It's great to be here to kick off day one at Computex and it's great to be back here in Taiwan. You know, the first time I came here was nearly 30 years ago. It was my first business trip to Asia and I remember back then visiting some of the key technology companies here at the time. Many of them were still young small companies, emerging companies and today those same companies have become the most important technology leaders in the world. Now I've had the opportunity to come back many times and see Taiwan continue to grow in importance as one of the world's leading technology centers. And today so much of the future of AI infrastructure is being built right here.
I have a question for all of you. What defines the performance of AI infrastructure? Now maybe you're thinking about the processor, the GPU, the XPU, or maybe it's the process node used to build it. 3 nanometer, 2 nanometer, or soon A14, A16. Those are great metrics. They tell you a lot about the speed, the efficiency, and the density of the compute. And AI workloads are certainly compute intensive, but that's not the whole story. Now, you might say, well, what about memory? AI workloads are incredibly memory intensive as well. More memory, higher bandwidth, all of that matters. It's all critical, no doubt.
But that's still not the defining characteristic of the system. Because one processor, no matter how fast it is, no matter how much memory it has attached to it, is simply not enough for today's AI workloads. You need tens of thousands and eventually millions of processors working together as a single massive compute engine. That's why computing at this scale is fundamentally a connectivity challenge.
And increasingly it is the architecture and characteristics of connectivity that defines the performance of the system. Now look, we've seen incredible breakthroughs in accelerated computing and we've seen the emergence of high bandwidth memory to meet the AI challenge. But I'm here to tell you the next major wave of innovation and scale will come from the underlying connectivity of these systems. And as those connections move from copper to optical, they will unlock new architectural possibilities. So today, I'm going to explain why connectivity is becoming one of the defining characteristics and challenges of the AI era and why this technology transition matters to optics. Now, this isn't something far out in the future. It's happening right now, this year, next year. We're in the ramp. And at Marvell, we've been preparing for this moment for nearly a decade. We built the company very deliberately around the infrastructure required to move data at massive scale. And to understand why we made that bet, let's go back in time 10 years ago when I joined Marvell as a CEO.
So prior to Marvell, I spent 22 years at one company, Maxim Integrated Products, which was a leading analog semiconductor company. And one of the unique things about working at an analog company is that your products go into virtually every piece of end equipment, every electronic system, every end market on the planet. So over those two decades, I had a front row seat to just about every major technology trend. First personal computing, then notebooks, digital still cameras, smartphones, eventually data center. And I watched wave after wave of technology reshape the whole industry.
So I joined Marvell and I didn't start off actually thinking about well what products do we have. I reflected on where the industry was headed and it seemed clear to me even at that time back in 2016 that the next major growth cycle for semiconductors in the world really was going to be driven by the data platform companies. Back then it was still the same ones as today. Companies like Google, Amazon, Microsoft, Meta and more specifically the semiconductor technologies that were required for those markets to move data, store data, process data and secure data, do it at massive scale. That was the vision we had.
But when I looked at the products we had at that time, very few of these were actually exposed to that trend. It was kind of a problem. Less than 10% of our revenue 10 years ago was coming from data center. That's it. A couple hundred million bucks. But more than 60% of our revenue back then was coming from consumer. And so it was exciting time. We were in virtual reality headsets. We were in gaming consoles, streaming devices, wearables. In fact, our claim to fame back then was Marvell was designed into the first Wi-Fi connected Barbie Dreamhouse. That was our big design win. It was real. In fact, the first week I was at Marvell, the team briefed me on what a great design win this was. So that's where we were. So we had a vision. There was a pretty big gap though between the reality that we were facing and where we saw the industry heading. But we had conviction. We had conviction. So we decided to bet the whole future of Marvell on it.
So to do that, we needed a clear vision. And our vision at that time was pretty simple. And by the way, this is still the same vision that we have today, 10 years later, which is build a best-in-class pure play company focused on semiconductor solutions for data infrastructure. Now, at that time, data infrastructure was not a recognized market category. It was the term that we used to describe the infrastructure that was going to be required to move the world's data, store the world's data, process the world's data, and secure it. But like I said, we were not in that business yet. And frankly, we didn't even have a lot to work with as we went after it. We had some. So my team and I came to a conclusion which is that we would need to build these capabilities internally and others we would need to build through strategic M&A and we had to get focused because when you're transforming it's not just deciding about what you're going to do. It's equally important to decide what you are not going to do.
So with that strategy in place, we got to work. We began systematically building Marvell around that vision. And it wasn't just one move. There was a series of deliberate choices. We looked for the premium assets in the markets that mattered the most. The best companies, best technologies, the best teams with the strongest market positions. Now, we first started by divesting businesses that weren't aligned with our strategy. You can see some of those there. Then very quickly we acquired Cavium to strengthen our compute and networking capabilities. That was back in 2018. 2019 we divested our Wi-Fi business. Again we were focusing but we acquired Avera to establish our custom silicon business and then Aquantia to bolster our connectivity portfolio. In 2021 we followed all that up by acquiring Inphi for $10 billion. It was our largest acquisition to date and we got world-class data center connectivity technology into the company through that and we acquired Innovium the same year adding high-end data center switching capability to the portfolio. So then we took a break, we took a few years to digest and focused on unifying and building out our whole technology platform to address the data infrastructure opportunity.
But over the last 12 months we fired up the M&A engine again. We divested our automotive Ethernet business again power of focus and acquired Celestial AI for its photonic fabric technology and XCON for scale-up switching. So if you add it all up over the last decade we've invested roughly 22.5 billion through acquisitions. We spent $18 billion organically inside of Marvell to develop the platform and then we divested approximately $4.5 billion worth of assets. So all in we've invested roughly $36 billion investing in this platform.
Let me show you the result of some of these investments. First of all, we have built an incredible technology platform and it all starts with the advanced process node. It's one of the most important decisions we made actually was to become a process node leader. Now, Marvell, Cavium, and some of the companies we acquired had all been fast followers, meaning you're like a node or two behind on everything you do. And that's largely a result of just not having enough scale. That's usually why people do that. But as we integrated these businesses, we made the decision that if we're going to compete in data infrastructure, we had to be at the absolute leading edge. No choice. Now, here's a little known fact. Marvell skipped seven nanometer completely. We made a full node jump at that time from 14 and 16 nanometer all the way to five. I mean, nobody does this. Nobody takes that kind of a risk or a bet. But we did and it worked. It worked really well, flawlessly. Actually, our engineering team did an outstanding job executing this transformation. So in early 2020 we released our first world-class IP platform complete with die-to-die interfaces, custom SRAM, high-speed SerDes and more. Now SerDes is a good example of how we built this platform. It combined Marvell's own core engineering strength with exceptional talent from Avera, Aquantia, Inphi and others. Now today that is a 1,500 person organization at Marvell, second to none in terms of engineering scale and capability.
So to support the process data portion of our mission, we built a best-in-class custom compute platform working in deep partnerships with the world's leading hyperscalers and that business has been doing very very well for us. In store data, we built a whole portfolio of storage controllers, CXL-based memory poolers, and near-memory compute. But here's where we really went all-in, and that was in data movement. And this is where our high-speed connectivity portfolio. And when you look at Marvell's data center business today, the vast majority of our revenue actually comes from connectivity. From high-speed optical interconnect inside the data center to long reach optics between data centers to high-speed switching infrastructure. So today we are the undisputed connectivity leader and when you step back and look at what we built and where the market ultimately went I think the results speak for themselves.
So back in 2016 Marvell was a $2.3 billion company. As we embarked on the transformation, actually in the first five years, we doubled the company, $4.5 billion dollars in revenue. Over the next five years, our growth accelerated and according to consensus estimates on Wall Street for the current year we're in, we're set to grow about two and a half times over the last 5 years to 11.4 billion. But in the recent couple of years, if you actually drill down, Marvell has been growing like 40% a year. So the growth rate is actually accelerating in the last few years. So at this point, Marvell is off to the races. Okay. And based on the outlook that we shared in our earnings call last week, consensus estimates have come up and they expect us now to deliver 16.4 billion in revenue next year.
So as I said earlier when we started this journey, data center represented less than 10% of our revenue and we bet the farm on it. Last quarter it was over 75% of our revenue and growing very rapidly. This is a very different company than we used to be and the thesis has largely played out but we're still in the early innings of this infrastructure buildout. The next phase is all in front of us. We'll have a different set of requirements and that brings us back to connectivity.
So for the past several years as AI has created new demands on the infrastructure, we've seen the industry solve one major bottleneck after another. And first it was compute. The industry needed dramatically more compute to enable modern AI and Nvidia did an incredible job leading that revolution and along the way became the world's first $5 trillion market cap company. Congratulations to Jensen and his whole team that's here. It was just a phenomenal phenomenal result.
Next came the memory bottleneck. Larger models required enormous amounts of memory and bandwidth and the memory companies are scaling aggressively now to meet that demand. And just recently, we've seen three new $1 trillion market cap companies emerge in that market. But the bottleneck is shifting again. Now it's connectivity that will define the limits of the infrastructure. Just like with compute and memory, the industry will rally to meet this challenge.
Now, this isn't just me saying this. This is what we're hearing from our largest customers. The world's largest hyperscalers are now reimagining their entire network architectures. They recognize that scaling AI infrastructure is now first and foremost a connectivity challenge. As reasoning models, mixture of experts architectures, agentic AI, it all continues to evolve. More data has to move across the infrastructure demanding higher bandwidth and lower latency. And as workloads no longer fit within one data center, guess what? They need to build larger data centers or full campuses full of data centers and all the high-speed connectivity between them. Thus, the connectivity becomes a critical enabler of scaling compute. And increasingly, our customers recognize that optics is the way forward and they're looking to leaders like Marvell to help them build larger, faster networks and at scale.
So, when you look across the semiconductor industry at the leading companies supporting this infrastructure buildout, it becomes clear each of us is focused on a different part of the infrastructure. That shows up in the revenue mix. Some of the companies are compute first, means the vast majority of their revenue is tied to compute with some of it tied to connectivity but most of it's compute and it's obviously a critical part of the stack and that's why we have several trillion dollar plus companies in this group. Then you have the companies focused on memory and again all trillion dollar market cap companies at this point, it's unbelievable. And then you have Marvell. We're different. We're unique. Today the vast majority of our revenue actually comes from connectivity. So we built this company around data movement and today the vast majority of our revenue comes actually from connectivity. Now this spans a broad range of technologies and even the portion of our revenue that's from compute which you can see is fundamentally because customers embed our connectivity in their compute engines. So this gives us a unique position and perspective on these technology transitions that are happening and it creates a very different relationship that we can have with the rest of the ecosystem. We partner deeply with the compute companies. We partner deeply with the memory companies. These are very strategic relationships and in many ways we are the Switzerland of the industry and we work with everybody.
Now, one of the best examples of the role that Marvell plays in this ecosystem is the recently announced strategic partnership and expansion with Nvidia. And as part of this announcement that we made a few months back, Nvidia invested $2 billion into Marvell. And we're expanding our partnership now across multiple dimensions including optics, photonics, NVLink Fusion. And I'm thrilled to announce that Jensen himself is here today. He's going to join me on stage. We're going to spend a few minutes chatting about the partnership and we're going to see where AI infrastructure goes from here. So with that, let me please welcome to the stage Jensen Huang.
What's up, Jensen? How you doing?
