Own Your Token Machine

September 15th, 2026 | Jason Lopez | 8:46

In this video interview, Liqid Founder and CTO Sumit Puri argues the path to affordable inference runs through composable AI infrastructure that dynamically taps into pools of GPUs, CPUs and DRAM while optimizing usage to manage costs. He says Liqid allows a single server to access a pool of 30 AMD GPUs to meet performance-hungy AI needs.

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Video transcript:

Sumit Puri, Founder & CTO, Liqid: Since we spoke to you guys last, one of the major trends that we’re seeing is the enterprises are finally past the initial exploration phase. Now they’re in the adoption and deployment phase of their journey of AI. And I think a lot of them are trying to figure out how exactly they are going to do this journey. And the first crossroads that they’re up against is, am I going to take all of my AI capability and move it into the cloud? Or am I going to own the infrastructure on-prem or in a colo that’s required to do AI? And what it all comes down to is tokenomics. At the end of the day, these companies have a limited amount of dollars and a limited amount of power in many cases that they can deploy. And what they’re trying to figure out is how do I get the most amount of tokens out of that very, very scarce resource?

And so those are the discussions that customers are having now. And the cost of these tokens is front in mind for them. If we notice what’s happening in the industry, large organizations are now saying, “Hey, we’re going to limit the amount of tokens that you as an engineer inside of our organization can consume because the cost of that is becoming very high.” And so the one great way to address that increasing cost is to own your own token machine. And so that’s one of the conversations that we are having with our customers is one way to reduce the cost of those tokens is bring the infrastructure, bring the GPUs on-prem or in a colo so it’s a one-time expense and you can consume all the tokens that you want out of that investment that you make. It is about the model that you are looking to deploy and you’re going to, in any environment, you’re going to have a variety of models.

You’re going to have big models, you’re going to have small models, you’re going to have medium sized models. The model actually, the size of the model will dictate the type of infrastructure that you need. If I’m running a very, very large model, I’m going to need a large quantity of GPUs in order to run that model. And so you have to figure out if I’m going to build a very big system, how do I get those large quantity of GPUs into play? One way to do it is independent scaling of resources. We come in and let customers say, “I don’t want to scale my compute as I’m scaling my GPUs. Allow me to just scale GPUs.” As an example, today we’re at the AMD show. The announcement that we’re making today is our ability to take a single server and scale up to 30 AMD GPUs to that single machine.

And the benefit of that is we can now start to run these very large frontier models on a single server infrastructure. These models are so big. Previously you had to run them in a cluster. Now we come in and say we can take those models that were previously clustered and consolidate them down to a single server, a single instance, back to that tokenomic story. That’s how we reduce the cost of deploying these models. Models will change over time. And so some cases you might want a very small model that will require, let’s say, a single GPU. Sometimes you will want a medium sized model that will require eight. And sometimes you will require a massively large frontier model, which will require dozens. You as a customer, it’s impossible for you to know ahead of time what size model I will need when. And so our vision is let’s take the guesswork out of that.

Let’s have a pool of servers, a pool of GPUs, and dynamically spin up GPU quantity to server based upon models. And the reality is the way that’ll be done is something called Kubernetes orchestration will be the way that they do it. Nutanix has a phenomenal way of going off and doing that through their NKP platform. We’re a big believer in NKP. And so we integrate closely with NKP. And so we say, let’s allow people to deploy these models, the variety of models that are supported with the perfect set of infrastructure so we can have this matching of infrastructure to model to not have any wasted resources, improve the tokenomics. In a resource constrained environment, utilization is critical. You cannot afford to have your GPUs at 30, 40% utilization because you can’t get enough. And so if we can implement technologies that drive utilization to 100%, we can do more with the same amount of infrastructure.

So that becomes critically important and we do a lot of that today. We look at an environment and we say, “Hey, listen, this workload is only using those GPUs 30% of the time. Let’s set up policies that allow you to move those GPUs to different parts of the infrastructure to raise the utilization.” The second thing is Brownfield. We support brownfield environments. So imagine an environment where I can’t buy servers because memory’s limited, CPUs are limited, servers are very expensive. We come in with our solution, which is disaggregated pools of GPUs, and we attach them to existing infrastructure. And so now we’re giving new life, new capability to the infrastructure that customers already own. As inference becomes a thing in places like the enterprise, it will only be done in containerized environments. NVIDIA has created something called NIMS, NVIDIA Inference Microservices. AMD has created something called AIMS, AMD Inference Microservices.

And the reason is both of these companies realize the complexity related to kernel revisions, operating system revisions, library revisions is too complex for average enterprise customers to debug and solve, and it slows the rate of AI deployment. So if we can containerize all of these models, put them into a store, allow the enterprise customer to bring whatever model he wants, a vision model, a speech model, a text model, bring it down in whatever size of model that they need, 7B, 70B, 405B, and make all of these available containers that the customer can deploy and implement immediately, that’s how we’re going to accelerate the pace of deploying AI. The way that we approach that is we have a plugin for Kubernetes that when you say, “Hey, give me Llama 7B as an example,” we’ll take the container, we’ll bring it down, we’ll crack open the container, we’ll determine the exact amount of physical resources that that container needs.

Well, this needs two GPUs and we want to put it on server number four. We’ll pause that container, we’ll go into the background infrastructure, we’ll put two GPUs on server number four, and then we’ll take that container and we’ll launch it onto that server. We automate the entire process of deploying models down to a two minute instance. So you say, “Give me Llama 7B.” Two minutes later, you’re speaking to a chatbot with the exact amount of hardware resources required for that instance. And the moment that that server doesn’t need that GPU anymore, we can delete that instance, remove those GPUs, put them back into a free pool so your next model that comes along always has a pool of GPUs to draw from. Liquid started, the way we started was around GPU pooling. That was the first thing that we went off and we accelerated and we composed and we pooled and we shared.

Now the next thing that we have just announced is memory pooling. So we are the first company to provide an end-to-end solution around DRAM pooling built around CXL. So now I can have my pools of GPUs and now I can have my pool of DRAM. The two most expensive things in the data center can now be dynamically allocated by workload. The days of over-provisioning your server with excess memory, those days are done now. And so we must find methods to precisely provision the exact amount of memory the application needs so we can reduce our memory costs inside the data center. And by doing that, it’s back to that tokenomics. Yeah. Well, the company is surely in its growth phase right now. I’m a technologist at heart. I’m super lucky to have built out an amazing team. We’re very lucky to have a new CEO on the team.

A gentleman named Rick Hagberg, who’s just an industry veteran who’s been phenomenal. We’re raising additional capital. We’re growing the team. We’re bringing new executives on board. We are having our moment right now and we’re super excited and blessed to be part of this industry. The way I feel about it is when we go around and we tell customers about our vision, which is stop putting static resources inside of a server. Let’s take all of the GPUs, pull them inside of the rack, add a push of a button, dynamically allocate any GPU to any server that you want. You won’t lose any performance. You’ll scale up as big as you want. We’re a driverless solution. We can drive to 100% utilization. When we tell this story, no one ever says, “Well, that’s a terrible idea.” That’s not the reaction that we get, right? The reaction we get is, “Wow, this is incredible. This is obvious. Obvious is the one that we get more than anything else. This is obviously the right answer of how this should be done. Why isn’t everybody doing it this way?” And me, that’s my mission as the founder here is to make sure eventually everybody is doing it this way.