Intel on Right-Sizing Enterprise AI Infrastructure for the Agentic Era

Enterprise AI budgets are colliding with a new kind of math. Token costs keep climbing even as unit prices fall, agentic workloads are multiplying, and boards want proof of ROI before they approve the next round of infrastructure spend. Uber's CTO recently confirmed blowing past an annual token budget in a matter of months, a preview of what most enterprises are about to face at scale.

At the Six Five Summit: AI Unleashed 2026, Ryan Shrout and Matt Kimball were joined by Anil Nanduri, Vice President of AI Products and GTM for Intel's AI Data Center, for an AI Infrastructure Spotlight interview on what it takes to move enterprise AI from pilot to production. Nanduri's starting point breaks with how most infrastructure conversations go. Enterprises default to hardware first. He pushes them to start by identifying where AI creates business value and letting that drive the budget. From there, he argues that not every token carries the same cost or urgency. Fraud detection needs ultra-premium, low-latency tokens generated in near real time. A batch audit report due in twenty days does not.

Agentic AI raises the stakes further. Nanduri breaks enterprise AI infrastructure into four planes: a control plane that orchestrates and secures agents, an agent plane where tokens get generated and tasks get executed and verified, a network plane moving data and models across racks and data centers, and a storage plane handling databases, caches, and context memory. His point is that wall clock time across all four planes is what enterprises should be measuring, since token generation speed alone misses where workflows actually stall. He points to Uber's own token budget overrun as proof. Uber tightened agent loop efficiency, gave engineers visibility into their own token spend, and mixed frontier and open-rate models by workload, since roughly 80 percent of tasks can run on cheaper tokens.

Looking further out, Nanduri frames the next inflection point around enterprise data rather than compute, since most public training data is already exhausted. He splits enterprises into two camps: those handing broad data access to frontier models, and those building hybrid architectures that bring AI to their own data instead of sending it out. He also details how Intel is building for that shift, from a heterogeneous computing approach scaled from the PC to the data center, to Crescent Island's low-power memory for cost-efficient inference, to a SambaNova partnership for high-throughput token generation, all tied together by Xeon-based infrastructure aimed at cutting wall clock time.

Key Insights:

🔹 Not all tokens are created equal. Nanduri argues enterprises need ultra-premium, low-latency tokens for real-time use cases like fraud detection, and can run batch workloads on cheaper, slower token generation. Treating all tokens the same wastes budget.

🔹 Wall clock time across the control, execution, network, and storage planes is what Nanduri says enterprises need to track, since token generation speed alone misses where AI workflows actually stall.

🔹 Uber's token overrun is a preview, not an outlier. Nanduri points to Uber blowing past its annual token budget in months, then cutting costs through agent loop efficiency, engineer-level cost visibility, and mixing open and frontier models by workload.

🔹 The next AI advantage sits in enterprise data, not public data. With most public training data already exhausted, Nanduri says the enterprises that build hybrid architectures to bring AI to their data, rather than exporting their data out to frontier models, will hold the edge.

🔹 Intel is building for cost efficiency over raw latency. From Crescent Island's low-power memory to its SambaNova partnership and Xeon-based orchestration, Nanduri frames Intel's approach around wall clock time and ROI rather than benchmark scores.

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Ryan Shrout:
Hey everybody, welcome to the Six Five Summit 2026 AI Unleashed. We're continuing the conversation with another AI infrastructure spotlight. I'm Ryan Shrout, joined by my colleague, Matt Kimball. Today we're gonna talk about a challenge nearly every enterprise is wrestling with today. How do you build AI infrastructure? But how do you build it fast enough How do you build it flexible enough, cost-effective to move beyond pilots and into real production, which seems to be a topic we touch on quite a bit these days. Joining us for this conversation is good buddy of mine, Anil Nanduri, Vice President of AI Products and Go-To-Market for Intel's Data Center. Welcome back to The Six Five, Anil.

Anil Nanduri: 

Hey Ryan, how you been? It's been a while.

Ryan Shrout: 

It's been good. It's been good. I'm looking forward to this conversation. I know you guys have some exciting stuff happening over there.

Anil Nanduri: 

No, thank you. And great to have me on your show and really looking forward to this conversation.

Matt Kimball: 

Yeah. Hey Anil, let's jump right in. Okay. So here's the setup. You talk to enterprise IT leaders all the time, the excitement you can't get around it. And it's really the topic of every conversation. But as they get excited about kind of the pilots that they're driving, what we hear is rising token costs. You know, you hear unit costs coming down, but really in volume it's increasing. New agentic workloads, how do you manage these? And like this growing pressure to manage ROI. You talk to more customers than we do, obviously. How do you think they should be thinking about these challenges as we move into kind of, you know, enterprise production?

Anil Nanduri: 

That's a great question, Matt. And I think the fundamental thing is a lot of our customers, they start thinking about hardware first. I think we really have to change the conversation to ROI. Why is that? Because it's all going to be around how AI can solve a problem. And from that perspective, you've got to really deconstruct how you're going to apply AI to what frame of the business and where you're going to drive the value, right? And based on that, you're really going to get a framing of what kind of budgets you're dealing with, what kind of cost economics you're dealing with, and what kind of productivity improvement you want to be aspiring for. Basic first principles of how you want to run through an operational aspect of it. Now, interestingly enough, because AI is coming at such a rapid pace, you want to have that innovation engine going, which means that you want to do a lot of pilots and that's fine. You sandbox them, create your pilots. But as you think about getting into production, you want to be a lot more, you know, clear about how you want to deploy it, right? And then so, you know, there's a lot of things, like you said, is happening. It's no longer about token maxing, which is not like the best, highest number of tokens will give you the best ROI. It's actually the most efficient use of tokens, you know, that get you the ROI. And I think, I do want the audience to kind of have a framing that not all tokens are the same, and then elaborate over the next half an hour what that really means. But not all the tokens are the same, which means that you're going to have ultra premium tokens, which is super low latency, super fast, very, very good for interactive. I mean, if you're trying to do like some credit card fraud protection. You want to have almost real-time AI. You want that fast, right? On the other hand, you're doing some auditory work and you need to present an audit report in 20 days. You don't need that super fast AI. You can handle it very differently in a batch mode. So those ROI conversations start to play into how you think about putting AI into production.

Ryan Shrout: 

You know, one of the things that we hear about, we're talking about all through this, the Six Five Summit, right, is agentic AI. We kind of went through this whole, you know, early LLM to reasoning LLMs to now agentic AI. And it's kind of the biggest conversation that we have. And it kind of dives into your point about not all tokens being equal. But I am curious from an infrastructure perspective, what fundamentally changes when these AI systems begin planning and coordinating and taking actions instead of simply generating a response?

Anil Nanduri: 

I think, Ryan, another great question, right? What do agents do? They kind of automate the work that we were trying to do. And as more agents come in, you really need to have the the loop or the agent loop to control how your agents operate. And so if you kind of step back and look at how these AI systems work, we spend a lot of time trying to figure out where to run the AI. And that AI is generating tokens, which is how you consume it. And token generation, as I said earlier, not one size fits all. And so you can generate tokens with small models and run it slow, but can very efficiently, even on a CPU, they are capable of running some level of tokens. But as you know, GPUs start to kind of go up the stack. Now, GPUs are great for the flexibility they offer, some density of tokens you can run, but if you're really wanting to get a large throughput and you want to really scale it, even the GPUs hit a Pareto limit. And that's why you have solutions like Dataflow architectures, like an RDU, or a Grok-like solution with LPUs. They're starting to kick and take the low latency, high interactivity, large volume of tokens that you need to generate, right? And so token generation itself is a lot of optimization you can do. And then we can talk about that and how you bring that into the business economics. The second part of it is, what do I do with these tokens? And these tokens are actually, especially in the agentic world, you're creating outcomes. And say I'm creating an application, or I'm trying to improve and refactor an application. And as you're thinking about that, agents are helping you create code. You're going to test that code. you need to verify, test, execute that code. And that's where CPUs start to play a lot of role, because CPUs are your execution engine, verification engine, and your sandboxing engine. And so if you kind of see recently a lot of why are CPUs in a shortage, it's because a lot of these back and forth ping pong is happening in the system, right? But you went a step ahead. When I think about as agents come in, Now you need to figure out who's orchestrating it, who's the quarterback. And the quarterback function is becoming more and more important. It's about what we use the term harness, which is basically saying, how am I going to run what workload, where am I going to run it, and who's going to run it, right? And so here you're looking at saying, what kind of agents do I need? What kind of agent loops do I need to orchestrate? What kind of security do I need? Do I need to run the model that is sitting on prem and where my data is, or do I want it to be running in the cloud and move my data over? These are all going to be very, very important CIO decisions, and the enterprise have to really look at, am I taking my data to the AI or am I bringing AI to the data? That's one way to think about it. But then as you execute this, you start to see different problems in the data center system. Your stress points are changing. Your stress point moved from being about generating tokens, which by the way is a key function and you're spending a lot of cost on it, to the running of the full end-to-end workflow where the wall clock time matters. And so these tokens, based on the systems you're running, you're stressing based on the context lengths, you're stressing memory. you're trusting storage, you're hitting databases, and now you're waiting on functions where if you are, a good example is an enterprise and you're trying to look at your supply chain procurement to a customer demand and your financial tools to make sure when you can say invest. And say you have a huge amount of demand and you want AI to help you manage this, you've got to have your financial system is in one place, your customer relations and customer data is in one place, and your supply chain is in one place. And so you will now have to have APIs. And the agents are really good at getting data between all these systems. And then you're not going to move all that data over, but you're going to have these agents interact and get the information from those systems. And so the wall clock time has shifted from just the AI running to actually waiting on answers. And so you really have to look at an end-to-end system throughput. And I like to use the word wall clock time, measure the wall clock time, not just how one independent piece of the system performs. And we are at a very interesting point in the market now. Memory costs have gone up so significantly that in the past, you didn't worry too much about it. You would say, hey, throw more storage, throw more memory at it. And we were just running fast. Now you have to step back and say, okay, am I getting the best efficiency out of what I'm investing in? Do I have all the right set of pieces so that I'm taking the most expensive part, that AI engine and the AI intelligence, and I've got everything else working, humming with it in the right efficiency so I could get the best ROI. So the agentic infrastructure actually stresses this at a much more complex levels than before.

Matt Kimball: 

So that's interesting you say that because it's…enterprises are spending lots of money on AI infrastructure, tens of millions of dollars. And it seems like there's a couple of things. The industry is saying, here's a benchmark score for you that shows you how great we are. And we're going to throw some gaudy numbers around tokens per second to really wow you as enterprise CIO and convince you that we have the best solution out there. And we all know in the enterprise, you know, there's this intrinsic understanding that AI is going to be good for business, right? But the board doesn't care about that. I'm giving you $10 million to go stand something up. I want to see some measurement that shows or some way of showing that that infrastructure is creating business value. You know, have you kind of thought this through and like, have you had this conversation with IT leaders and kind of how they do that?

Anil Nanduri: 

Very much so. And I think one of the more public examples lately, I think the Uber CTO had stated how they ran past and blew past their token budget in a few months, their annual budget that they had looked at, right? And then this week, in fact, yesterday or the day before yesterday, they had their earnings call and their CFO and the CTO shared on how they've changed in a matter of months. And then what they did was they said, hey, we've started to bring down our cost of AI implementation while increasing the amount of AI that they're implementing. So how do they do that? So first, what they said is that they optimized and made the AI system of operation more efficient. What does that mean? What they did was that, you know, if you kind of look at it saying, how do tokens get generated? If you have a very deep agent loop, and if you have a very inefficient agent loop, or if you have a very inefficient prompting and, you know, you know, context caching mechanisms, you are going to create more tokens. Okay. As we move from, you know, a person in control to machine to machine, you really need to have the tools in place to make that more efficient. And so the algorithms at the agentic architecture level itself, you can actually optimize, which means that by using the right prompt and the right context and the least amount of tokens, can I get the same answer. And that's more often thinking of the problem of not throwing more tokens at the problem, but think of it as an engineering efficiency solution problem, right? So they did that first. The second one they did was actually more interesting. They gave visibility to the engineers themselves and the employees, the use of the AI. So they gave visibility in how much tokens are you consuming? How much budget have you consumed? And so observability into the system is actually very important as an ROI measurement. So once you know where the problem is, you can go attack the problem, right? And so the first step is to understanding where in that agent loop, where in the AI loop are you actually being most expensive, and then you can go look at it and tackle it. And the third thing, which is actually going to get more and more interesting, is the change in the landscape around open rates, open models, and the closed models. Frontier models are great, but they're expensive. But there's a lot of innovation going on where open rate models are catching up. And so my going back to my earlier statement, not all tokens are same. You can actually now start to control what part of your workflow needs what type of a model. In fact, what type of a token and what type of an agent. And that, again, is applying some human creativity into solving that workflow, right? Because you can come back and say, 80% of my work can be done with the basic open way tokens, and I need, say, 20% of it to be that premium intelligence token. So you are getting into, this is no different from what enterprises have solved for when the on-prem and cloud debate went on, right? You would think that, hey, cloud's going to take over everything. And then you're going to say, oh, on-prem is a more cheaper way to do it. And what really transpired is a very, very hybrid infrastructure. And I see that same thing happening. And why do I believe in it? Because if you think of like the Hermes or open cloud moment that happened for consumers, where you're really creating a personal agent, and you can run them on a Mac mini or a workstation desktop and run a bunch of local models. you are going to basically enterprise that at scale and get into a hybrid mode of operation. Now, in that hybrid mode, that AI that you're running, Not all of it need to be on-prem. You can say, you know what? The open rate models are still running on a cheaper cloud. And I have a more expensive cloud. So it's going to manifest itself into that way. And I think it's just a question of time. We are already seeing the ones who are early adopters in this benefiting a lot by trying to get to that kind of a hybrid deployment.

Ryan Shrout: 

I know it will not come as a surprise to you, Anil, but I have a lot of thoughts on how we measure performance, how we benchmark these things, and getting away from things like tokens per second as a metric of value and more on completed correct work per token or per million tokens or something like that, right? We have already been doing a lot of thinking and some development on like, how do you measure success by business outcome, right? Or by some kind of correct result at the end of the day. And it's a combination of, what model do you pick? What infrastructure do you pick? What are the tool sets that those agents may or may not be using? But I'm curious from your view, If the goal is to measure business outcomes rather than individual component performance, which I think we can all agree is where you get your measured ROI, how do you talk to enterprises that are thinking about building out that infrastructure so that they can balance what CPU do I need, what GPU, what accelerators, what storage, what different deployment environments to support those workloads?

Anil Nanduri: 

I think that's a great question and I think Ryan would agree a lot more work has to be done here into kind of providing the benchmarks that kind of reflect this. But at a first principle level, you could actually analytically come to that conclusion, right? And so the most important thing is that if you kind of break down the hierarchy of your infrastructure into a few simple building blocks. I'd call them first is the control plane. There's the application plane, which is where your software runs, right? So that's fine. But there's a control plane. Control plane for me is like the quarterback. This is an orchestrator. Think of it as your agent router, your model router. You can figure out where your security harness is. This is where it's really putting the backbone of your operational controls in place. These typically run on CPUs. And so you want to make sure you have a very solid, you know, control plane thought process into saying that, hey, I got to figure out that I got the right, you know, CPUs in there and most general purpose and most flexible, because that's what you need. And then you really focus on things like having security features and capabilities that get you that. Then there is the agent plane or the data plane where the tokens reside, your task execution, sandboxing, you again have CPUs for the sandboxing and execution and verification, but your token generations. Now in the token generation, like we said, you're going to have to figure out what type of tokens do you need and where do you source it from, on-prem, to CPUs, to GPUs, to RDUs. There, the measurement has to be around your token throughput and your dollars per token, and what type of tokens, like whether it's an open source, open rate model, or you're using a foundational model. Costs vary, right? So you need that configurator to know how much of your budget is going there. And then you need to think about the network plane. Network plane is that what's happening in the ecosystem and I think people don't see this as often is that the models are getting more and more complex and bigger, even in the open rich space, they are crossing boundaries, which means that one ship cannot run a full model. you have to run this across a few chips together, stitched together, and then you're going to have to have data and systems communicate across racks and across servers, I mean, across data centers. And so your network element, both scale up and your scale out starts to play a big role on this outcome measurement, right? Which is your wall clock time, right? Because that's going to be a bottleneck in it. And then the storage side of it, which is like, hey, where are your databases? Where's your KV cache? Where's your context memory pooling going on? Where's all your storage data? And how do you get access to it? And again, they all run, by the way, hosted with a CPU, but very appliance-like solutions out there. And so when you think in these layers, you've got to make sure that your investments are balanced. evenly or to the right mix. Because if you don't have a great orchestrator, but you have a great token efficiency approach, you still are missing out. And if you don't have the right storage to go with it, you're not going to get the right efficiencies. And if you don't have the right network, you're not going to be able to then leverage all of these as they interact. And so that I think is the aha moment right now. And then you can go figure out from the bigger attributes of your budgets, is it better for on-prem, better for hybrid, or better to go to the cloud, right? And so that, I think, is the first principle view of what is the AI agent workload look like when I deploy it, and then how is it computationally going between these phases of the workflow. So you have to think of this as a workload, and we've done these before. We've had HPC workloads, we had media workloads, we had health and life sciences workloads. And we've been able to break that up and understand the computational dependencies and which part of the system you stress. So I think it's just going back to that. And I think it's just that AI moves so fast that people always thought of it, but now they're looking at it. Is there a cheat sheet? Can you give me a configurator? Can you give me something easy for me to understand and deploy?

Matt Kimball: 

It's interesting you say that, Anil, because if you think over time, the last 30 years or so in the data center, there's this constant evolution. You see one inflection point after another, virtualization to cloud, even before that, digital transformation. AI is different, as you said, it moves a lot faster, it's a lot different. So I'm an enterprise CIO, I'm looking, trying to look five years out on the horizon. And there's a point there where my data center, my workloads, my business is AI-ified, if you will, right? There's a lot of different infrastructure that I deploy than what I've been deploying in the past. And there are different strategies. But you talk about first principles. I think that's a good thing. I'm an enterprise CIO. what's the biggest shift I need to make? What's that biggest kind of flip I need to make in my brain from a mindset perspective before I begin that kind of, you know, that mapping out that strategy and what that end point looks like?

Anil Nanduri: Actually, that's a very insightful question. And, you know, I would have a view on this in a very simple way. How does AI get smarter, right? It's a first, again, back to first principles. AI gets smarter by learning from more data. Okay. And you can, and you kind of look at the ecosystem and say, why are all these AI models like now really a lot of, you know, kind of a lot of press and noise and about like, you know, Hey, these models, open source models are catching up to frontier models. There's a reason for it. They've pretty much scrapped all the data that they can learn from. Okay. And so you are getting to a point where now you're trying to, you know, create synthetic data, you're trying to go now create more simulations, and you're trying to get models better. But in terms of crawling for data that exists for you to train these, pretty much you could take a debate, you know, 90% plus of the data is already, you know, being accessed, right? Now, where's the rest of the data? The enterprises have that data. Okay. And so they are at a very critical point on how are you going to now bring AI to your data, okay? And I believe that there's going to be ones who decide saying, you know what, I'm going to take the easy button and just going to go, you know, access and provide access of all my data sets into, you know, these frontier models and other models. And what's really happening is those models are going to get smarter and learn from your data and understand how your business works. then there's going to be the other set of CIOs who are thinking about it and saying, how do I protect my data? And how do I put the right harness and the right hybrid structure where I want the benefits of AI working towards what I need to do? And then not, on the other hand, let that reverse osmosis happen where my knowledge is going back into the AI system, right? And can I do that? Yes, you can. And how do you do that? This is where the open rates model as well as how you create your harnesses start to matter. How do you create your architecture saying, I'm going to sandbox certain capabilities, but I do want the latest AI tools. So you can fine tune models, you can make them run smaller, because that's the great part. What they've shown us is that I don't need a three children model to be on a very domain-specific export. I can actually distill it. I can fine tune it and make a much smaller model that is much more capable on my dataset than trying to solve everyone else's problems. And so there's going to be that inflection point in the next few years where the ones who are going to win in the enterprises are the ones to very, very strongly protect the data and ensure that their data is, again, and you bring the value of AI into where your data is, not the other way around. So I think that for me is going to be that critical inflection point of figuring out how this AIfication, as you said, into enterprise becomes a business success or they get disrupted.

Ryan Shrout: 

Well, I can tell from this conversation and many others, there's no shortage of big problems for us to solve as we continue to dive into this. So I'm excited to see where it goes. It's the fastest industry that I've ever been a part of. And Anil, I think you would agree with that. Thank you, Anil, for joining us for this AI Infrastructure Spotlight today. Really appreciate you taking the time to be with us.

Anil Nanduri: 

Thank you. And it's been great. Hey, a quick one, Ryan, Matt, do you want to have one extra question saying, hey, how is Intel approaching this? And I can connect some things what we're doing.

Matt Kimball: 

That seems like an obvious question to ask, huh? Sure.

Ryan Shrout: 

Approaching this being what?

Anil Nanduri: 

How are you approaching, you know, how are you looking to cater to this market when you're having these problems? So what is Intel, you know, doing to meet the needs of the enterprises or something.

Ryan Shrout: 

Yeah. Let me, let me, let me throw something out there.

Anil Nanduri: 

I don't know if it makes sense. Yeah.

Ryan Shrout: 

Yeah, no, that's good.

Anil Nanduri: 

Yeah.

Ryan Shrout: 

So Anil, I mean, that's, that's a, that's a lot for us to lean into, but I'm curious, you know, what's Intel's view? Like how does Intel plan to address all of these problems and segments, right? You've got a robust portfolio and one that's growing, but how do you view Intel's kind of place in this?

Anil Nanduri: 

I think great question. I think, you know, let's look at it from a perspective of, you know, where AI started with training and, you know, high-end GPU compute, which, you know, we did not participate in. And so, but as you look forward, a lot of this is about inferencing. And if you think about agents, it's all about inferencing. And we are quickly coming down to the aspect that not all tokens are the same, and inferencing can be done in very different compute domains. And whether you are in a brownfield infrastructure or a greenfield infrastructure, your choices and decision trees change, right? And so how we've taken an approach is, you know, we do a lot of compute. We do this notion of heterogeneous computing because it is a combination of CPU, GPU, different kinds of, you know, compute elements that are going to work together. The most best example of heterogeneous computing has been the PC. It's evolved and you can see how things get better power efficient, more energy efficient, and better performance over time to run all kinds of tasks you want, right? Now, we're taking that first principles from the PC and we're scaling it to data center. So, we kind of bringing up from our series of GPUs to like Crescent Island. And we are doing things differently, knowing the problem is going to be an ROI cost, right? So with Crescent Island, we intentionally chose low power DDR instead of GDDR or HBM memory. And we brought in the solution of bringing it into a much more energy efficient, cost efficient, But it's not going to be the more ultra premium tokens. I mean, it's not low latency, but it has a very good sweet spot where, you know, a lot of enterprise customers can plug in a PCI card and scale their business as they need it. And it's very easy to go kind of install it and deploy. Now, that's on one side. On the other side, we partnered with Samba Nova. Samba Nova, on the other hand, is like this huge pipe of tokens you can generate, right? At low latency, very cost efficient, but it works for, you know, kind of in the, you know, lot of high interactivity, right? And so it has its place. So when somebody wants to get a huge pipe of tokens of a specific model to run, something like a Salmon over really works well. And so we are partnering very closely with them. And then, as I said earlier, a lot of the wall clock time is going to be kind of driven by tasks that sit outside these models, which is going to be access to your verification execution, your orchestration and your storage plane. and your network plane. And so we're doing a lot of work on the CPUs that actually can help provide that efficiency and performance. So you can start to look at a wall clock time problem, right? And so if you think about our Xeon portfolio, plus, you know, looking forward to Crescent Island and our partnership with like Samanoa, we are looking to provide that capability purely from a cost efficiency TCO ROI kind of approach. Now the deployment mechanism could go from on-prem to a Neo cloud to somewhere else, right? And so that's how we are thinking about it. We are super excited to put the building pieces or the building blocks for these and kind of look at it from this notion of outcomes than just look at it from a specific individual piece of computer.

Ryan Shrout: 

Well, there's clearly no shortage of problems and solutions to create from this. And I think Intel has a very interesting portfolio here and one that is growing and expanding. So I'm looking forward to having more of these conversations with you as that kind of progresses. Emil, thanks for joining us for this AI infrastructure spotlight today. It was great to have you.

Anil Nanduri: 

And thank you, Ryan. Thank you, Matt. It's been a pleasure. And enjoy the rest of the event.

Ryan Shrout: 

To our viewers, don't forget to subscribe, follow us on social media, and check out all of our Six Five Summit content at sixfivemedia.com slash summit. See you next time.