25 Earnings Conference Call”

January 22, 2025

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MANAGEMENT: MR. TARUN DUA - MANAGING DIRECTOR, E2E NETWORKS LIMITED

MS. MEGHA RAHEJA - WHOLE-TIME DIRECTOR & CHIEF FINANCIAL OFFICER, E2E NETWORKS LIMITED MR. RONIT GABA - COMPANY SECRETARY, E2E NETWORKS LIMITED MODERATOR: MS. SOUMYA CHHAJED - GO INDIA ADVISORS LLP

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Moderator:

E2E Networks Limited January 22, 2025

Ladies and gentlemen, good day and welcome to E2E Networks Limited Q3 FY25 Earnings Conference Call hosted by Go India Advisors LLP.

As a reminder, all participant lines will be in the listen-only mode and there will be an opportunity for you to ask questions after the presentation concludes. Should you need assistance during the conference call, please signal an operator by pressing the “*” then “0” on your touchtone phone.

I now hand the conference over to Ms. Soumya from Go India Advisors. Thank you and over to you.

Soumya Chhajed:

Thank you, Steve. Good evening, everyone. We welcome you to E2E Networks Limited Q3 FY25 Results Con Call.

We have with us on the call today Mr. Tarun Dua – Managing Director; Ms. Megha Raheja – the Whole Time Director and Chief Financial Officer; and Mr. Ronit Gaba – the Company Secretary.

I must remind you that the discussion on today's call may include certain forward-looking statements and must be viewed in conjunction with the risk that the Company may face.

I now request Mr. Tarun Dua to take us to the Company's “Business and Financial Highlights”, subsequent to which we will open the floor for Q&A. Thank you and over to you, sir.

Tarun Dua:

Thank you, Soumya. Good afternoon, everyone. Thank you for joining us for the E2E Networks Q3 FY25 Earnings Call. I hope you are all doing very well. We are happy to share our journey and provide an update on the steps that we are taking to sustain our growth and strengthen our position in the cloud GPU and AI/ML workload space.

As you are all aware, last quarter, we achieved strong operational momentum driven by key advancements deployed in our cloud infrastructure. Our cumulative deployments have now grown to nearly 700 H100 GPUs, 256 H200 GPUs, and around 700 non-H100 and non-H200 GPUs. And we also successfully raised another round of more than Rs. 1,000 crores from L&T for expanding our accelerated cloud infrastructure and focusing on the next generation cloud GPUs and GPU clusters.

This quarter, one of the most important developments for us has been deepening of our strategic partnership with L&T, a collaboration that marks a significant milestone in our journey towards revolutionizing AI and cloud infrastructure in India. With L&T's deep expertise in data center management and our advanced AI compute infrastructure and cloud experience, both the companies can jointly offer robust and scalable solutions to the enterprise, government, and organizations. This collaboration will open new revenue streams, drive growth in high-demand

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AI services, and we will also improve our operational efficiency leading to enhanced profitability and revenue. Together, we will leverage each other's strengths to capture a larger market share and create a more powerful presence in the AI and cloud sector.

Each quarter, we continue to stay ahead of the curve by embracing latest advancements in cutting edge GPU technology. In line with our growth plan, we are also expanding our data center capacity from 4.2 megawatts to nearly 10.2 megawatts, which is a move which is supported by our recent fundraise. We continue to innovate at a rapid pace to expand our battery-tested, scalable, high-performance cloud infrastructure for education, research, enterprises, government and startups. The surge in demand for cloud and AI services has helped us achieve strong growth and we have strengthened our position in the market. By leveraging our advanced AI cloud platform, we are committed to driving India's digital transformation, empowering government and enterprise initiatives, and positioning India as a global leader in AI and cloud innovation. So, our platform supports a wide range of cloud native services today, including CPU and GPU environments, virtual machines, native containers, serverless architecture mode. We offer flexible high-performance storage solutions across object storage, across block storage, parallel file systems. And we also offer advanced networking, load balancing, firewalls and relational database services, NoSQL database services, VectorDB services.

We have continued to develop our AI/ML platform called TIR, which is designed for data scientists and developers to streamline their AI/ML workload, like training inference and model endpoint deployment. With an early mover advantage in the AI/ML space since 2020, we continue to offer a superior price to performance ratio helping customers scale without any longterm commitment to our cloud. As the AI market, especially generative AI, rapidly expands, we are positioned to help businesses capitalize on AI's transformative potential, filling the demandsupply gap in India and supporting countries' digital evolution.

Now, I would like to hand over our call to CFO – Megha, who will briefly touch upon the financial and operational highlights of the quarter-ender review. Over to you, Megha.

Megha Raheja:

Thank you, Tarun. And good afternoon, everyone.

Let me first start by giving you some of the key “Financial Highlights”:

I will summarize the performance of Q3FY25. For Q3FY25, the total revenue stood at INR 416 million, which witnessed a substantial growth of 73.7% on year-on-year basis. EBITDA for the quarter is INR 246 million, which further provides a growth of around 119% on year-on-year basis. EBITDA margin for the current quarter is 59%, which demonstrates a growth of 1223 bps year-on-year. PAT is reported at 116 million, which demonstrates growth of 108% on year-onyear basis. PAT margin for December quarter is 27.8% and diluted EPS is 7.03 for the quarter, which is around 86.5% on year-on-year increase.

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E2E Networks Limited January 22, 2025

Now we can do a quick comparison with last quarter that is September ’24:

We have witnessed total revenue decline of 12.6% on quarter-on-quarter basis from 476 million to 416 million in the current quarter. EBITDA for the current quarter is 246 million as against 314 million on the previous quarter. As a result of this, PAT for the current quarter is 116 million exhibiting quarter-on-quarter decline of 4.3% from Q2 FY25.

While our overall outlook, on AI computing infrastructure in all forms remains very strong, there has been some dip in the revenue numbers as compared to previous quarter. So, the decline in revenue and growth in the current quarter is nearly due to churn and downscaling of training deployments, which are, as you know, bursty in nature. And the revenue effect of deprovisioning has tended to be concentrated during this quarter due to concentration and due to our smaller scale. We anticipate the effect of bursty training workloads would eventually become muted at a larger scale and then we intend to build cloud GPU computing infrastructure. We anticipate a resurgence in demand for advanced AI solutions over medium term.

Now an important update as well:

During 2024, we had raised a total of 14,849 billion through presidential issue of equity shares. Out of this, we have utilized INR 1,508 million till Q3’25 and we have balance of 13,349 million as on December 31[st] .

And that concludes the update for the quarter. And now we can open the floor for question-andanswer session.

Moderator:

Aastha:

Megha Raheja:

Aastha:

Tarun Dua:

Thank you very much. We will now begin the question-and-answer session. The first question is from the line of Aastha from Pkeday Advisors. Please go ahead.

The first question is that what was our CAPEX deployed in Q3FY25 and how much CAPEX we plan to do in Q1, Q2, Q3 of FY26?

So, we have deployed 2,017 million till December. That is Rs. 201 crores.

And for the next year, ma’am?

We are kind of like very agile in terms of like how we deploy the CAPEX. So, we are anticipating that like majorly based on the pipeline we will evaluate and based on that like we will kind of like do just-in-time deployment as much as possible over next several quarters. And based on that, the outlook is not based on a plan, but practically by looking at the end of the quarter, what was the CAPEX that was done. So, it would be based on the demand outlook that we kind of like come up with.

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Aastha:

Tarun Dua:

Moderator:

Amar Morya:

Tarun Dua:

My second question is, I understand the answer was given earlier, I missed it. But I wanted to understand what was the reason for a drop in quarter-on-quarter revenue?

We currently have a fairly small scale and compared to that, some of our larger customers, their deployments were quite large. Now our goal obviously is kind of like build a much larger infrastructure which is spread across much larger footprint of larger customers. Now, training workloads by their very nature are bursty. So, which means that typically, although you don't expect them, like, okay, at what time does the training get over? Or like a particular data science group decides that, okay, the training needs are over for now. So, unfortunately, all of these got concentrated during the Q3. And we have also seen somewhat muted demand for the end of this quarter, December typically is a slow month, so where we have not been able to kind of like recover back that unutilized infrastructure to a higher utilization rate. So, that is the current status today. So, obviously in the medium term, we expect that like as the footprint of customers grows larger, then the effect of like 1 or 2 large customers declining their training needs should not impact us. So, that is subject to growth of our overall scale.

Thank you. The next question is from the line of Amar Morya from Lucky Investments. Please go ahead.

Couple of questions from my side. Firstly if you can help us understand this L&T tie-up. And how it is going to help us in long run in terms of gaining some large enterprise clients that is number one. Number two is in terms of our current, I think we have something around 4,200 megawatts. So, what is the peak revenue potential of this capacity? And by when we will be reaching that kind of peak utilization level. And in that, how much of the percentage would the enterprise versus the retail? These are two questions.

So, your first question is about the L&T tie-up, how it would help us in the long run. See, obviously, a small Company when they tie up with a big Company, you get the advantage of the size and scale and connections that a bigger Company brings in and also their own capabilities in terms of technology as well as infrastructure. So, over here, we obviously intend to leverage all of those things. It's quite early days. I think it's been practically less than a few weeks. So, basically, where we have started interlocking, started working together, and jointly exploring opportunities with the customers, so in the long run, I believe that the interlock in terms of jointly exploring opportunities would obviously help both the businesses who bring their own strengths to the table. And we will obviously be pursuing a lot more what we internally call as long cycle business opportunities. So, compared to working with SMEs or smaller companies, the long cycle business opportunities are obviously long-term in nature and more rewarding in terms of lifetime value. And smaller companies obviously make the decisions fast. That has been the mainstay of our business till now. I think that natural change will come over like medium term where we start working with larger customers in conjunction with our partners including L&T, and I think that is one of the major changes that we are going to see, where we are pursuing more

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enterprise business, apart from what we continue to pursue in the SME and startup and other organizations that we have continued to do. Now, in terms of megawatt capacity, that's a measure of the data center capacity, how much IT load you can put in. Typically, these are fairly wellknown numbers in the industry, which are point-in-time numbers, like that one megawatt of what it pertains to today, would vary between, say, $25 million to $50 million over a spectrum of, say, a smaller player and a large hyper scaler. So, each megawatt would represent different revenue levels for those. So, it could vary anywhere between, let's say, $10 million to $50 million. And it is also dependent on the kind of workloads. Now regarding percentage of enterprise versus retail, I think it would be very hard to predict today. But my assumption is that like mostly wherever in the industry there are like large revenues, large profit pools. Obviously, they are the ones who invest the maximum amount of money to compute. So, what I believe is that like there would be a shift from smaller companies to bigger companies in terms of overall concentration of our revenue over the medium and long term.

Now, it would be very hard to say like how the percentages would vary, but the kind of capabilities we are building today are very focused on meeting the requirements of the larger enterprises.

Moderator:

Ashwin Kedia:

Tarun Dua:

Ashwin Kedia:

Tarun Dua:

Thank you. The next question is from the line of Ashwin Kedia from Alchemy Capital Management. Please go ahead.

Just one question was how has your revenue percentage changed from training to inference quarter-on-quarter and how do you expect that to change moving forward for the next year?

The major concentration of the revenue obviously has been training, will continue to be training. I think training will continue to play a very major role, I think, for the foreseeable future. That being said, of course, the impact of large bursty training workloads is what we have seen in this quarter. But as we scale up, I think that effect would be less and less pronounced. So, I think it's a function of us being in our very early days of our journey as of today, which is what we are seeing. I think that is probably the question you're trying to ask. So, the other part being that I believe that ultimately it's going to be somewhere the ratio eventually over the medium and long term, I think it should become more like 50:50 or maybe 60:40 in favor of like training.

And what is your inference revenue today percentage of your total revenue?

We don't really like track it today very separately because there is like, it's a very nebulous line between building training infrastructure for foundational models being built from the ground up versus foundational models being fine-tuned at what level. So, like the graduation from training to inference is like, kind of like there is a whole range. So, you can't really classify what constitutes training versus inference. So, inference and production would continue to remain smaller, like even in the global context itself. Like so ultimately, it's the training. So, today we are like all the AI is like in its early phase. So, for a significant future, training is going to

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constitute a majority of revenue for not just for us, but overall in the cloud ecosystem, training would constitute the majority of the revenue.

Moderator: Thank you. The next question is from the line of Garvit Goyal from Nvest Analytics Advisory. Please go ahead.

Garvit Goyal: Can you help me understand our geographical concentration particularly in data center business like North America is the biggest market for the data center and if interest rate remains elevated hyper scalers like Meta, Microsoft may scale down the CAPEX which in return will impact our business. So, can you put some color on it like is that going to happen or otherwise what is your take on it?

Garvit Goyal: So, I can't really comment on what large global players are doing. From the view from whatever we are, obviously all of us are hearing in the industry news, is that the investment into AI is definitely going up. Now regarding data centers themselves, we rely on data centers only in India. So, we have one facility in the North, in the Delhi NCR region. And we are in the process of establishing the second facility near in the Greater Chennai area. And geographically from an infrastructure point of view, we are concentrated in India. From a customer point of view, primarily the customers we tend to serve are in India. Now, over the medium and long term, obviously, we will continue to look for opportunities to expand out of India into other markets as well. So, and this may necessarily not be on the public cloud, but these opportunities could be in the form of software and services and support as well.

Moderator: Thank you. The next question is from the line of Ketan Kapasi from Taurus Investments. Please go ahead.

Ketan Kapasi: Sir, what will be the impact of the Company of this new GPU rule proposed by the US government?

Tarun Dua:

In the short term, I don't think there would be any immediate impact. So, one is, of course, there is like about like a more than a period of a quarter before these regulations come into effect. Secondly, based on what India is doing today in terms of overall volume, currently, we are not really hitting the limits that are being placed. And there are certain exceptions which we have not fully studied, but there are exceptions around certain end users who are consuming up to 1,700 GPUs, then not being counted in the overall country limit. There would be companies based in the US itself when they bring their infrastructure to India, potentially they are not being counted. So, overall, I think that impact assessment would be more, like our belief is that that should be assessed in about two-year time frame rather than on an immediate basis.

Thank you. The next question is from the line of Keshav from Niveshaay. Please go ahead.

Moderator:

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E2E Networks Limited January 22, 2025

Keshav:

As I can see that your non-H100s and H200s have seen an increase of 100 GPUs. So, is it like that the demand for the H200s and H100s are currently not much? That is why you have deployed –

Tarun Dua: The demand environment continues to remain strong. So, the pipeline is there obviously for both H100 as well as H200. What we are potentially missing is immediate closures that we were seeing in the past. So, I think the sales cycles have grown. I don't think the demand has gone away. I think the sales cycles have become longer. I think that is what we are seeing.

Keshav: The H100 and H200, are they completely utilized or they are underutilized as of now? Tarun Dua: No, they are underutilized as of today.

Keshav: So, how much of that would be the percentage if you have that number?

Tarun Dua: It's a fluctuating number because a lot of workloads in the cloud, it's very, very hard to measure like a point in time very, very easily. But we have reasonable capacity on both sides, both being consumed as well as being made available to the customer.

Keshav: And while we are sitting on a big pile of cash, so we see the utilization has only been Rs. 150 crores. So, how much time do you think that the funds are going to take time to utilize? Like how much is the CAPEX cycle if we can comment on that?

Tarun Dua: Very very hard to commit to kind of like spend that money in a market where the infrastructure, as in like the high-tech equipment like GPUs and CPUs, they get like upgraded version. So, it is very, very important not to kind of like build a lot of inventory of what would potentially kind of like will get superseded by a newer version. Although these are all long cycle products, they will sell for 7-8 years. But it is also important to have dry powder for the latest version. So, that has always been our strategy that like always be able to invest for every version. So, that where we will continue to evaluate the demand and continue to try and keep the utilization high, but not have so little capacity idle that we are not able to serve this new demand. So, it's a balance that we have to draw out. I think that balance will get established. And I think those future CAPEX cycles, they will obviously continue to get established. And when we look back, we will be able to say with certainty that okay, this was the CAPEX that was spent as opposed to trying to predict today. So, we are reactive, not predictive. So, that is what we will continue to do. Like overall outlook for both medium term and long term, we continue to see that the AI transformative potential and its adoption is definitely picking up.

Keshav:

So, my last question is that NVIDIA has recently launched BITS , I guess, the new product, I don’t know if I am recollecting the name correct? So, is that fair to the cloud GPU since the devices...

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Tarun Dua:

In the compute market, you have to understand that there would be multiple approaches to solve various problems. So, obviously, they would have, NVIDIA would have seen gaps in terms of what can be serviced from the cloud for like micro units. See, the end devices have always existed. So, if you look at Edge compute, that has always existed. Now they've built a slightly more powerful Edge machine. It does not take away the need for data center in any way. I think that this would be one more thing that would add to the ecosystem. I don't think it's a zero-sum game that whatever you're able to do on the Edge, there would be still further more workloads on the cloud.

Keshav:

Okay, so technically like what is the difference? Could you explain it a bit?

Tarun Dua:

See, there's a whole range of devices that kind of like do a bit of AI. And this is a slightly more powerful edge. So, when you talk about what you can do in the cloud, it's a lot more number of things compared to what you can do on the Edge. See, it's not just about being able to do an inference or training in isolation. You also need access to be VectorDbs, you also need access to relational databases, you also need access to a lot more storage, you also need redundancy, you also need reliability, you also need security. So, there are certain things that you can do on your laptop, certain things you can do on your mobile, certain things that you can do on workstations, certain things that you will be able to do on a Edge device which is very powerful, required for one particular organization or one particular business unit, and there would be a lot more things that you will continue to do on the cloud. So, it's not like really comparative in terms of like a straight away movement of workloads from here to there.

Moderator: Thank you. The next question is from the line of Akshay from CD Integrated Services. Please go ahead.

Akshay:

Sir, my first question is, what is our sourcing strategy for the GPUs like do we source GPU from the NVIDIA and then we do add some value like we make our architecture or we do source it from companies like HP or network technologies?

Tarun Dua:

That's a good question. So, GPUs do not work in isolation obviously. So, see I think like before the Hopper architecture during the Ampere architecture the A100s and A30s and A40s, etc. There was a very large prevalence of GPUs being deployed in the PCI form factor, where you typically bought the GPUs and deployed them in compatible servers, which were typically on the hardware compatibility list of major vendors like NVIDIA or AMD or Intel. So, that used to be the approach. Today, it's a lot more integrated approach where a vendor like a Dell or HP or a Netweb or a Supermicro, I think there are like 10 to 15 such vendors who build GPU servers. So, now today is the approach of like, kind of like buy a complete integrated hardware, which is a design which is done by a principle like an NVIDIA or an AMD or an Intel. And the major bill of material also comes from NVIDIA or Intel or AMD. Currently, of course, we are very majorly focused on NVIDIA. That does not preclude us to work with other players in the future. We buy these integrated servers. Typically, that is called the HGX line. That's the major one. Some of

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the other GPUs like L40s, L4s, they continue to follow the older model of being able to kind of like deploy PCI cards into boxes which typically come without GPUs. So, we do that as well. But these are less powerful GPUs typically. The more powerful GPUs come in their pre-built integrated boxes from the major GPU server vendors.

Akshay:

And my second question is, what is the competitive intensity in our kind of business? Like, do we face intense competition from companies like multinational companies like Amazon Web services and Google Cloud?

Tarun Dua:

Yes. So, as AI has become very, very mainstream since 2019-20, since we have been working with AI, the competition has become far more intense today. And competition in the compute and technology world has always been intense. So, that has definitely increased. So, typically, there are a lot more players today who are doing the cloud GPU infrastructure. As the market has expanded, there is space for even more number of players. I think that competitive intensity and the market both will continue to expand.

Akshay: So, what is our competitive edge? Like if any player, if any Company would have to choose from the other Company like ours, then what is our competitive edge and why they will choose us?

Tarun Dua:

See, like everyone will build their niche solutions which are targeting particular solutions for data science teams, particular industry solutions, and everyone will eventually find their niche to succeed in. So, again, like I said, these are early days. We continue to learn from our customers. We continue to learn from the ecosystem. And we have an R&D team which has the ability to move very fast and build. And we have built a lot of intellectual property that is integrated into our cloud. And this will continue to play a big role in terms of differentiation.

Moderator:

Thank you. The next question is from the line of Abhishek Shindadkar from InCred Capital. Please go ahead.

Abhishek Shindadkar: Did the 3Q in terms of both revenue margins in terms of ramp up, did it play out as we anticipated or there was a negative surprise? And if yes, it was towards the end of the quarter, towards the middle of the quarter, any color in terms of how the revenue paid out for us?

Tarun Dua:

In terms of when you are a cloud operator, all these things basically like, I mean, you, like, we have maintained, like, I think, like, over last many, many calls that, like, we react to every situation, we don't try to predict any situation. So, basically, like, nothing surprises us basically. So, like, we operate on the principle that, like, we have the ability to react to anything.

Abhishek Shindadkar: And regarding your mention about these bursty workloads, was it part of the change was among our top large customers?

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Tarun Dua: Yes, so obviously these are the larger customers, that's why the impact is visible. If it was smaller customers, then we wouldn't have seen the impact. Now, our whole philosophy over here is that eventually as we scale up our infrastructure, as we scale up our customer base with larger customers, larger deals, so eventually, each single large customer will not impact us that much. And that is definitely the path that we continue to follow.

Abhishek Shindadkar: Just last one data point in terms of depreciation, we saw a significant jump in the depreciation. How should I read this in the context of the hardware, especially the GPU numbers that you've shared between the last quarter and this quarter, where the impact is only in the A100s, V100s. So, how should I read that deprecation number?

Tarun Dua: Like not very easy to answer that question. So, how do we read depreciation number? I guess like I can hand that question over to Megha to kind of like understand it better and answer it better.

Abhishek Shindadkar: The idea to understand here is that did any of our assets were billed, I mean were in you know our part of the depreciation but yet not billed. Maybe one reason could be the bursty workloads. Megha Raheja: Let me clarify this. Depreciation is charged on an SLM basis over a period of six years, which is the life as per the Companies Act as well. So, once we do additions in a particular quarter, then depreciation will continue over a straight-line method over a period of six years.

Moderator: Thank you. The next question is from the line of Pankit Shah from Dinero Wealth. Please go ahead. Pankit Shah: Actually, I wanted to understand on the platform side, what are we doing on the platform side which will differentiate us or which will make our customer acquisition journey more smoother? Something on the cloud side? Tarun Dua: Pankajji, so there would not be any one single thing which will have the better impact. But having an integrated platform that works seamlessly across multiple functions and having good user experience for the data scientists, for DevOps, for developers, and incorporating a lot of abilities that are being derived from AI into the platform. I think that is the key to success. Essentially, it's like any software’s adoption cycle where you keep taking the product feedback from the customers, from the industry, from the market, and continue to build at a rapid pace that creates the sustainable long-term advantage with the platform. Pankit Shah: Working on this, like an end-to-end, as you are saying, is it like currently very limited few players are there, or how should we look at this? Tarun Dua: One key advantage for our platform, obviously it has been in continuous operation for the past 10 years now. So, that's one of the, so a decade-long experience of running some very critical

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services has made the entire platform very battle tested. So, that core platform continues to be very robust and secure. And we continue to build on the same principles and continue to focus both on reliability, scalability, and the features that are required for our customers.

Pankit Shah:

Actually, I was trying to understand on the integration side, the software capabilities where we can like move from more like a CAPEX led business to more like a software led business which will be more recurring in nature over a longer time?

Tarun Dua:

So, like I didn't really understand what the question is over here. Like the cloud consists of like all these things basically. So, cloud is a catch all term for like basically like what data center provides to you, what physical hardware in terms of server switching, infrastructure, storage infrastructure that gets deployed, all of that gets integrated with the software. And all these abilities, obviously, there are multiple delivery approaches towards these abilities. So, it could be in the form of a public cloud. It could be in the form of a private cloud. It could be in the form of on-premise infrastructure. Or eventually, it could be the form of some services. So, all of them constitute various parts of the cloud infrastructure. You can say that like various ways of looking at the same thing. And software obviously constitutes a key piece in all of this.

Moderator: Thank you, sir. The next question is from the line of Hardik Gandhi from HPMG Shares and Securities. Please go ahead.

Hardik Gandhi:

Just two questions for my end. The first question, I just wanted to like two timelines. First, you mentioned that we are doing a data center expansion. So, I just wanted to know by when are we planning on executing that? And the second part of the same question is that we've applied for a tender in government AI project, right? So, any update or any time where we can expect an answer on that?

Tarun Dua:

We are trying to build a second new location as quickly as possible in the south near Chennai. And second, basically, whatever is happening in India AI, I think that is highly visible and public information as we see any impact due to that, it will obviously inform all the stakeholders. So, currently, I think what has happened is that there has been a technical evaluation, where we have qualified a technical evaluation. Now, the second part of the financial bid will have been opened, but they have not been, the L1s have not been declared as yet. So, that is the current status at India AI. So, like once the India AI team declares like, okay, these are the L1s we have received, I think at that point of time, they would ask for L1s to be matched by players who want to be empaneled. And then I guess some of the players would get empaneled. So, we continue to put our best foot forward over there.

Hardik Gandhi:

But you did not mention the timeline for the data center, the Chennai one, when would that be operational?

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Tarun Dua: We are trying to do it as soon as possible. I guess like during the next quarter, it would get operationalized whether we will be able to deploy all the services in the first quarter itself, like remains to be seen. Our effort would be to kind of like to get as many services as we have in the current location to be also made available in the second location.

Hardik Gandhi: And I could see a huge amount of other income. Is that a one-time income or can you please explain on that front?

Tarun Dua:

I think majorly it's the treasury income from the recent fund raises, is my understanding.

Hardik Gandhi:

And just one last bit. So, last month I can see the ARPU has reduced.

Tarun Dua: Some of the larger customers on the training side has churned or downscaled. So, that has resulted in the ARPU decline.

Hardik Gandhi: So, when we say we are going to get back to normal, that will normalize over a longer period of time, right? So, how long are we expecting, are we expecting these numbers to remain….

Tarun Dua: We can't put very sharp timelines over there, like obviously like the AI industry, the AI infrastructure compute, all of it continues to expand. We continue to build the capabilities that we are seeing are needed by our customers. And we continue to convince newer and bigger customers of our capabilities. And I think it would be hard to put a timeline on like when that starts to show up in the numbers.

Hardik Gandhi: On a safer side, we can take this as a conservative number and continue or do you expect much more churning going in the short term?

Tarun Dua:

Like I said, we are not really predicting anything, but obviously, our goal is to continue to build and expand. So, I think we see them as a part of the journey. These are not unexpected events in terms of training workloads being churned. So, the increase in the sales cycle, I think, like, that is something that we probably didn't anticipate. But I think over a period of time, that increased sales cycle gets mitigated by doing more effort on the sales, by expanding in parallel the number of conversations that you are having.

Moderator: Thank you. The next question is from the line of Amey from Ambit Capital. Please go ahead.

Amey:

Two questions quickly here. You spoke about workloads; they are more on the training side. I want to understand, are the workloads lower on the inference side due to A, the demand from the customers, or is it because of the capabilities of E2E? That's the first question. And the second question is, obviously we will try to increase your data center capacity with a lot of colo (colocation) capacity, set to hit in the next 2-3 years. Would you prefer utilizing a colo (co-location)

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capacity in the future, keeping the asset light model, or would you prefer building out your own data center?

Tarun Dua:

Amey:

Tarun Dua:

Moderator:

Parth Poddar:

Tarun Dua:

So, we obviously have always preferred not to build physical data centers. So, we will continue to rely on co-location. That is one. With regard to inference and training, I don't think like, see, we are like fully capable player in terms of like anyone who has tested our platform to run like inference, like they obviously have the ability to run inference on us . Inference by its very nature, like the, it grows slowly as the adoption of AI grows in terms of like the adoption by the end customers of enterprises. So, the initial volume required by inferencing is always small. And then that scales up over a period of time. And also, inferencing, obviously, it starts much smaller than typical training workloads. But over a period of time, obviously, we expect that there would be a normalization between training and inference to be somewhere between 40-60, 60 in favor of trading or 50-50.

So, just so that I get this right. My understanding is your training happens during your development phases and inferences where you actually...

It's a continuous process. Although training teams could take a break, they'll just say that, okay, sometimes they would be running multiple trainings in parallel. So, trainings do tend to get downscaled for some period. So, for example, typically if you look at the December period, when a lot of training teams would also be taking some vacation. So, you would probably not start like, I am just guessing this is just all a conjecture. So, like the training teams could decide that, okay, it's like the year end. So, let's end the trainings that we were previously running and come back next year and then redo those trainings. There could be of course other products related reasons. I am just making a conjecture over here. So, ultimately, like Trainings would not be like a 24x7 activity or 365-day activity. It would be more like maybe like nine months out of 12 months kind of an activity. But yes, training during development doesn't really go away because you're always building newer features for your customers or improving whatever you're doing today.

Thank you. The next question is from the line of Parth Poddar from Vmpl. Please go ahead.

I have a small query. Sir if we see the revenue part from last year, I mean last year and last quarter, the depreciation part and the other income part is very high. In revenue, it's very high. And again, the expenses are very high. We compare with the last year. So, I am not able to digest. I mean, are we having plans for the revenues so that we can manage the current, like, last quarter the share price reached to Rs. 5,000. Currently, it is trading at Rs. 3,500. So, are we comfortable or are we confident enough that we will reach that price again?

No comments, we never comment on the share price.

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Parth Poddar:

Okay, sir, but it's still the revenue part. We can leave the share price aside. The revenue that I mean...

Tarun Dua:

We kind of like don't predict the revenue, we react to the revenue. So, in the sense that we are obviously trying to build like a lot of scale of infrastructure and capabilities. And over the medium term, long term, obviously, AI is a very large market with a large number of players. And I think that the market will continue to grow. And medium to long term, obviously, we see that will continue to grow.

Moderator: Thank you. The next question is from the line of Sumeet Jaiswal, an individual investor. Please go ahead.

Sumeet Jaiswal: Recently, I just went through your PPT and I have been following the IndiaAI Mission to procure the 10,000 GPUs. So, my question is how you are looking at the progress going ahead and not about this year, I mean the next 2, 3, 4 years that the government is trying to make democratize the GPO and AI tools of masses?

Tarun Dua: You see, that's a medium-to-long outlook. Obviously, that outlook is very bright. So, there is obviously a government focus on expanding the role of AI in the Indian economy. And as a country, we should not be left behind. And I think we are seeing that in enterprises as well, where everyone is trying to figure out how to. And there are implementations of AI that a lot of enterprises are already working on. So, definitely that's the overall outlook. So, IndiaAI Mission, obviously, it is a net positive for our entire AI industry in India, where it would help in terms of expanding the overall market regardless of whoever becomes the biggest beneficiary of IndiaAI Mission. Regardless of that, the market would certainly expand because of the existence of AI Mission and the budgetary support from the government over there. And overall, we continue to maintain a very positive outlook for compute infrastructure and AI services in India.

Moderator: Thank you. The next question is from the line of Ashwin Kedia from Alchemy Capital Management. Please go ahead.

Ashwin Kedia:

Yes, I am curious, what is your planned CAPEX for the next quarter or the next two quarters and have you all placed an order for the Blackwell GPUs from NVIDIA yet? Or what's the strategy with acquiring those GPUs?

Tarun Dua:

So, obviously the plan is to begin with an immediate number of Blackwell GPUs. Currently, we have not placed the orders for Blackwell GPUs, although we have a lot of conversations going on for acquiring the Blackwell GPUs. And as we plan the initial capacity, it will obviously keep all the stakeholders informed about that. And yes, absolutely, we do intend to build a significant amount of capacity on the Blackwell range as and when it becomes available.

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Ashwin Kedia: Is there any planned CAPEX before that outside of the Blackwell range on the Hopper range in the meantime? Tarun Dua: There could be reactive CAPEX in the Hopper range. It would depend on the outlook that is coming from the sales conversations that we are having. Moderator: Thank you. The next question is from the line of Hardik Satiya, an individual investor. Please go ahead. Hardik Satiya: So, on the IndiaAI side, there was a technical requirement of having 1000 GPUs made available within 6 months of timeframe? Tarun Dua: We already have more than 1000 GPUs. So, that is the requirement we practically made out of the box. Hardik Satiya: Should we assume that that is already part of your ecosystem and that is being consumed by others right now and whenever you get this tender through or so you will have it immediately ready or again? Tarun Dua: We have enough flexibility to be able to kind of like significantly expand our capacity as and when needed. Hardik Satiya: So, it can be procured in a short notice with all the...? Tarun Dua: Yes, absolutely. Hardik Satiya: And the second question on the Chennai data center. Any particular reason why we are having the second location, given that our current capacity is not fully? Tarun Dua: We have one location, one major location in Delhi NCR, or rather two major locations in Delhi NCR, which are kind of like joined together with a big link. And we have a smaller location in Mumbai. Now, Chennai, of course, is kind of like, gives us access to a very different seismic zone, very different type of connectivity compared to what Mumbai and Delhi have. So, the landing stations in the south would be significantly different from the landing stations you get in Delhi and Mumbai. So, in that way, like in all respects, Chennai or Bangalore was a good choice to have for the secondary or rather the second location, not a secondary location. Both of these locations would be for the primary workload of every possible variety. Moderator: Thank you sir. Ladies and gentlemen, due to time constraint, this was the last question for today's conference call. I now hand the conference over to the management for the closing comments.

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Tarun Dua:

Moderator:

E2E Networks Limited January 22, 2025

Yes, thank you everyone for listening to our conference call and thank you for your questions. We continue to look forward to working with all of you over the long term. Thank you everyone.

On behalf of Go India Advisors, that concludes this conference. Thank you for joining us and you may now disconnect your lines.

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