Every time when we say AI is making its mark in every sector, we also talk about a whole ecosystem working at the backend. With an AI ecosystem, we mean having a suitable landscape where AI systems are boosted with skills, governance, and the right technical support. For the success of these AI ecosystems, we need innovative ideas to boost the encouragement and involvement of more and more people. We need assistance and support to nurture ideas at every stage, even if they are at a nascent stage.

With more and more people moving towards exploring and working on AI models, the need for having a workstation that can support these AI models is increasing. Unfortunately, AI requires a GPU-extensive infrastructure which can be very difficult and expensive to procure and requires high bandwidth and power supply. To help ease this burden, Jarvislabs.ai provides virtual GPU machines to cater to all deep learning workloads.

With a global semiconductor shortage, importing hardware has become extremely complex, and the prices have skyrocketed. JarvisCloud by Jarvislabs.ai spins up instances securely with Ju-pyterLab, SSH access, and your favourite libraries in few clicks on modern GPUs like A100, A6000. Using the algorithm, JarvisCloud spins up the instance in the most cost-efficient way. This proves to be a game-changer to small and mid-size enterprises who not just have to pay for the hosting of these instances on AWS or GCP or Azure, but also to DevOps to maintain and optimize these instances for better cost management. 

According to Vishnu Subramanian, CEO Founder Jarvislabs, “A small company in a small town in India doesn’t need to the pay the same fee to AWS as a fortune 500 company pays. Hence we are catering to a small segment which doesn’t need a big complex bundle of services.”

With JarvisCloud, you can set up deep learning instances in under 30 seconds, scale up to cater to GPU performance needs and speed model training, pause, and resume the workload while paying as you go. It supports Nvidia A100, A6000, Quadro RTX6000/5000. It supports multiple frameworks like PyTorch, Fastai, Tensorflow and can also install a variety of commonly used libraries like git, Wget, GCC, Pandas, NumPy, sci-kit-learn, matplotlib, etc. Once the instance is launched, an IDE such as PyCharm or Visual Studio Code can be connected seamlessly without any effort. 

One of the big challenges is, of course, maintaining the data centre as data centres for GPU re-quire 2X-4X power supply than the normal data centres. Jarvislabs.ai maintains all its data centres within India which make it cost-effective. This is also in line with the government's Hyperscale Data Centres Scheme, which plans to provide between 3% and 4% of capital investment as an incentive to companies, along with real estate support and faster clearances. This certainly will pave a path towards faster cloud adoption and data localization.

Vishnu believes, “Our generation should focus on contributing to the tech space more; as India still holds a small share in terms of producing tech products that we need and depends on ready-made products.”

The company also encounters a question many times; are you directly competing with big players like AWS, Google Cloud, and Azure. To which the company says that they are focusing on an altogether clientele which is small or medium scale enterprises that are welcoming AI in their lines of works. Big players in the market are providing a whole bunch or bouquet of services which might not always be required for people just starting, or experimenting, or say learning. Jarvis-Cloud will help AI enthusiasts, students, people learning AI, and startups who want to adopt AI. The idea is to provide an affordable platform to people who need to build upon their business without any complexity. 

Moreover, the requirements for all players in the market aren't the same, so there is a need for some customized, scalable, and simple setup. With the GPU market being monopolistic in nature with NVIDIA being the prime player, there is an underlying need for the governments to invest more around achieving both hardware and software expertise to fuel AI revolution in the country. 

Such endeavours set the tone right for people to delve and research around AI, which is a must right now and is going to be non-negotiable in times to come. AI has always been perceived as an expensive technology, and hence high setup costs are associated with it. However, if we wish to encourage more involvement of students, academia, and researchers in AI, we must deal with these issues as early as possible.