According to industry experts, small companies are key in improving access to graphic processing units (GPUs) in India, especially in cloud computing and AI infrastructure.

Neysa Velocis, based in Mumbai, helps scale AI projects using GPU infrastructure and has a large inventory of thousands of GPUs.

NeevCloud, a new firm from Indore, plans to deploy 40,000 GPUs to offer affordable and accessible resources for AI researchers and developers.

Based in Bengaluru, Jarvis Labs has access to over 1,000 GPUs through partners and its own capacity in India.

These companies often provide better local support and faster response times, which are important for businesses in India.

Setting up and maintaining GPU infrastructure can be costly, especially for smaller companies, and the market is becoming more competitive.

The growing demand for AI and machine learning in India presents a big opportunity for small companies to succeed.

Karan Kirpalani, Chief Product Officer at Neysa, mentioned that the company will soon update its GPU inventory to stay modern and relevant.

Neysa’s clients in the private sector include businesses from financial services, insurance, education, media, entertainment, pharma, life sciences, and IT/ITeS.

Neysa Velocis offers GPU-based AI services like training, fine-tuning, and inferencing, focusing on cost efficiency and ease of use. The company has raised $50 million and works with NTT Data Corporation’s data centres in India. Since launching in October, it has onboarded 12 clients, including Indian and global multinationals.

Neysa stands out due to its intellectual property and cost-effective design, with pricing 40-70% lower than hyper scalers, offering better return on investment and lower total cost of ownership. It is also involved in the India AI mission, using partnerships to scale its services.

Kirpalani noted that India’s market needs more support and customised solutions to help clients achieve their AI goals. He emphasised the value of local players who understand the market better and pointed out that clients focus more on GPU performance and cost than on the specific model.

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