Yes, I feel over the past three or 4 years, there’ve been a variety of initiatives. Intel’s played an enormous a part of this as well of re-imagining how servers are engineered into modular components. And really modularity for servers is just exactly because it sounds. We break different subsystems of the server down into some standard constructing blocks, define some interfaces between those standard constructing blocks in order that they’ll work together. And that has a variety of benefits. Primary, from a sustainability perspective, it lowers the embodied carbon of those hardware components. A few of these hardware components are quite complex and really energy intensive to fabricate. So imagine a 30 layer circuit board, for instance, is a fairly carbon intensive piece of hardware. I don’t desire all the system, if only a small a part of it needs that sort of complexity. I can just pay the worth of the complexity where I want it.

And by being intelligent about how we break up the design in numerous pieces, we bring that embodied carbon footprint down. The reuse of pieces also becomes possible. So once we upgrade a system, perhaps to a latest telemetry approach or a latest security technology, there’s only a small circuit board that has to get replaced versus replacing the entire system. Or perhaps a latest microprocessor comes out and the processor module might be replaced without investing in latest power supplies, latest chassis, latest every thing. And in order that circularity and reuse becomes a big opportunity. And in order that embodied carbon aspect, which is about 10% of carbon footprint in these data centers might be significantly improved. And one other advantage of the modularity, except for the sustainability, is it just brings R&D investment down. So if I will develop 100 different sorts of servers, if I can construct those servers based on the exact same constructing blocks just configured in another way, I will have to speculate less money, less time. And that may be a real driver of the move towards modularity as well.

So what are a few of those techniques and technologies like liquid cooling and ultrahigh dense compute that enormous enterprises can use to compute more efficiently? And what are their effects on water consumption, energy use, and overall performance as you were outlining earlier as well?

Yeah, those are two I feel very essential opportunities. And let’s just take them one at a  time. Emerging AI world, I feel liquid cooling might be some of the essential low hanging fruit opportunities. So in an air cooled data center, an amazing amount of energy goes into fans and chillers and evaporative cooling systems. And that is definitely a big part. So in the event you move an information center to a completely liquid cooled solution, that is a chance of around 30% of energy consumption, which is type of a wow number. I feel persons are often surprised just how much energy is burned. And in the event you walk into an information center, you almost need ear protection since it’s so loud and the warmer the components get, the upper the fan speeds get, and the more energy is being burned within the cooling side and liquid cooling takes a number of that off the table.

What offsets that’s liquid cooling is a bit complex. Not everyone seems to be fully in a position to put it to use. There’s more upfront costs, but actually it saves money in the long term. So the overall cost of ownership with liquid cooling may be very favorable, and as we’re engineering latest data centers from the bottom up. Liquid cooling is a extremely exciting opportunity and I feel the faster we will move to liquid cooling, the more energy that we will save. But it surely’s a sophisticated world on the market. There’s a number of different situations, a number of different infrastructures to design around. So we shouldn’t trivialize how hard that’s for a person enterprise. One among the opposite advantages of liquid cooling is we get out of the business of evaporating water for cooling. A number of North America data centers are in arid regions and use large quantities of water for evaporative cooling.

That is sweet from an energy consumption perspective, however the water consumption might be really unusual. I’ve seen numbers getting near a trillion gallons of water per 12 months in North America data centers alone. After which in humid climates like in Southeast Asia or eastern China for instance, that evaporative cooling capability shouldn’t be as effective and so way more energy is burned. And so in the event you really need to get to essentially aggressive energy efficiency numbers, you simply cannot do it with evaporative cooling in those humid climates. And so those geographies are sort of the tip of the spear for moving into liquid cooling.

The opposite opportunity you mentioned was density and bringing higher and better density of computing has been the trend for many years. That’s effectively what Moore’s Law has been pushing us forward. And I feel it’s just essential to comprehend that is not done yet. As much as we take into consideration racks of GPUs and accelerators, we will still significantly improve energy consumption with higher and better density traditional servers that enables us to pack what might’ve been a complete row of racks right into a single rack of computing in the longer term. And people are substantial savings. And at Intel, we have announced now we have an upcoming processor that has 288 CPU cores and 288 cores in a single package enables us to construct racks with as many as 11,000 CPU cores. So the energy savings there may be substantial, not simply because those chips are very, very efficient, but because the quantity of networking equipment and ancillary things around those systems is so much less since you’re using those resources more efficiently with those very high dense components. So continuing, if maybe even accelerating our path to this ultra-high dense sort of computing goes to assist us get to the energy savings we want perhaps to accommodate a few of those larger models which are coming.

Yeah, that definitely is smart. And that is a superb segue into this other a part of it, which is how data centers and hardware as well software can collaborate to create greater energy efficient technology without compromising function. So how can enterprises spend money on more energy efficient hardware comparable to hardware-aware software, and as you were mentioning earlier, large language models or LLMs with smaller downsized infrastructure but still reap the advantages of AI?

I feel there are a number of opportunities, and perhaps probably the most exciting one which I see straight away is that whilst we’re pretty wowed and blown away by what these really large models are in a position to do, although they require tens of megawatts of super compute power to do, you possibly can actually get a number of those advantages with far smaller models so long as you are content to operate them inside some specific knowledge domain. So we have often referred to those as expert models. So take for instance an open source model just like the Llama 2 that Meta produced. So there’s like a 7 billion parameter version of that model. There’s also, I feel, a 13 and 70 billion parameter versions of that model in comparison with a GPT-4, perhaps something like a trillion element model. So it is, far, far smaller, but whenever you nice tune that model with data to a particular use case, so in the event you’re an enterprise, you are probably working on something fairly narrow and specific that you just’re attempting to do.