0:00

If you're somebody that wants to utilize

0:02

fleet of H100s or , soon

0:04

, the GH200 platform , not only do you

0:07

need to commit to spending a lot

0:09

of money , but in many cases , if you

0:11

go to the bigger cloud providers

0:13

today , you need to actually commit to a year

0:15

or three years of spend with them before

0:17

you can even get access to these GPUs . And so

0:19

this is sort of that inflection point , I think , where a

0:21

solution like Akash really shines sort

0:24

of that inflection point , I think , where a solution like Akash really shines . For example , if

0:26

you were to go to akashnetworkcomgpu right now , what

0:29

you would find is that you can

0:31

geta H100 with an SXM

0:33

interface for as low as

0:35

$1.50 , which , I believe

0:37

, is you know , half or less than half of

0:39

what you can get at many of the other places out there .

0:59

Hi there , welcome to Buidl Crypto

1:01

Today . I have with

1:03

me Anil from Akash

1:06

. Welcome , Anil .

1:07

Hey , it's great to be here . Thanks for having

1:09

me .

1:17

Great to have you .

1:19

Great to have you . So , anil , could you talk a little bit about yourself ? Yeah

1:22

, sure , I'll be going into that a little bit

1:24

. So yeah , so my background I started out my education

1:26

was in college , was

1:28

in electrical engineering . So

1:31

I background in electrical engineering , undergrad and

1:33

then , even though I have a graduate

1:36

engineering degree in electrical

1:38

engineering , I actually focused

1:40

on computer networks and so a lot

1:42

of my coursework there was computer

1:45

science classes , a little bit of electrical

1:47

engineering as well , and

1:49

then for the first several years I'd

1:51

say probably about half of my career

1:54

, which is about two decades at this point I

1:57

spent my time working

1:59

on embedded software so this

2:02

is typically device

2:04

drivers for embedded devices

2:06

and so I spent

2:08

a bunch of years doing that at companies

2:10

like Motorola , working on consumer

2:12

electronic devices and writing

2:15

essentially what's called the hardware

2:17

abstraction layer for devices like

2:19

that . And then

2:21

at some point along that journey I

2:23

got more and more , you

2:27

know , towards the

2:29

customer , facing portions of the

2:31

business , and that's where I realized that

2:33

you know , understanding

2:35

the whole product lifecycle and

2:38

identifying a problem

2:40

in the existing

2:43

customer base or , you know , sort

2:45

of a gap in the market was something that

2:47

I find exciting and that sort of took

2:49

me towards looking at product

2:52

roles as a potential transition

2:54

point in my career , and

2:56

somewhere along there I also ended up going to business

2:58

school while I was working , and

3:01

you know , put that together with the

3:03

experience that I had as an engineer I

3:06

ultimately ended up moving into

3:08

a product role , initially

3:10

working for mid-sized companies

3:13

and then startups , and then macro mid-sized

3:15

companies , and

3:17

then , over the second half of my career

3:19

as a product person , I

3:22

got to work on everything from networking

3:25

and hardware devices to cloud networking

3:28

devices to pure cloud

3:31

and then , ultimately , monitoring

3:33

and telemetry type companies . So

3:36

I spent some time at companies like New Relic

3:38

and HashiCorp prior to coming to Akash , prior

3:40

to coming to Akash . And

3:44

so , given that I had a whole bunch of

3:47

background

3:49

in building solutions for cloud-native

3:51

products and cloud-native customers , when

3:55

I was approached by the folks at Akash or

3:57

at Overclock Labs , which is the

3:59

parent company above Akash Network and

4:02

the creator of Akash Network

4:04

as well , the

4:07

project really attracted me because I

4:09

looked at it as a significantly new

4:11

way of imagining how infrastructure

4:14

gets utilized , and

4:16

just looking at how the clouds

4:18

have evolved over the last couple of decades

4:21

, it became very clear to me

4:23

that the original

4:25

premise that the clouds were created for

4:27

is no longer

4:29

valid anymore in many cases

4:32

, and what

4:34

Akash was doing seemed to be in

4:36

the right direction in terms of seeing

4:40

where the industry was going to go in the next few years , and

4:42

that's what got me excited about Akash and got me

4:44

joining Overclock Labs .

4:47

I do remember Greg , one

4:49

of his interviews saying that story

4:53

of tech is story of

4:55

cloud , and so

4:57

, rightly so , and

5:00

based on what your

5:02

experience , you would naturally gravitate

5:04

towards the

5:07

Akash version of the cloud

5:09

. So could you talk

5:11

a little bit more about that ?

5:13

Yeah , so you know , like you said

5:16

, having been in the tech industry for

5:18

the last couple of decades , for

5:21

the first few years of my career

5:23

, you know , cloud

5:26

was either non-existent or very nascent

5:28

and most companies were running

5:30

all of their software on-prem

5:32

and offering it as a service

5:34

or just shipping software

5:37

as binary images that their customers would

5:39

utilize , and so this was like the early

5:42

to mid-2000s , right . And so this

5:44

was like the early to mid-2000s , right . And then

5:46

when the concept of a cloud was invented back

5:48

in I don't know , 2006 , 2007

5:51

, by Amazon , and it's really

5:53

started to gain traction , probably four or five years

5:55

after that , where you saw , you

6:03

know , there was this inflection point where , initially , cloud was primarily targeted towards , you know

6:05

, the startups and the small and medium businesses , and then , eventually

6:07

, the enterprises realized that this

6:10

is something that they can utilize as well . That

6:13

transition probably happened somewhere around 2011

6:16

, 2012 , which is kind of the

6:18

time you can consider the cloud

6:20

going mainstream and beginning enterprise

6:22

adoption from then on . The

6:24

cloud going mainstream and beginning enterprise

6:26

adoption from then on . I think for the first few initial

6:29

years when the cloud came up , the

6:32

big draw towards the cloud was that it basically gave , or it essentially leveled

6:34

the playing field for startup

6:37

companies . So if you were a startup

6:39

company in the , you

6:41

know , around the time that the dot-com boom

6:43

happened , or in the early

6:46

2000s , if you wanted to build

6:48

a software service

6:50

and offer it to your customers , you

6:52

had a fairly large upfront investment

6:55

to make , and the

6:57

cloud essentially enabled

7:00

startups to be able to , you

7:03

know , get

7:07

to market much faster at a much lower cost by taking away that capital expenditure that they

7:09

needed to spend on infrastructure , on servers

7:11

and storage and all of that , and

7:13

also for resources to manage all that

7:15

infrastructure . And

7:18

so that was great and you know , we've obviously

7:20

had a really good run in terms of a lot

7:22

of startups being able to test out products at a

7:24

really low cost , find product market

7:27

fit or , in some cases , not find product

7:29

market fit and decide to abandon the idea , do

7:31

something else , and so it really levels

7:33

the playing field for them and allowed a lot

7:35

of startups to disrupt the status quo

7:38

and ultimately bring value to the customers

7:40

. But

7:48

what's happened in the last few years , particularly as GPUs have really taken off thanks to all the

7:50

demand from AI and machine learning workloads in the last few years

7:52

, particularly since OpenAI's chat

7:54

GPT movement . What has happened is

7:56

that we're sort of going back to

7:58

the traditional ways where

8:01

, given the scarcity in the

8:05

availability of GPUs , particularly certain

8:07

high-end models like the A100s

8:10

and the H100s , and soon

8:12

the GH200s and the B100s

8:14

from NVIDIA , not

8:16

only are these significantly more expensive

8:18

, but in many cases

8:20

they're just really hard to get . In

8:23

many cases they're just really hard to get , and so if you're somebody

8:26

that wants to utilize , you know , a

8:28

fleet of H100s or , you

8:30

know , soon , the GH200 platform , not

8:33

only do you need to commit to spending

8:37

a lot of money , but in many cases

8:39

, if you go to the bigger cloud providers

8:42

today , you need to actually commit to

8:44

a year or three years of

8:46

spend with them before you can even

8:48

get access to these GPUs . So

8:50

if you sort of go back to the start

8:53

of the cloud and compare that to

8:55

where we are today , the whole

8:57

premise of the cloud , which is remove

8:59

that capital expenditure , the upfront

9:02

expenditure that you've got to do , and

9:04

give you the flexibility to scale

9:06

up and scale down without having to take on the

9:08

uh ongoing expense that

9:11

sort of goes away . Uh , if you have to commit

9:13

to a year of cloud expenditure

9:15

in order to get access to a certain piece

9:17

of hardware and so , uh

9:19

, this is sort of that inflection point , I think , where a solution

9:21

like akash really shines , and that's what we've've

9:24

been seeing with a lot of our customers and users

9:26

as well .

9:27

Thanks for that , anil . So to follow

9:30

up , a follow-up question , for that is

9:32

how Akash makes it

9:34

more accessible , the GPU

9:36

accessibility , as you rightly pointed out

9:38

. Right , it's harder to get

9:40

hands-on on H100s

9:43

and the higher-ups now NVIDIA is

9:45

coming out with . First of all . That's

9:48

my first question and I

9:51

remember Craig also mentioning about

9:53

Akash being suitable also

9:56

for the small language models . So

9:59

yeah , if you could expand on that .

10:01

Yeah , absolutely Would love to dig into that . So

10:04

there's a few different things that are at play here

10:06

and we kind of saw this coming , you

10:08

know , a year , year and a half ago , and we're just

10:10

kind of why we , you know

10:12

specifically , you

10:15

know , focused our but really doubled down on

10:17

that strategy and it was kind of

10:19

, I think , driven by two or three things , if I can

10:21

frame it that way . The

10:37

first was , you know , it was very clear that there was

10:39

going to be a huge amount of demand for GPU workloads because of the

10:41

growth in the amount of applications that are going to get built

10:43

in the next few years . That was very clear . I think

10:45

it became more and more clear after the chat

10:48

GPT movement , but it was clear to many people

10:50

even before that that there is going to be some

10:52

point in time whether it was going to be

10:54

six months , one year , two years , there

10:56

was going to be some point in time where this was going to happen . So

10:59

that's kind of number one . The second

11:01

thing was what

11:09

we also realized was that , uh , even though initially when the chat

11:11

gpt movement happened um , open , ai was it almost seemed

11:14

for a little bit , you know , maybe a month or two months

11:16

, that open ai was going to be the only

11:18

game in town and they were going to basically

11:20

suck all the oxygen out of everything else

11:22

and everybody's going to be just building an open AI

11:24

. And that sort

11:26

of went back again to history

11:29

repeating itself , which was , if you've been

11:31

around in tech for long enough , or if you have read

11:33

about technology

11:36

history even if you've not been around for that long

11:38

, what you have seen is that

11:40

there's always been points in

11:43

technology history

11:45

where , even

11:47

if a certain technology gets invented by a really

11:49

big player and is

11:51

initially only

11:54

available through that specific player , over

11:57

time there is

12:00

enough movements in

12:02

communities around the world that

12:04

leads to open source solutions . Movements

12:07

in communities around the world that leads to open source

12:09

solutions . Arguably

12:13

the biggest example of that , historically speaking , is the Linux operating system . So you know

12:15

, way back in the day , in the 90s , obviously Windows

12:17

was the most dominant operating

12:19

system out there , and

12:21

today , if you look at most server

12:24

workloads , as well as a lot of consumer electronics

12:26

and many

12:29

other services that you access through

12:31

SaaS services , all

12:33

of them underneath run Linux and

12:47

as a result of communities that build in the open and are

12:49

able to come together and create something that is , overall , going to create

12:51

a better world for people that are building . It

12:55

is pretty clear in our head that that is going to be the case

12:57

even with AI . If

13:03

you look back at Akash , akash

13:05

has always had a significant portion

13:08

of its code-based

13:10

open source right from day one . But

13:13

what we did approximately

13:16

, you know , a year and a half ago

13:18

is we decided to go 100%

13:21

open source , and this was way before even

13:23

the chat GPT movement source

13:28

, and this was way before even the chat GPT movement . And then not only did we decide

13:30

to go 100% open source , we also decided to go to an open development

13:32

model . So we essentially

13:34

came up with this idea

13:37

of building in the open , similar to what

13:39

projects like the Kubernetes

13:41

project does . So they have the concept

13:43

of special interest groups and work groups

13:45

where people essentially

13:48

are able to propose ideas , talk

13:51

about them in the open as part

13:53

of a community and

13:55

vote on things and then work

13:58

together to implement certain things that

14:00

make sense from a community-driven

14:03

project perspective . So this

14:05

is the switch that we made about a year and a half ago

14:07

and we , to this day , operate

14:10

the same way . So literally every single

14:13

decision that gets made gets made in the open

14:15

. It's documented in our GitHub repository . All

14:17

of our code base sits there as well , and

14:20

so we made this transition . And then , sure

14:22

enough , for the first few

14:24

months after OpenAI released ChatGPT

14:26

and that whole inflection point happened a

14:29

few months following that you saw that

14:32

there were competing open source models being

14:34

released for similar

14:37

types of functions or capabilities

14:39

as what OpenAI was releasing . And

14:42

then since then , which was about a year

14:44

or a year and three months now , we

14:46

have seen a whole bunch of new open source models

14:49

get released . Hugging Face has been

14:51

an amazing repository for all those models and

14:54

everything from image generation to

14:56

large language models to small language

14:59

models everything between those

15:01

is now almost always

15:03

you can find an equivalent open source

15:05

version of a closed source model , and

15:08

so our strategy of being an

15:10

open company aligns really well with that

15:12

, and so that's worked out really well for us

15:14

. And now

15:17

taking those two things and then marrying

15:19

it with one of the questions that you asked , which

15:21

is how does small language models fit and

15:23

how does large language models fit ? Essentially

15:27

, the way things come together really

15:30

nicely for us is given that we

15:32

have been a crypto native company as well , or

15:34

a crypto native project as well . We

15:36

obviously have a blockchain based

15:38

mechanism for matching

15:40

supply with demand . So

15:43

, essentially , the way Akash works for folks

15:45

that are not familiar with it is that we're

15:47

essentially a two-sided marketplace . On

15:49

the one side , you have supply , which

15:51

is compute supply , whether it's

15:53

regular compute or accelerated compute in the form

15:56

of GPUs . Now All

15:58

of this supply is

16:01

available on the network in terms of individual

16:03

providers , so a single provider can

16:05

have a single server , they can have 10,000

16:07

servers , they can have 100,000 servers

16:09

any number and

16:11

each of these providers are

16:13

independent entities and no

16:15

single person owns the entire infrastructure . So

16:18

, even though Overclock Labs is the

16:20

company or the organization that created our cash

16:23

network , we don't own all the infrastructure

16:25

. We own a teeny , tiny portion of it . We're one of the

16:27

providers on the network , and there's over

16:29

75 of those providers today . And

16:32

then on the other side of this is people that want to

16:34

deploy applications onto

16:37

that compute infrastructure , and

16:39

the way the matching of these two sides

16:41

happens is through a blockchain , and the

16:43

reason we use a blockchain for that is because number one

16:45

, it

16:51

lets us be able to do this in a very automated fashion , so being able to easily create a smart

16:53

contract between somebody that wants a certain resource and somebody that has that

16:55

resource to give can be done very easily in

16:57

blockchains , using smart contracts or using

16:59

programmatic ways , and so that's what

17:01

we've implemented is a two-sided marketplace

17:04

where you can get the best possible

17:06

resource in terms of price

17:08

and performance for the workload that you want to

17:10

run . And so , given that

17:12

we have a crypto background , we have

17:14

a natural affinity or we have a good portion

17:16

of our community , that is consists

17:18

of people that have had GPU mining

17:20

equipment mining equipment so

17:22

if you look back to you know 2017

17:25

, 2018 , 2019 , 2020

17:27

as well similar to how

17:29

NVIDIA has seen a huge boost

17:31

from AI workloads in the last one year , two

17:33

years prior

17:41

to that , the prior inflection point that NVIDIA had was from GPU mining . Some of the people that

17:43

were around in the GPU space then would probably remember that and so

17:45

there's a whole bunch of GPU capacity sitting in

17:48

miners even now , whether it

17:50

was for Bitcoin mining or it was previously for

17:52

Ethereum mining or any others , and

17:54

a lot of these chains

17:57

have either transitioned away from proof of

17:59

work type blockchains to proof of stake

18:01

or , in case of Bitcoin , it's

18:03

getting more and more expensive , with each having to

18:06

be able to mine Bitcoin . So there

18:08

is , as a result , a lot of GPU capacity that's out

18:10

there that you know

18:12

. People have already invested the money into

18:14

that they

18:17

would love to monetize , and so

18:19

, while those GPUs

18:21

may not be the most latest

18:23

and the greatest GPUs , given

18:25

that they've been around for four , five , six years

18:27

, they still

18:29

serve in many cases to

18:32

be a really good platform for being able to do inference

18:34

. So , while you may not be able to train

18:37

the largest of the models on these

18:39

older GPUs , many of them work

18:41

really great for inference . For example

18:43

, one of the most common GPUs

18:45

that we get requests for

18:48

inference today is the RTX

18:50

4090 , believe it or not and

18:52

what people have found is that the

18:54

price to performance ratio of an

18:57

RTX 4090 is really good

18:59

when you're trying to do basic inference , whether

19:01

it's running something

19:03

like LAMA or

19:09

LL for language responses as a natural language

19:11

processing engine . You

19:15

know wanting to do image

19:17

generation using stable

19:20

diffusion or any of the other image

19:22

generation models out there , they work as

19:24

a really good platform for that type of stuff . So

19:28

that's where sort of you know us being able to match all

19:30

of this demand from the crypto and mining

19:32

communities towards people that want to do

19:34

small language models or just

19:36

pure inference on models

19:39

with fewer parameters . It works great . Now

19:42

, when you think about the higher-end GPUs , which

19:44

is primarily people that want to be able

19:47

to run models with

19:49

tens of billions of parameters or

19:52

want to be able to do large-scale training

19:55

, what we have found here

19:58

is that we are able to actually

20:00

bring in crypto-driven incentives . So

20:02

we have the concept of a community pool

20:04

within our

20:06

protocol that has

20:09

several million dollars of

20:11

money available for

20:13

us to deploy as part of community

20:16

incentives , and so what we're able to do is

20:18

we're able to actually source a lot , lot

20:20

of these iron gpus as well and

20:22

offer them at a significantly competitive

20:24

price relative to anybody else that's on out

20:26

of the market . So , for example , if you were

20:28

to go to akashnetwork gpu

20:31

right now , what you would find is um

20:34

that you can get a h100

20:36

with an sxm interface , but

20:39

as low as a dollar and50 , which

20:41

I believe is half or less than half

20:44

of what you can get at many of the other places out there

20:46

. So I hope that answers

20:48

some of the questions that you have .

20:50

Yeah , yeah , that definitely answers my

20:52

questions and brings in more questions

20:55

for me , actually , that I was thinking while

20:57

you were talking about this

20:59

. So I have used a cost service in the past

21:01

and it's it's amazing in terms of I

21:04

hosted a , hosted a

21:06

blog also , so everything is kind of containerized

21:08

. They're nice templates , it's very easy

21:10

to use and I was going to get to

21:12

the ease of use for non-crypto

21:14

native users . So that

21:17

was like a year ago and now things

21:19

might have improved even more . So

21:21

any improvements there in

21:23

terms of , like , how the GPUs

21:25

or the GPU marketplace works

21:27

, because that's relatively new right ?

21:29

Yeah , great question . So , yeah

21:32

, so the GPU marketplace was launched

21:34

. I mean time flies right . So

21:36

we actually launched the GPU marketplace in beta

21:38

, I think around June of last year , may to

21:40

June , may , june last year , may 23 . And

21:43

then , you know , ga'd it , I think a month

21:45

or two after that . So it's been around for

21:47

about six or seven months now , but

21:49

, yeah , we're quickly coming up on almost a year

21:51

. Yeah , so

21:54

, from the perspective of being

21:56

able to use GPUs or request GPU

21:58

resources on the network , the way

22:00

we have implemented GPU support

22:02

is to match it exactly the way

22:04

regular CPU resources work

22:06

, and so any sort of deployments

22:09

that you did on regular

22:11

non-accelerated compute I

22:13

don't know how long ago that was , maybe a year

22:15

ago or so or maybe two years ago you'll

22:19

find that the deployment workflow is exactly

22:21

the same even with GPUs . So , just

22:23

like how you can write this thing called

22:25

a stack definition language file , or an SDL

22:27

file , as we refer to it , which is effectively

22:30

like a Docker compose file for

22:32

those that are infrastructure nerds listening

22:34

to this and what you do

22:37

there is you basically say , hey , these

22:39

are the services that I want to be running , and

22:41

a service could be a backend service . It could be a frontend

22:43

service , it could be a machine

22:45

learning workload , it could be an inference app , whatever

22:48

you like , and

22:50

so you can have multiple of these services specified

22:52

inside that file , and then , for each of the services

22:54

, you specify something called a compute profile

22:56

, which is basically saying these

22:59

are the resources , or this is the amount of resources that

23:01

I think the service is going to need

23:03

in order to operate . So the compute

23:06

profile typically is you know , I need six

23:08

CPUs , I need one GPU , I

23:11

need a gigabyte of RAM

23:13

, I need , you know , two gigabytes of storage

23:15

. So you specify all these

23:17

things and then submit this job

23:19

onto the network , and then what you get back is

23:21

a whole bunch of bids from various providers

23:23

. Each bid typically consists of , you

23:26

know , information about

23:28

the provider , where is it located , what's

23:30

been the uptime on the provider for the last seven days

23:32

, what's the

23:34

name of the provider and then

23:37

, of course , what is the cost . So

23:39

what is this provider going to charge you for

23:41

running this workload for one month ? And

23:45

so you get all these bids back , and then you go

23:47

ahead and accept one of the bids and then , the moment

23:49

you do that , the workload gets

23:51

deployed onto the specific provider and

23:53

what you get back is an endpoint that

23:56

gives you access into the container running

23:58

container instance , and

24:00

if you expose certain ports , then

24:04

you know . If one of those ports happens to be a port 80 or 443 , then

24:06

you have essentially a HTTP you

24:08

know interface into that as well . So the

24:11

entire workflow is exactly the same as what it was with

24:13

CPUs . Nothing's changed , so

24:15

that should be totally familiar if you go try that . The

24:18

other aspect of that which you asked about

24:21

was how do we make

24:23

it easy for non-crypto people to be

24:25

able to access this ? And that's a really good question because

24:28

obviously

24:30

a majority , a big share of the AI

24:32

workloads today are being

24:34

built and deployed by folks

24:36

that are not crypto-native right , and

24:39

so , to that end , there's a few things

24:41

that are ongoing within

24:43

Akash and within Overclock Labs . First

24:47

and foremost , as you probably

24:49

know from past conversations and from following Akash

24:51

, we have a fairly vibrant ecosystem

24:54

and a fairly vibrant community . So

24:57

one aspect of our community is

24:59

that there is a bunch of people that are

25:01

actually building solutions

25:03

on top of Akash . So , similar

25:05

to how you know , when AWS and Azure

25:07

and all of these services took off , you

25:10

had a bunch of people building you know monitoring

25:12

solutions , building things

25:14

like Roku or you know Vercel

25:17

or these kinds of things that utilize AWS

25:19

compute underneath , or

25:21

Vercel or these kinds of things that utilize AWS compute underneath

25:24

. There is a bunch of teams that are building similar solutions to utilize Akash compute

25:26

underneath . In fact , one of those teams the

25:30

name of the team was CloudMOS . They were

25:32

called CloudMOS because they're

25:34

essentially built on the Cosmos or

25:37

they're part of the Cosmos ecosystem

25:39

, and they were primarily targeting

25:41

Akash compute

25:43

as the platform that they would

25:45

build on top of . They call themselves CloudMOS

25:47

. That team was actually

25:49

acquired by Oracle Oclabs about seven

25:52

or eight months ago and they're actually part of our team now , and

25:55

so they built this client that takes

25:58

you know our basic APIs and our CLI

26:00

and implements essentially a UI

26:02

on top of that to make it easy to deploy . And

26:06

so now that those folks are part

26:08

of our team , we've rebranded

26:10

that to consoleakashnetwork

26:13

. If you go to consoleakashnetwork , what

26:15

you'll see is what looks like a

26:17

simpler version of AWS console , but

26:19

specifically for Akash . So that's already

26:22

there . So you can check out consoleakashnetwork

26:24

and you can see what that looks like . What

26:26

you will see in the next few months

26:29

is us work on , you know , more

26:31

curated workflows for AI there's

26:33

already like a bunch of templates out there , but even better

26:35

curated workflows and also

26:38

potentially look at offering

26:40

a credit card based interface and not just a

26:42

crypto and a wallet driven interface as well . So

26:45

that's one aspect of it . The second aspect of it

26:48

is there is other teams out there , so there's a team called

26:50

spheron that has built a

26:52

ui app that already has a credit

26:54

card based interface that can

26:56

be utilized for deployments and

26:58

then separate from teams that are directly building

27:00

on us . We're also in

27:02

talks with you know

27:04

, partnerships talks with certain Web2

27:06

companies . So these are companies that have

27:09

built essentially AI

27:11

inference platforms , right ? So

27:13

these are companies that are built like a UI

27:16

and API layer that

27:18

allows people to

27:21

be able to utilize open source

27:23

models and abstracts away

27:25

all the infrastructure components from

27:28

that whole experience , right ? So whether you're utilizing

27:30

AWS underneath or Azure underneath or

27:32

Akash underneath , all of that is abstracted away

27:34

and what you , as a data scientist or

27:36

a machine learning engineer , get is you get

27:39

this API interface or UI

27:41

interface where you can just say , hey , this

27:44

is the model I'd like to run , I

27:46

would like to run it really fast , or I'd like to run

27:48

it medium or slow and

27:52

either run the model right now and give me the outputs or

27:56

give me a programmatic interface where I

27:58

can request that the model

28:00

be run with this data set and

28:02

with these parameters so I can tune

28:04

the model as well . So we have several

28:07

talks with companies that you'll be hearing about in the

28:09

next few months that are Web2

28:11

companies that have built these kinds of platforms

28:13

that are going to be using Akash computer .

28:16

Yeah , so you're fully realizing the

28:18

SkyCloud concept looks , looks like you

28:21

know , with this uh full realization of

28:23

that , where you can define

28:25

your uh compute parameters and then

28:27

it uh does what it does in the background

28:30

and as you , as

28:32

you described , like the fast , slow and

28:34

you know it depends upon the type of jobs

28:36

you're running , right and time and the time of the day . Then

28:39

you can have that price selectivity

28:41

as well . So it sounds fantastic

28:43

.

28:44

Yep , amazing . The flexibility of

28:46

Akash , I think , is that it

28:48

lets you not just be able

28:50

to choose the kinds of compute resources

28:53

and make the tradeoffs between price to performance

28:55

that is

28:57

applicable to your specific application performance

29:02

that is applicable to your specific application but it also gives you the option of choosing to be

29:04

as decentralized or not decentralized as you want to be . So let's

29:06

say , you use Akash for a

29:08

few months and you decide that these three

29:11

of the 75 providers are the ones that I like the most

29:13

and those are the only ones I want to be deploying

29:15

to . You can programmatically

29:17

set it up so that you always default

29:20

to those providers . Or , if you're

29:22

somebody that is a completely you

29:24

know you're a hardcore decentralization

29:26

fan , or

29:28

you know

29:31

you can be choosing a different provider

29:33

every single day and build your application

29:35

to do that . So that flexibility

29:37

in being able to decide what path you want to choose

29:39

is essentially what I think makes Akash really

29:42

unique , and so this brings me to like

29:44

, like a bigger , bigger

29:46

question .

29:48

Akash was talking about decentralized

29:50

cloud before all this deep

29:53

in narrative , right . So we've

29:55

, like , I had my interview with with Greg almost

29:57

a year , I had my interview with Greg almost a year , two

29:59

years ago , and so we were talking

30:01

about these things , and so where

30:03

do you see the conversions

30:05

now of AI and

30:08

crypto , like in the broader scheme of

30:10

things ?

30:11

Yeah , that's been a really hot topic

30:13

for the last few months , hasn't it ? So

30:16

you're absolutely right . Basically , what

30:18

we have seen , at least in the last several months , is

30:20

something that we have

30:23

been passionate about for several years now . Greg

30:25

and adam , way longer than

30:27

I have this idea of

30:30

decentralizing the

30:32

infrastructure , or the compute infrastructure

30:35

and the cloud , which you know in many

30:37

ways , is a public utility at this point . That's

30:40

something that really has taken on

30:42

a narrative for this specific crypto

30:44

cycle coming up , and so , as

30:46

with all narratives you know , similar to you know , in

30:48

the last crypto cycle , DeFi

30:51

and NFTs and you

30:53

know a few other things were pretty

30:55

hot and everybody wanted to jump on them . You have a bunch

30:57

of people trying to jump on this decentralized

31:00

physical infrastructure narrative

31:03

or the deep end narrative now , and

31:06

a bunch of people trying to claim that they

31:08

are quote unquote decentralized compute marketplaces

31:10

. What's

31:12

been interesting to watch is that many of

31:14

these projects actually don't in

31:17

the absolute worst case , some of them don't have an

31:19

actual product underneath and they're just

31:21

talking about things that you know

31:23

, in many ways are just copying messages

31:25

from uh projects like akash

31:27

and others that have been at this for several

31:29

years now in the , and that's

31:31

sort of in the worst case scenario where they don't really have

31:33

I haven't really built anything , but they're just talking about

31:36

it . And then , in the best case scenario

31:38

, is projects that have

31:40

legitimately built something but

31:43

they've not taken the effort to truly

31:46

think about decentralization at the core . So

31:48

they may have gone and acquired

31:51

compute from one or two or three

31:53

sources and then offering

31:55

that as a decentralized solution . You

31:57

know , that's not the true definition of decentralization

32:00

. That's just taking a regular

32:02

good old approach of

32:04

you know going and sourcing compute , but just sourcing it from multiple

32:07

sources yourself , right ? So

32:09

I think that's not . It's not I'm not saying

32:11

it's a bad solution . It works

32:13

. It's better than the first one , which is

32:15

just claiming things when you don't really have anything

32:18

, but it's also not really decentralized

32:20

. What's also

32:22

interesting to see about a lot of these solutions

32:24

is that they're

32:26

all closed source , so they

32:29

don't open up . They definitely

32:31

don't open up the source code for others to look at

32:33

, similar to what Akash has done but

32:37

they don't even open up their metrics

32:39

. In case of Akash , you can actually go to a

32:41

web page called statsakashnetwork

32:44

. What this is is basically

32:46

it shows you all the statistics of

32:48

things that are happening on Akash

32:50

. Every single second , every single

32:52

minute , Every time a block is created . What

32:56

you can see there is you can see the total number of providers on

32:58

the network . You can see the total amount of compute in

33:00

terms of GPU , CPU storage memory . You

33:03

can see the total number of

33:05

leases being created . A lease is basically

33:07

when one workload

33:09

gets deployed onto one provider . That's

33:11

typically a lease , so it's like one application

33:14

being deployed and you can

33:16

see the total number of compute

33:18

resources being spent , amount of

33:20

money being spent on the network , and all

33:22

of these metrics are basically stored

33:24

on the blockchain . So

33:29

it's not something that we as overclock labs or as anybody in the

33:31

community , can go and sort of spoof

33:33

or mess with or fake

33:35

in any way , because anybody

33:37

can query these parameters from the blockchain and

33:40

prove us wrong if we try to do that or fake

33:42

in any way , because anybody can query these parameters from the blockchain

33:44

and Roo was wrong if we try to do that . So

33:47

in that sense , I don't know of any other project out there other than

33:50

Akash that is fully open source , fully decentralized

33:53

and exposes all of its statistics on

33:56

a blockchain for anyone to query within

33:59

the compute deep end marketplace

34:01

. So I don't know of any other compute

34:03

deep end project that is doing

34:05

those three things , and if there is , then I would

34:07

love to learn about it .

34:10

Yeah , well said , I myself

34:13

haven't , even though I've interviewed

34:15

some folks in

34:17

competing with you guys or in

34:20

healthy competition , but

34:22

I haven't seen this clear

34:24

statistics from anyone so far , so

34:27

that's

34:29

good to see . So , as far as the convergence

34:32

of AI and crypto goes , I mean , clearly

34:35

there is one solid

34:37

use case that Akash is building for

34:39

, which is providing GPU accessibility

34:42

, which is at the inference

34:44

level as

34:46

well models

34:50

, and you have more efficient

34:52

models coming , and then you have

34:54

inferencing getting

34:56

better on on commodity hardware

34:58

. You know you can definitely see that uh

35:01

utilization of gpus even going

35:03

higher . Right , I'm currently looking

35:06

at the stats and I do see a lot

35:08

of utilization happening here month

35:10

over month , so that's good to see

35:12

. Okay , one question

35:15

that's uh

35:17

, one of the last

35:19

questions is and

35:21

I've started doing this with my speakers

35:24

is um

35:26

, what advice would

35:28

you give to somebody who comes next

35:30

on my show and and this

35:33

is you could say

35:35

regarding something in the crypto

35:38

sphere that you have learned so far ?

35:41

And actually I , just before I answer that , I just realized

35:44

I didn't answer the previous question completely , so

35:46

I'll just quickly answer that as well . I

35:48

didn't touch quite on the AI crypto narrative

35:51

, so I talked a lot about , you know

35:53

, the new projects coming up and how Akash

35:55

potentially is different from those , but

35:57

really the AI versus AI

35:59

across crypto narrative view , if you

36:02

want to call it . That makes

36:04

complete sense to me , because one

36:06

of the biggest things that people talk about in the

36:08

non-crypto world today with

36:10

regards to AI is how AI

36:12

is being controlled by a handful of companies , right

36:15

? So there is this huge outcry

36:17

among a lot of people that you

36:19

know a few companies have enough

36:22

compute capacity and

36:25

are capable of acquiring a lot of compute capacity

36:27

into the future , and these

36:29

are the companies that are going to be able to train the

36:31

best models , run the best models and all of that

36:33

. And I think this

36:35

is where crypto really makes

36:37

sense to me , because it's

36:40

the one way that we can build systems

36:42

in the open , allow

36:44

easy or programmatic

36:46

aggregation of capacity compute

36:48

capacity , the way Akash is doing and

36:51

be able to crowdsource

36:53

the development of not just

36:56

the development of models but also , you know , the

36:58

accessibility to models as well as compute

37:01

in an open fashion , and

37:03

so being able to do this with

37:05

crypto is a lot

37:07

more easier to make programmatic

37:09

and a lot more easier to make you know

37:11

sort of source from a community or crowdsource

37:14

than it is to do without crypto , and that's

37:16

why that thing makes complete sense to me . Now

37:18

to answer your last question , which is you know what sort

37:20

of advice I might have for the next person that comes along

37:22

, I

37:24

think the biggest advice I would give

37:26

to someone and this is coming from

37:28

me as someone who was

37:31

not in crypto before I started working

37:33

on Akash and joined Overclock Labs is

37:37

when you think about a crypto project . I

37:39

know there's a lot of people out there that build

37:41

crypto projects with

37:43

the pure intention of shilling a token

37:45

or making a quick buck

37:47

and calling it a day . I think

37:50

it'd be really nice to see more people think

37:52

about the real utility of crypto and

37:55

how it can be applied specifically

37:57

to areas of our

37:59

life that require

38:01

decentralization or require

38:04

things that need

38:06

an incentive mechanism to

38:09

make them more like a public utility , without

38:12

actually making them a public utility

38:14

in the sense of , you know , making

38:16

a fixed cost or a fixed price and no competition . So

38:19

I think what crypto is really good at is being

38:21

able to sustain

38:23

innovation while

38:25

, at the same time , leveling

38:28

the playing field and giving people access to technology

38:31

that otherwise would not have access to them , while

38:33

at the same time , you know , allowing entrepreneurs

38:35

to be able to generate wealth , retain

38:38

value or capture a certain portion of value that

38:40

they create , without having to get

38:42

democratized completely or without

38:44

having to get commoditized completely

38:46

. So I think that's kind of my overall advice

38:49

is to think of solutions that could

38:51

really uniquely be solved only with crypto

38:53

, as opposed to being solved by a

38:55

web to solution , and just do crypto for the sake of it .

38:58

Great Thanks , anil

39:00

, chatting with you and folks

39:03

here listening . Where can they , where

39:06

can they find you ?

39:07

Yeah , so , akash , you can find us at

39:09

akashnetwork on the web . And

39:12

then I'm on Twitter . My handle

39:14

is underscore Anil underscore

39:16

Murti underscore . And

39:20

then I'm also on LinkedIn and the usual

39:22

places that you'll find someone Awesome .

39:24

Thank you .

39:25

Thanks you .