NVIDIA GTC 2024 | Joshua Patterson | Voltron Data

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Joshua Patterson

Co-Founder & CEO

Voltron Data

play_circle_outline Revolutionizing Data Analytics: Unleashing Voltron Data's Power to Overcome Traditional System Limitations

play_circle_outline Unveiling Voltron Data: A High-speed Query Engine & Innovative Software Deployment and Pricing Model

play_circle_outline Vision of the company and future partnerships

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Joshua Patterson | Voltron Data

Joshua Patterson

Co-Founder & CEO Voltron Data

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(gentle music)

>> Hi, everybody. We're back at GTC 2024 in San Jose, California. This is Dave Vellante, you're watching "theCUBE." John Furry's also in the house. We're broadcasting all day, wall-to-wall coverage of GTC. Josh Patterson is here. He's the co-founder and CEO of Voltron Data. Welcome to the Cube, thanks for coming on.

>> Thank you for having me.

>> Alright, first question. Why did you found the company?

>> Before Voltron Data, I was actually working on data processing for about five years at NVIDIA. I created the RAPIDS ecosystem with Keith Krauss and Mike Winn and others from the team. And we showed that GPUs were great at data processing, machine learning, graph analytics, and the rest of the data science ecosystem that wasn't deep learning, but it was difficult to use. A lot of the frameworks in data analytics weren't designed for GPUs and accelerated computing. And so we were getting good performance, but not great performance, and we wanted to push the bounds a lot further. We wanted to make it simpler. We wanted make it easier for enterprises to deploy GPUs at scale for data analytics. And we also thought that data analyst could be significantly cheaper and faster. And so we started Voltron Data, we brought in a few other companies over the years and you know, really the rest is history. Ursa Computing, the AblazZe SQL guys, Terra Tide, Island Data. Most recently we acquired the Clay Pot AI team and we've just been laser focused on making it easier to build large scale distributed systems using NVIDIA GPU.

>> Thank you for that. So can we go through a little bit of history of the analytics business? I mean, it kind of starts with, you know, let's not go back too far, but you had the Cognos Cubes, if you will, and then, you know, Hadoop came along and we were very excited, you know, the whole idea of bringing five megabytes of code to, you know, a big hunk of data, but it was too complicated. Then Spark comes along, it does simplifies that with the Spark execution engine. And, you know, cloud obviously came in, and cloud databases, separating compute from storage. And then boom, AI comes. So you're seeing a lot of the analytics companies, they go out, they buy AI companies, they're picking up talent. Now the big, the mantra is bring the AI to the data. Okay, so how are you guys different? What are you able to do? You're obviously AI native, if I can use that term, or at least proximate to when-

>> Accelerator native is the one we like to say.

>> Yeah, okay, there you go, accelerated computing native, but at least proximate to the timeframe. It's not like you were founded 15 years ago and having to, you know, retool. But so why does the world need you and versus some of those existing analytic systems.

>> So when you think about the transition between Hadoop and Spark, Hadoop had this cold map produced paradigm. You would read data from disc, you would do Apple, some analysis, you would write to disc, read back from disc, and you do this read, write from disc.

>> Map it, reduce it, map it, reduce it.

>> That's right. >> There it is. (laughs)

>> And sparks it, let's keep it in memory as long as possible. We'll spill graciously, but it was really kind of moving us to into this in-memory computing, and you saw these, you know, 10 to 25 X speed ups going from this, you know, map-produced paradigm to this in-memory paradigm. It's the same thing with GPUs. And, so, when we first started at NVIDIA, and we were trying to figure out how to make NVIDIA GPUs better at data analytics. The first thing we said is, "What if we could keep data in the GPU longer?" Instead of using the GPU as a co-processor where we just pull data in to do computationally hard things, what if we did everything in the GPU? Decompression, decoding, reading CSVs, doing string parses, regex engines, joins, group eyes, and what if we use system memory, kind of like how, you know, spark would use disc, we would spill to that.

>> Tier it. >> Tier it, exactly. And so we inverted the whole, you know, the thought process instead of using GPUs as a co-processor for computationally hard things, GPUs are the processor for everything. And what we realized is we unlocked a lot of speedups, but then we couldn't feed the GPUs fast enough. There were still a lot of overhead with, you know, Java-based systems just because of the JBM and garbage collection, Python-based systems really, you know, couldn't keep up with the speeds and feeds that we needed. And so we, you know, really took a deep look and started back from the ground up with the C++ base distributed execution engine. You know, really optimized for full stack acceleration, GPUs, high-end networking like InfiniBand and Rocky, as well as using flash storage. I'm sure you all are familiar with all them (indistinct)

DDN, Bass Data, et cetera. >> Of course, yeah, yeah, yeah.

>> And so when you fully leverage all these things in like GPU direct storage, UCX for really pushing InfiniBand you get these really massive speed ups. And so that's what we do as a company. So we focus on the data pipeline side of AI. How do we do ETL, how do we do feature engineering? How do we do data pre-processing at scale so we can feed these, you know, large scale machine learning, AI geospatial systems, routing optimization systems, a lot faster and easier. So that's really the differentiation. Everyone's still, you know, trying to continue to do data processing on CPUs and Voltron GPUs for AI and we're like, "No, let's just move everything to the GPU" "so we can get this whole end-to-end acceleration."

>> And if the workload is, is GPU friendly if you will, then that works well. You don't want to use this for, you know, some older general purpose workload, that's fine, GPU is too expensive, but you're unlocking the power of the GPU, utilizing it to a much greater degree and exploiting the high performance storage and high performance networking in ways that are novel.

>> Absolutely, and so to your point, you're like, "Use the GP for what's important." This is honestly why we target problems that are 10 terabytes and above, not how much data you have, but how much are you actually querying at any given time. And so at 10 terabyte queries, you really start to use a lot of CPU cores and, you know, even at 10 terabytes doing TPCH, you plateau at 200 nodes around 6,400 cores, where even if you add more cores, more nodes, it doesn't get faster. And we can outperform a 200 node spark cluster with two DGX A100s. And so shrinking that back down, you know, that 100X reduction in servers, not only is it faster, but it's also more energy efficient, more space efficient, and it allows us to scale to these, you know, 100 terabyte problems. And that's what we announced this week at GTC, basically Theseus' performance, our distributed query engine, at 100 terabytes, 30 terabytes and 10 terabytes.

>> Oh, awesome. Okay. So my alternative would be, I could use the spark execution engine. I suppose I could use elastic MapReduce in the cloud. (Josh laughs) I could go back 10 years and just try to redo Hadoop, but none of that would make sense.

>> Right. >> Okay. So you're solving a problem that that is large enough and, just economics, makes sense to do it with full term data.

>> It's long enough to merit A GPU and it's important. When you typically have, you know, 50 terabyte, 100 terabyte, 200 terabyte queries probably doing something important. And so, yes, that's a great time to use GPUs. Not only does it free up your CPU cluster for these, you know, more BI-like workloads, it also feeds into these AI system a lot faster. People can iterate their model training faster. You know, one of my, you know, favorite stories is back in 2020, the NVTabular team at NVIDIA they showed at the RecSys competition that doing more feature engineering faster, being able to iterate feature engineering faster, actually produced better models than throwing fancy deep-learning models at the problem. And so the more we can do feature engineering quickly, the actually the better business insights people can get, and save money.

>> Okay, so let's get into, I want to understand the product better, but let's start with sort of the use case. So what's a good representative use case? Let's start there. And then I want to say, "Okay, that's my use case." "How do I get started?" "What do I have to do?" "What are the deployment, you know," "of parameters, et cetera?"

>> So, you know, one of the simplest use cases is really in retail, forecasting. You have a lot of goods. You have, you know, terabytes of data of what was bought last week, the week before that, the week before that, you want to apply some type of decay factor, more recent purchases have a higher weight than past purchases. And then you want to just basically do a bunch of feature engineering and ETL to get that ready for some type of AI or ML model, whether it's, you know, PyTorch or XG Boost. You still have to do, you know, tabular pre-processing, I want to join different data sets together. I want to merge in, you know, distribution center data. I want to merge in, you know, weather data, holidays, all these different things to build this massive corpus. And then you want to, you know, do feature engineering. So really nothing different than what Spark, Presto, Trino do today, but when you're a, you know, a retailer, if you're a small retailer, that might be a four or five terabyte query. If you're, you know, a Walmart, a Target, a Home Depot, that's dozens of terabytes. And so when you get to these really large queries, the faster you can pre-process them in a myriad of different ways, you can build better forecast and better forecast just means you save more money.

>> So you are a query execution and a query acceleration engine, is that right? Is that the right way to think of it? In other words, I leave the data where it is, I bring the compute to the data.

>> Absolutely. >> Okay.

>> So we are an accelerated query engine. It's Kubernetes native, cloud native. So, essentially it's all ephemeral, it's containers. We spin up a cluster in seconds and we can pull raw data directly from network attached storage into our engine. We can basically, you know, fully utilize InfiniBand and pull data in at line rate and just analyze the data, just do what you would normally do, joins, group buys, filters, aggregations, and then we write that data back to other open formats, whether it's Parquet, ORC, Avro, JSON or even just push it through Arrow Flight to, you know, directly into a machine learning system.

>> But most, I'm trying to think, I mean, I think about most analytic data platforms are very limited in terms of the number of complex joins they they can do. Is that a problem that you solve necessarily, or?

>> They're limited by, you know, performance and time, primarily. And when you take something from hours and you can now run it in seconds, you can do a lot more joins, you can do a lot more group bytes, you can do a lot more complexity. It's probably the best way to say it. And, so, absolutely we, with just our brute force performance and speed, it allows people to do more things quickly.

>> So what do I buy from you and how do I deploy it?

>> So what you buy from us is access to our software. It's an enterprise software license model. We license our software, you get our containers, you spin them up on your Kubernetes distribution and you're off to the races. It's really simple. We can tie into existing logging, authentication, you know, security systems that you might have in within your environment. We are not selling a SaaS. We're selling software. We don't want to own your data. Data has gravity. Data is expensive. We want people to just have this ephemeral compute that runs on their NVIDIA GPUs to fill those underutilized cycles with really, really fast data analytics. We also license it through partners. And so we partner with HPE, we are working on other partnerships today where they can embed our engine directly in their product. And so seamlessly without even knowing you're hitting a GPU, people can write code as they would've done normally, and then it will target Theseus and then pull data from your data lake and you're off to the races and you just are happy that your queries are coming back faster and cheaper.

>> So you charge me an ELA or are you charging me per query? How do you charge?

>> Our pricing model right now is unlimited license model.

>> Okay.

>> When you're talking with people about hundreds of terabytes, they don't really want to meet her.

>> Right (chuckles) indeed.

>> A lot of our early customers are in DOD and in the intelligence space, they don't want to monitor. And so we went with the unlimited pricing model. It's your data center, it's your hardware, it's your code. We're just going to make it faster and give you a better engine to run on.

>> So, I write you a check, I get your software and I can use it. And then how do you, you know, keep it up to date? And it's not a SaaS, right? But, so, what do I do? I come back every couple years, you ship me updates?

>> We have a private forum for people to file bugs. We have new releases every six to eight weeks. We add new features, new functionality. So it's really kind of going back to just a traditional database model with software.

>> And what's the vision for the company?

>> Oh, that's a great question. We believe that this engine is going to be so powerful that the direct users are going to be prevalent. But, really, it's what are people going to do with the engine in their own products? We are really a partner-first company. We want people to embed Theseus into their products. We are actively, you know, talking to a few different SIM companies, cyber security incident management systems. We would love for Theseus to be powering the next generation of SIMs. We want people to start building more industry specific platforms on Theseus, whether it's genomics, any money laundering, we really want Theseus to be the backbone of data analytics at scale. And, so, regardless of the industry, you know, regardless of, you know, where you deploy it, whether it's cloud, on-premise, Air-Gapped, when you're experiencing these large scale 30 terabyte or above queries, we want people using NVIDIA GPUs and Theseus.

>> And what's your funding look like? Where are you at?

>> We raised about 110 million across our seed in Series A.

>> Nice.

>> We did that in early 2021 and 2022.

>> Oh, wow! Okay, you got it right in under the wire, well done.

(both laughing) >> Thank you. So we're pretty well funded and it's expensive to build a data engine. I mean, anyone who's built a database or a query engine, it's a lot of work, a lot of effort. And you know, I'm actually super impressed with how fast the team built it, how large it scales, and, you know, in the time we've done it,

>> You know, Jensen was talking in his keynote, he must have used the word digital twin, I dunno, 10 or 15 times. That whole idea of a digital representation of your business. I think about, you know, analytics systems today are largely historical systems of truth, that's assuming you can get, you know, the data correct. Do you see a day where you start to bring in transactions and you actually can have a real time digital twin of your business, people, places and things? Is that something that you think is technically feasible in the near to midterm?

>> Are you asking is HTAP going to happen? Yes.

>> Well, more than HTAP right? I mean...

>> I was joking a little bit, but yes. I actually think that's where we're going. And so the world is a combination of real time data and then using batch processing to power other things, which can simulate the possibilities. And so you kind of need to be this like one step ahead if you're going to do digital twin, but across a permutation of different outcomes. And, so, as data's coming into a system and as a system's changing, you need to be able to record that and allow that to manipulate data, as well as using that data to kind of predict what's the next step out. And so right now the systems are kind of disjointed, the actual, you know, how the world is moving data is very different than how do we change the environment? How does this impact other things around it graciously? And, so, being able to move transactional systems and analytic systems closer together will allow us to do much more sophisticated digital twins faster, you know, discovery in these types of environments. And so one of the things about being able to do analytics very quickly on large scale, we can actually start to marry these systems together. And we've even shown that we can basically read NoSQL and other non-columnar formats just as fast as we can read columnar formats just by using all the same hardware bypassing and speed of GPUs. And so I actually believe that, you know, very soon we're going to start to see these, you know, really elegant solutions that are quite simple. One data store, both operational data and analytic data, powering both AI and, you know, simulations.

>> And when Josh was asking me about "Well, you think HTSP's going to happen?" He's talking about hybrid transaction analytic, you know, processing, which, you know, you could say it's kind of here today where people just sort of think about MySQL heatwave, they've sort of got an analytic engine and they got a transaction engine, there's a big hunk in memory. But really talking about a semantic layer where you can make all those different data types that you were just talking about coherent. You could do large scale joins and it starts to get down to sort of rethinking how you even lay out data on a disc or a flash, really taking things that databases understand, think of them as strings, and turning them into things that humans understand, like things, people, places and things. And, you know, you think about the natural language processing and the AI era, that's a vision that we'd love to see happen. We just don't know if it's technically feasible in, you know, this decade, but you think it is.

>> Performance makes all things possible. (Dave laughs) I mean, you know?

>> And money! (laughs)

>> And money, but I mean, I think that's the immediate story.

>> But, we don't have enough money, so it's, right.

>> We keep making GPUs faster, every generation, and the faster the GPUs get, the better AI gets, the better inferencing gets, we start to, you know, really pull down this time constraint. And so these systems start to kind of, you know, evolve graciously just out of pure performance and speed. And, so, all the things you were saying are exactly right. But if I can take data in a format that might not be ideal, but I can just read it in so fast and manipulate it so fast and then convert it to a new format at real time speed, these lines just get very blurred quickly.

>> Imagine what that does to your supply chain and drug discovery, I mean there's so many use cases.

>> Routing optimization. >> Right, absolutely. Alright, give us the pitch for your company. Give us the little elevator pitch, you know, as before we exit here.

>> Sure, Voltron Data is the leading designer and builder of data systems. We want to make the next generation of data systems as efficient as its systems today. Data's growing, data will be 10 times larger, you know, in five years. We want it to be easier than it is today to build these systems.

>> Josh, fantastic. Thank you so much for coming on "theCUBE." It's really a pleasure having you.

>> Thank you.

>> And good luck. All right, and keep it right there everybody, this is Dave Vellante, John Furry is also here, GTC 2024 from San Jose. You're watching "theCUBE." (gentle music)

Joshua Patterson | Voltron Data

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(gentle music)

>> Hi, everybody. We're back at GTC 2024 in San Jose, California. This is Dave Vellante, you're watching "theCUBE." John Furry's also in the house. We're broadcasting all day, wall-to-wall coverage of GTC. Josh Patterson is here. He's the co-founder and CEO of Voltron Data. Welcome to the Cube, thanks for coming on.

>> Thank you for having me.

>> Alright, first question. Why did you found the company?

>> Before Voltron Data, I was actually working on data processing for about five years at NVIDIA. I created the RAPIDS ecosystem with Keith Krauss and Mike Winn and others from the team. And we showed that GPUs were great at data processing, machine learning, graph analytics, and the rest of the data science ecosystem that wasn't deep learning, but it was difficult to use. A lot of the frameworks in data analytics weren't designed for GPUs and accelerated computing. And so we were getting good performance, but not great performance, and we wanted to push the bounds a lot further. We wanted to make it simpler. We wanted make it easier for enterprises to deploy GPUs at scale for data analytics. And we also thought that data analyst could be significantly cheaper and faster. And so we started Voltron Data, we brought in a few other companies over the years and you know, really the rest is history. Ursa Computing, the AblazZe SQL guys, Terra Tide, Island Data. Most recently we acquired the Clay Pot AI team and we've just been laser focused on making it easier to build large scale distributed systems using NVIDIA GPU.

>> Thank you for that. So can we go through a little bit of history of the analytics business? I mean, it kind of starts with, you know, let's not go back too far, but you had the Cognos Cubes, if you will, and then, you know, Hadoop came along and we were very excited, you know, the whole idea of bringing five megabytes of code to, you know, a big hunk of data, but it was too complicated. Then Spark comes along, it does simplifies that with the Spark execution engine. And, you know, cloud obviously came in, and cloud databases, separating compute from storage. And then boom, AI comes. So you're seeing a lot of the analytics companies, they go out, they buy AI companies, they're picking up talent. Now the big, the mantra is bring the AI to the data. Okay, so how are you guys different? What are you able to do? You're obviously AI native, if I can use that term, or at least proximate to when-

>> Accelerator native is the one we like to say.

>> Yeah, okay, there you go, accelerated computing native, but at least proximate to the timeframe. It's not like you were founded 15 years ago and having to, you know, retool. But so why does the world need you and versus some of those existing analytic systems.

>> So when you think about the transition between Hadoop and Spark, Hadoop had this cold map produced paradigm. You would read data from disc, you would do Apple, some analysis, you would write to disc, read back from disc, and you do this read, write from disc.

>> Map it, reduce it, map it, reduce it.

>> That's right. >> There it is. (laughs)

>> And sparks it, let's keep it in memory as long as possible. We'll spill graciously, but it was really kind of moving us to into this in-memory computing, and you saw these, you know, 10 to 25 X speed ups going from this, you know, map-produced paradigm to this in-memory paradigm. It's the same thing with GPUs. And, so, when we first started at NVIDIA, and we were trying to figure out how to make NVIDIA GPUs better at data analytics. The first thing we said is, "What if we could keep data in the GPU longer?" Instead of using the GPU as a co-processor where we just pull data in to do computationally hard things, what if we did everything in the GPU? Decompression, decoding, reading CSVs, doing string parses, regex engines, joins, group eyes, and what if we use system memory, kind of like how, you know, spark would use disc, we would spill to that.

>> Tier it. >> Tier it, exactly. And so we inverted the whole, you know, the thought process instead of using GPUs as a co-processor for computationally hard things, GPUs are the processor for everything. And what we realized is we unlocked a lot of speedups, but then we couldn't feed the GPUs fast enough. There were still a lot of overhead with, you know, Java-based systems just because of the JBM and garbage collection, Python-based systems really, you know, couldn't keep up with the speeds and feeds that we needed. And so we, you know, really took a deep look and started back from the ground up with the C++ base distributed execution engine. You know, really optimized for full stack acceleration, GPUs, high-end networking like InfiniBand and Rocky, as well as using flash storage. I'm sure you all are familiar with all them (indistinct)

DDN, Bass Data, et cetera. >> Of course, yeah, yeah, yeah.

>> And so when you fully leverage all these things in like GPU direct storage, UCX for really pushing InfiniBand you get these really massive speed ups. And so that's what we do as a company. So we focus on the data pipeline side of AI. How do we do ETL, how do we do feature engineering? How do we do data pre-processing at scale so we can feed these, you know, large scale machine learning, AI geospatial systems, routing optimization systems, a lot faster and easier. So that's really the differentiation. Everyone's still, you know, trying to continue to do data processing on CPUs and Voltron GPUs for AI and we're like, "No, let's just move everything to the GPU" "so we can get this whole end-to-end acceleration."

>> And if the workload is, is GPU friendly if you will, then that works well. You don't want to use this for, you know, some older general purpose workload, that's fine, GPU is too expensive, but you're unlocking the power of the GPU, utilizing it to a much greater degree and exploiting the high performance storage and high performance networking in ways that are novel.

>> Absolutely, and so to your point, you're like, "Use the GP for what's important." This is honestly why we target problems that are 10 terabytes and above, not how much data you have, but how much are you actually querying at any given time. And so at 10 terabyte queries, you really start to use a lot of CPU cores and, you know, even at 10 terabytes doing TPCH, you plateau at 200 nodes around 6,400 cores, where even if you add more cores, more nodes, it doesn't get faster. And we can outperform a 200 node spark cluster with two DGX A100s. And so shrinking that back down, you know, that 100X reduction in servers, not only is it faster, but it's also more energy efficient, more space efficient, and it allows us to scale to these, you know, 100 terabyte problems. And that's what we announced this week at GTC, basically Theseus' performance, our distributed query engine, at 100 terabytes, 30 terabytes and 10 terabytes.

>> Oh, awesome. Okay. So my alternative would be, I could use the spark execution engine. I suppose I could use elastic MapReduce in the cloud. (Josh laughs) I could go back 10 years and just try to redo Hadoop, but none of that would make sense.

>> Right. >> Okay. So you're solving a problem that that is large enough and, just economics, makes sense to do it with full term data.

>> It's long enough to merit A GPU and it's important. When you typically have, you know, 50 terabyte, 100 terabyte, 200 terabyte queries probably doing something important. And so, yes, that's a great time to use GPUs. Not only does it free up your CPU cluster for these, you know, more BI-like workloads, it also feeds into these AI system a lot faster. People can iterate their model training faster. You know, one of my, you know, favorite stories is back in 2020, the NVTabular team at NVIDIA they showed at the RecSys competition that doing more feature engineering faster, being able to iterate feature engineering faster, actually produced better models than throwing fancy deep-learning models at the problem. And so the more we can do feature engineering quickly, the actually the better business insights people can get, and save money.

>> Okay, so let's get into, I want to understand the product better, but let's start with sort of the use case. So what's a good representative use case? Let's start there. And then I want to say, "Okay, that's my use case." "How do I get started?" "What do I have to do?" "What are the deployment, you know," "of parameters, et cetera?"

>> So, you know, one of the simplest use cases is really in retail, forecasting. You have a lot of goods. You have, you know, terabytes of data of what was bought last week, the week before that, the week before that, you want to apply some type of decay factor, more recent purchases have a higher weight than past purchases. And then you want to just basically do a bunch of feature engineering and ETL to get that ready for some type of AI or ML model, whether it's, you know, PyTorch or XG Boost. You still have to do, you know, tabular pre-processing, I want to join different data sets together. I want to merge in, you know, distribution center data. I want to merge in, you know, weather data, holidays, all these different things to build this massive corpus. And then you want to, you know, do feature engineering. So really nothing different than what Spark, Presto, Trino do today, but when you're a, you know, a retailer, if you're a small retailer, that might be a four or five terabyte query. If you're, you know, a Walmart, a Target, a Home Depot, that's dozens of terabytes. And so when you get to these really large queries, the faster you can pre-process them in a myriad of different ways, you can build better forecast and better forecast just means you save more money.

>> So you are a query execution and a query acceleration engine, is that right? Is that the right way to think of it? In other words, I leave the data where it is, I bring the compute to the data.

>> Absolutely. >> Okay.

>> So we are an accelerated query engine. It's Kubernetes native, cloud native. So, essentially it's all ephemeral, it's containers. We spin up a cluster in seconds and we can pull raw data directly from network attached storage into our engine. We can basically, you know, fully utilize InfiniBand and pull data in at line rate and just analyze the data, just do what you would normally do, joins, group buys, filters, aggregations, and then we write that data back to other open formats, whether it's Parquet, ORC, Avro, JSON or even just push it through Arrow Flight to, you know, directly into a machine learning system.

>> But most, I'm trying to think, I mean, I think about most analytic data platforms are very limited in terms of the number of complex joins they they can do. Is that a problem that you solve necessarily, or?

>> They're limited by, you know, performance and time, primarily. And when you take something from hours and you can now run it in seconds, you can do a lot more joins, you can do a lot more group bytes, you can do a lot more complexity. It's probably the best way to say it. And, so, absolutely we, with just our brute force performance and speed, it allows people to do more things quickly.

>> So what do I buy from you and how do I deploy it?

>> So what you buy from us is access to our software. It's an enterprise software license model. We license our software, you get our containers, you spin them up on your Kubernetes distribution and you're off to the races. It's really simple. We can tie into existing logging, authentication, you know, security systems that you might have in within your environment. We are not selling a SaaS. We're selling software. We don't want to own your data. Data has gravity. Data is expensive. We want people to just have this ephemeral compute that runs on their NVIDIA GPUs to fill those underutilized cycles with really, really fast data analytics. We also license it through partners. And so we partner with HPE, we are working on other partnerships today where they can embed our engine directly in their product. And so seamlessly without even knowing you're hitting a GPU, people can write code as they would've done normally, and then it will target Theseus and then pull data from your data lake and you're off to the races and you just are happy that your queries are coming back faster and cheaper.

>> So you charge me an ELA or are you charging me per query? How do you charge?

>> Our pricing model right now is unlimited license model.

>> Okay.

>> When you're talking with people about hundreds of terabytes, they don't really want to meet her.

>> Right (chuckles) indeed.

>> A lot of our early customers are in DOD and in the intelligence space, they don't want to monitor. And so we went with the unlimited pricing model. It's your data center, it's your hardware, it's your code. We're just going to make it faster and give you a better engine to run on.

>> So, I write you a check, I get your software and I can use it. And then how do you, you know, keep it up to date? And it's not a SaaS, right? But, so, what do I do? I come back every couple years, you ship me updates?

>> We have a private forum for people to file bugs. We have new releases every six to eight weeks. We add new features, new functionality. So it's really kind of going back to just a traditional database model with software.

>> And what's the vision for the company?

>> Oh, that's a great question. We believe that this engine is going to be so powerful that the direct users are going to be prevalent. But, really, it's what are people going to do with the engine in their own products? We are really a partner-first company. We want people to embed Theseus into their products. We are actively, you know, talking to a few different SIM companies, cyber security incident management systems. We would love for Theseus to be powering the next generation of SIMs. We want people to start building more industry specific platforms on Theseus, whether it's genomics, any money laundering, we really want Theseus to be the backbone of data analytics at scale. And, so, regardless of the industry, you know, regardless of, you know, where you deploy it, whether it's cloud, on-premise, Air-Gapped, when you're experiencing these large scale 30 terabyte or above queries, we want people using NVIDIA GPUs and Theseus.

>> And what's your funding look like? Where are you at?

>> We raised about 110 million across our seed in Series A.

>> Nice.

>> We did that in early 2021 and 2022.

>> Oh, wow! Okay, you got it right in under the wire, well done.

(both laughing) >> Thank you. So we're pretty well funded and it's expensive to build a data engine. I mean, anyone who's built a database or a query engine, it's a lot of work, a lot of effort. And you know, I'm actually super impressed with how fast the team built it, how large it scales, and, you know, in the time we've done it,

>> You know, Jensen was talking in his keynote, he must have used the word digital twin, I dunno, 10 or 15 times. That whole idea of a digital representation of your business. I think about, you know, analytics systems today are largely historical systems of truth, that's assuming you can get, you know, the data correct. Do you see a day where you start to bring in transactions and you actually can have a real time digital twin of your business, people, places and things? Is that something that you think is technically feasible in the near to midterm?

>> Are you asking is HTAP going to happen? Yes.

>> Well, more than HTAP right? I mean...

>> I was joking a little bit, but yes. I actually think that's where we're going. And so the world is a combination of real time data and then using batch processing to power other things, which can simulate the possibilities. And so you kind of need to be this like one step ahead if you're going to do digital twin, but across a permutation of different outcomes. And, so, as data's coming into a system and as a system's changing, you need to be able to record that and allow that to manipulate data, as well as using that data to kind of predict what's the next step out. And so right now the systems are kind of disjointed, the actual, you know, how the world is moving data is very different than how do we change the environment? How does this impact other things around it graciously? And, so, being able to move transactional systems and analytic systems closer together will allow us to do much more sophisticated digital twins faster, you know, discovery in these types of environments. And so one of the things about being able to do analytics very quickly on large scale, we can actually start to marry these systems together. And we've even shown that we can basically read NoSQL and other non-columnar formats just as fast as we can read columnar formats just by using all the same hardware bypassing and speed of GPUs. And so I actually believe that, you know, very soon we're going to start to see these, you know, really elegant solutions that are quite simple. One data store, both operational data and analytic data, powering both AI and, you know, simulations.

>> And when Josh was asking me about "Well, you think HTSP's going to happen?" He's talking about hybrid transaction analytic, you know, processing, which, you know, you could say it's kind of here today where people just sort of think about MySQL heatwave, they've sort of got an analytic engine and they got a transaction engine, there's a big hunk in memory. But really talking about a semantic layer where you can make all those different data types that you were just talking about coherent. You could do large scale joins and it starts to get down to sort of rethinking how you even lay out data on a disc or a flash, really taking things that databases understand, think of them as strings, and turning them into things that humans understand, like things, people, places and things. And, you know, you think about the natural language processing and the AI era, that's a vision that we'd love to see happen. We just don't know if it's technically feasible in, you know, this decade, but you think it is.

>> Performance makes all things possible. (Dave laughs) I mean, you know?

>> And money! (laughs)

>> And money, but I mean, I think that's the immediate story.

>> But, we don't have enough money, so it's, right.

>> We keep making GPUs faster, every generation, and the faster the GPUs get, the better AI gets, the better inferencing gets, we start to, you know, really pull down this time constraint. And so these systems start to kind of, you know, evolve graciously just out of pure performance and speed. And, so, all the things you were saying are exactly right. But if I can take data in a format that might not be ideal, but I can just read it in so fast and manipulate it so fast and then convert it to a new format at real time speed, these lines just get very blurred quickly.

>> Imagine what that does to your supply chain and drug discovery, I mean there's so many use cases.

>> Routing optimization. >> Right, absolutely. Alright, give us the pitch for your company. Give us the little elevator pitch, you know, as before we exit here.

>> Sure, Voltron Data is the leading designer and builder of data systems. We want to make the next generation of data systems as efficient as its systems today. Data's growing, data will be 10 times larger, you know, in five years. We want it to be easier than it is today to build these systems.

>> Josh, fantastic. Thank you so much for coming on "theCUBE." It's really a pleasure having you.

>> Thank you.

>> And good luck. All right, and keep it right there everybody, this is Dave Vellante, John Furry is also here, GTC 2024 from San Jose. You're watching "theCUBE." (gentle music)