INSUBCONTINENT EXCLUSIVE:
Riding on a wave of an explosion in the use of machine learning to power, well, just about everything is the emergence of GPUs as one of the
go-to methods to handle all the processing for those operations.But getting access to those GPUs — whether using the cards themselves or
possibly through something like AWS — might still be too difficult or too expensive for some companies or research teams
SoDavit Buniatyan and his co-founders decided to start Snark AI, which helps companies rent GPUs that aren&t in use across a distributed
network of companies that just have them sitting there, rather than through a service like Amazon
While the larger cloud providers offer similar access to GPUs,Buniatyan hope is that it&ll be attractive enough to companies and developers
to tap a different network if they can lower that barrier to entry
The company is launching out of Y Combinator Summer 2018 class.&We bet on that there will always be a gap between mining and AWS or Google
Cloud prices,&Buniatyan said
&If the mining will be [more profitable than the cost of running a GPU], anyone can get into AWS and do mining and be profitable
We&re building a distributed cloud computing platform for clients that can easily access the resources there but are not used.&The startup
works with companies with a lot of spare GPUs that aren&t in use, such as gaming cloud companies or crypto mining companies
Teams that need GPUs for training their machine learning models get access to the raw hardware, while teams that just need those GPUs to
handle inference get access to them through a set of APIs
There a distinction between the two because they are two sides to machine learning — the former building the model that the latter uses to
execute some task, like image or speech recognition
When the GPUs are idle, they run mining to pay the hardware providers, and Snark AI also offers the capability to both mine and run deep
learning inference on a piece of hardware simultaneously, Buniatyan said.Snark AI matches the proper amount of GPU power to whatever a team
needs, and then deploys it across a network of distributed idle cards that companies have in various data centers
It one way to potentially reduce the cost of that GPU over time, which may be a substantial investment initially but get a return over time
If that the case, it may also encourage more companies to sign up with a network like this — Snark AI or otherwise — and deploy similar
cards.There also an emerging trend of specialized chips that focus on machine learning or inference, which look to reduce the cost, power
consumption or space requirements of machine learning tasks
That ecosystem of startups, like Cerebras Systems, Mythic, Graphcore or any of the other well-funded startups, all potentially have a shot
at unseating GPUs for machine learning tasks
There also the emergence of ASICs, customized chips that are better suited to tasks like crypto mining, which could fracture an ecosystem
like this — especially if the larger cloud providers decide to build or deploy something similar (such as Google TPU)
But this also means that there room to potentially create some new interface layer that can snap up all the leftovers for tasks that
companies might need, but don&t necessarily need bleeding-edge technology like that from those startups.There always going to be the same
argument that was made for Dropbox prior to its significant focus on enterprises and collaboration: the price falls dramatically as it
becomes more commoditized
That might be especially true for companies like Amazon and Google, which have already run that playbook, and could leverage their dominance
in cloud computing to put a significant amount of pressure on a third-party network like Snark AI
Google also has the ability to build proprietary hardware like the TPU for specialized operations
ButBuniatyan said the company focus on being able to juggle inference and mining, in addition to keeping that cost low for idle GPUs of
companies that are just looking to deploy, should keep it viable, even amid a changing ecosystem that focusing on machine learning.
