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>> From our studios,
in the heart of Silicon Valley,
Palo Alto, California,
this is a CUBE Conversation.
>> Hey, welcome back everybody,
Jeff Frick here with theCUBE.
We're at our Palo Alto studios
having a CUBE Conversation,
we're just about ready for the madness
of the conference season to start in a few months,
so it's nice to have some time
to have things a little calmer in the studio,
and we're excited to have a new company,
I guess they're not that new,
but they're relatively new,
they've been working on a really interesting technology
around infrastructure,
and we welcome to the studio, first time, I think,
Sumit Puri, CEO and co-founder of Liqid, welcome.
>> Thank you guys, very very happy to be here.
>> And joined by our big brain, David Floyer,
of course, the CTO and co-founder of Wikibon
and knows all things infrastructure.
Dave, always good to see you.
>> It's so good to see you.
>> All right, so let's jump into this,
Sumit, give us the basic overview of Liqid,
what are you guys all about,
little bit of the company background,
how long you've been around.
No, absolutely, absolutely,
Liqid is a software-defined infrastructure company,
the technology that we've developed is referred to as
composable infrastructure, think, dynamic infrastructure,
and what we do, is we go and we turn data center resources
from statically-configured boxes
to dynamic, agile infrastructure.
Our core technology is two-part.
Number 1, we have a fabric layer,
that allows you to interconnect off-the-shelf hardware,
but more importantly, we have a software layer,
that allows you to orchestrate,
or dynamically configure servers, at the bare metal.
>> So, who are you selling these solutions to?
What's your market, what's the business case
for this solution?
>> Absolutely, so first, I guess, let me explain a little bit
about what we mean by composable infrastructure.
Rather than building servers by plugging devices
into the sockets of the motherboard,
with composability it's all about pools, or trays,
of resources.
A tray of CPUs, a tray of SSDs, a tray of GPUs,
a tray of networking devices,
instead of plugging those into a motherboard,
we connect those into a fabric switch,
and then we come in with our software,
and we orchestrate, or recompose, at the bare metal.
Grab this CPU, grab those four SSDs,
these eight GPUs, and build me a server,
just like you were plugging devices into the motherboard,
except you're defining it in software,
on the other side, you're getting delivered infrastructure
of any size, shape, or ratio that you want.
Except that infrastructure is dynamic,
when we need another GPU in our server,
we don't send a guy with a cart to plug the device in,
we reprogram the fabric and add or remove devices
as required by the application.
We give you all the flexibility that you would get
from public cloud, on the infrastructure
that you are forced to own.
And now, to answer your question of where we find
a natural fit for our solution,
one primary area is obviously cloud.
If you're building a cloud environment,
whether you're providing cloud as a service
or whether you're providing cloud
to your internal customers,
building a more dynamic, agile cloud
is what we enable.
>> So, is the use case more just to use
your available resources and reconfigure it
to set something that basically runs that way for a while,
or are customers more using it
to dynamically reconfigure those resources based on,
say, a temporary workload,
is kind of a classic cloud example,
where you need a bunch of something now,
but not necessarily forever. >> Sure.
The way we look at the world
is very much around resource utilization.
I'm buying this very expensive hardware,
I'm deploying it into my data center,
typical resource utilization is very low,
below 20%, right?
So what we enable is the ability to get
better resource utilization out of the hardware
that you're deploying inside your data center.
If we can take a resource that's utilized 20% of the time
because it's deployed as a static element inside of a box
and we can raise the utilization to 40%,
does that mean we are buying less hardware
inside of our data center?
Our argument is yes, if we can take rack scale efficiency
from 20% to 40%, our belief is we can do
the same amount of work with less hardware.
>> So it's a fairly simple business case, then.
To do that.
So who are your competition in this area?
Is it people like HP or Intel, or,
>> That's a great question,
I think both of those are interesting companies,
I think HPE is the 800-pound gorilla
in this term called composability
and we find ourselves a slightly different approach
than the way that those guys take it,
I think first and foremost, the way that we're different
is because we're disaggregated, right?
When we sell you trays of resources,
we'll sell you a tray of SSD or a tray of GPUs,
where HP takes a converged solution, right?
Every time I'm buying resources for my composable rack,
I'm paying for CPUs, SSDs, GPUs,
all of those devices as a converged resource,
so they are converged, we are disaggregated.
We are bare metal,
we have a PCIe-based fabric up and down the rack,
they are an ethernet-based fabric,
there are no ethernet SSDs, there are no ethernet GPUs,
at least today, so by using ethernet as your fabric,
they're forced to do virtualization protocol translation,
so they are not truly bare metal.
We are bare metal, we view of them
more as a virtualized solution.
We're an open ecosystem, we're hardware-agnostic, right?
We allow our customers to use whatever hardware
that they're using in their environment today.
Once you've kind of gone down that HP route,
it's very much a closed environment.
>> So what about some of the customers that you've got?
Which sort of industries, which sort of customers,
I presume this is for the larger types of customers,
in general, but say a little bit about
where you're making a difference.
>> No, absolutely, right?
So, obviously at scale, composability has even more benefit
than in smaller deployments,
I'll give you just a couple of use case examples.
Number one, we're working with a transportation company,
and what happens with them at 5 p.m.
is actually very different than what happens at 2 a.m.,
and the model that they have today is
a bunch of static boxes and they're playing a game
of workload matching.
If the workload that comes in fits the appropriate box,
then the world is good.
If the workload that comes in ends up on a machine
that's oversized, then resources are being wasted,
and what they said was, "We want to take a new approach.
"We want to study the workload as it comes in,
"dynamically spin up small, medium, large,
"depending on what that workload requires,
"and as soon as that workload is done,
"free the resources back into the general pool."
Right, so that's one customer, by taking a dynamic approach,
they're changing the TCO argument
inside of their environment.
And for them, it's not a matter of
am I going dynamic or am I going static,
everyone knows dynamic infrastructure is better,
no one says, "Give me the static stuff."
For them, it's am I going public cloud,
or am I going on prem.
That's really the question,
so what we provide is
public cloud is very easy,
but when you start thinking about next-generation workloads,
things that leverage GPUs and FPGAs,
those instantiations on public cloud
are just not very cheap.
So we give you all of that flexibility
that you're getting on public cloud,
but we save you money by giving you that capability on prem.
So that's use case number one.
Another use case is very exciting for us,
we're working with a studio down in southern California,
and they leverage these NVIDIA V100 GPUs.
During the daytime, they give those GPUs
to their AI engineers,
when the AI engineers go home at night,
they reprogram the fabric and they use those same GPUs
for rendering workloads.
They've taken $50,000 worth of hardware
and they've doubled the utilization of that hardware.
>> The other use case we talked about
before we turned the cameras on there,
was pretty interesting,
was kind of multiple workloads against the same data set,
over a series of time
where you want to apply different resources.
I wonder if you can unpack that a little bit
because I think that's a really interesting one
that we don't hear a lot about.
So, we would say about 60 plus to 70% of our deployments
in one way or another touch the realm of AI.
AI is actually not an event, AI is a workflow,
what do we do?
First we ingest data, that's very networking-centric.
Then we scrub and we clean the data,
that's actually CPU-centric.
Then we're running inference,
and then we're running training, that's GPU-centric.
Data has gravity, right?
It's very difficult to move petabytes of data around,
so what we enable is the composable AI platform,
leave data at the center of the universe,
reorchestrate your compute, networking, GPU resources
around the data.
That's the way that we believe that AI is approached.
>> So we're looking forward in the future.
What are you seeing
where you can make a difference in this?
I mean, a lot of changes happening,
there's Gen 4 coming out in PCIe,
there's GPUs which are moving down to the edge,
how do see, where do you see you're going to make a difference,
over the next few years.
>> That's a great question.
So I think there's 2 parts to look at, right?
Number one is the physical layer, right?
Today we build or we compose based upon PCIe Gen 3
because for the first time in the data center,
everything is speaking a common language.
When SSDs moved to NVMe, you had SSDs, network cards,
GPUs, CPUs, all speaking a common language
which was PCIe.
So that's why we've chosen to build our fabric
on this common interconnect,
because that's how we enable bare metal orchestration
without translation and virtualization, right?
Today, it's PCIe Gen 3, as the industry moves forward,
Gen 4 is coming.
Gen 4 is here.
We've actually announced
our first PCIe Gen 4 products already,
and by the end of this year,
Gen 4 will become extremely relevant into the market.
Our software has been architected from the beginning
to be physical layer-agnostic,
so whether we're talking PCIe Gen 3, PCIe Gen 4,
in the future something referred to as Gen Z,
(laughing) it doesn't matter for us,
we will support all of those physical layers.
For us it's about the software orchestration.
>> I would imagine, too, like TPUs and other physical units
that are going to be introduced in the system, too,
you're architected to be able to take those, new--
>> Today, today we're doing CPUs, GPUs, NVMe devices
and we're doing NICs.
We just made an announcement,
now we're orchestrating Optane memory with Intel.
We've made an announcement with Xilinx
where we're orchestrating FPGAs with Xilinx.
So this will continue, we'll continue to find more and more
of the resources that we'll be able to orchestrate
for a very simple reason,
everything has a common interconnect,
and that common interconnect is PCIe.
>> So this is an exciting time in your existence.
Where are you?
I mean, how far along are you
to becoming the standard in this industry?
>> Yeah, no, that's a great question,
and I think, we get asked a lot is
what company are you most similar to
or are you most like at the early stage.
And what we say is we, a lot of time,
compare ourselves to VMware, right?
VMware is the hypervisor for the virtualization layer.
We view ourselves as that physical hypervisor, right?
We do for physical infrastructure
what VMware is doing for virtualized environments.
And just like VMware has enabled many of the market players
to get virtualized, our hope is we're going to enable
many of the market players to become composable.
We're very excited about our partnership with Inspur,
just recently we've announced,
they're the number three server vendor in the world,
we've announced an AI-centric rack,
which leverages the servers and the storage solutions
from Inspur tied to our fabric to deliver
a composable AI platform.
>> That's great. >> Yeah, and it seems like
the market for cloud service providers,
'cause we always talk about the big ones,
but there's a lot of them, all over the world,
is a perfect use case for you,
because now they can actually offer
the benefits of cloud flexibility
by leveraging your infrastructure
to get more miles out of their investments
into their backend.
>> Absolutely, cloud, cloud service providers,
and private cloud, that's a big market
and opportunity for us, and we're not necessarily
chasing the big seven hyperscalers, right?
We'd love to partner with them, but for us,
there's 300 other companies out there
that can use the benefit of our technology.
So they necessarily don't have the R&D dollars available
that some of the big guys have, so we come in
with our technology and we enable those
cloud service providers to be more agile,
to be more competitive.
>> All right, Sumit, before we let you go,
season's coming up, we were just at RSA yesterday,
big shows comin' up in May,
where you guys, are we going to cross paths
over the next several weeks or months?
>> No, absolutely, we got a handful of shows coming up,
very exciting season for us, we're going to be at the OCP,
the Open Compute Project conference, actually next week,
and then right after that, we're going to be at
the NVIDIA GPU Technology Conference,
we're going to have a booth at both of those shows,
and we're going to be doing live demos
of our composable platform, and then at the end of April,
we're going to be at the Dell Technology World conference
in Las Vegas, where we're going to have a large booth
and we're going to be doing some very exciting demos
with the Dell team.
>> Sumit, thanks for taking a few minutes
out of your day to tell us a story,
it's pretty exciting stuff,
'cause this whole flexibility is such an important piece
of the whole cloud value proposition,
and you guys are delivering it all over the place.
>> Well, thank you guys for making the time today,
I was excited to be here,
thank you. >> All right, David,
always good to see you, >> Good to see you.
>> Smart man, alright,
I'm Jeff Frick, you're watching theCUBE from
theCUBE studios in Palo Alto,
thanks for watching, we'll see you next time.
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