The increasing demand for GPU cloud computing needed by AI service providers and may have found a solution in this Canadian startup that promises lower prices at the same level of performance.

Thanks to the use of GPUs and parallel computing AI have finally become viable.

According to Forbes, cloud computing service providers are “racing to invest in GPU-enabled virtual machines”. The reason is that the increasing adoption of AI creates an ever increasing need for high-fidelity GPU based computing power and what the space can offer as of right now is expensive. Very expensive.

An unexpected player

In this situation, Nebula AI — a startup from Montreal — is trying to solve this issue by launching a Orion, a service that can be described as the Uber for GPUs. The Nebula AI platform — which was officially launched on September 28th, lets users around the world sell their computing power. Such an approach can potentially “spawn” a GPU cloud computing infrastructure much faster than what is possible by building big data centers like most competitors do.

Another consequence of such a design is that — just like in the case of Uber — the prices of this service are lower when compared to GPU servers rental for deep learning or rendering.

The system’s design

Nebula’s platform works in a completely different way than its competitors. While centralized data centers are a part of the network, anybody can build a rig and start selling computing power. The system is designed in such a way that GPU owners can contribute to the network with their machines.

Also, the NBAI system doesn’t only provide computing power. It also hosts AI apps that respect the privacy of the users. The data storage is both encrypted and decentralized to ensure that not even the company that created the system can access the user data.

AI is shaping the semiconductors of the future

The demand for such services is so high that it is currently shaping the GPU industry as a whole with chips being designed and produced with this sole purpose in mind. Nvidia — the current market leader in the GPU industry — has reportedly recently upped its game in this space.

The recent Tesla T4 GPU — based on its new Turing architecture — is specifically designed for the acceleration speech synthesis, translation, recommendation systems, image and speech recognition. This new compact card is meant for AI data centers more than proves that AI is now a driving force to be dealt with in the semiconductors industry.

Further proof is constituted by the amount of effort that is being currently put into designing and shipping new AI specific chips. Not only Nvidia but also Intel, IBM, Bitmain and many others are presently developing silicon meant explicitly for such applications. Chips for machine learning is a new arms race.

The current state of AI

AI is starting to be ever more present in our daily lives, but nearly everyone in the industry feels sure that it is going to be pretty much ubiquitous sooner than most could anticipate.

While talking about what AI will be capable of in the distant future could be interesting and — why not — fun, the scope of this article is to present only hard facts, so let’s describe the current state of the art.

Even if little or no progress in the field was to be made — and a lot is being made — AI still has the potential to grow and become much more present into our lives than it currently is.

Even something simple like a traffic light can be enhanced by AI, which — according to tests — reduces the wait times by 40%. AI also does other things that most tech-oriented people know of, like catching spam sent to your email, recognizing faces in Facebook photos, but there is a lot more than just that to the current AI.

In a recently released paper, an AI has made possible a robot that — without any expert supervision — can learn to do things like tying a knot or navigate an office. If you aren’t easily impressed, then how about a humanoid robot that is capable of performing a backflip?

It wasn’t a long time ago that humanoid robots were barely capable of staying upright — actually, they weren’t — , and now, in a big part thanks to machine learning advances, they can do things that most people aren’t able to do (like backflips, for instance). So there is little doubt that AI will be capable of a lot more than calling to get your haircut appointment.