Yotta eyes expansion, plans to double GPU capacity with Nvidia by 2025

Data centre operator Yotta is planning to expand its computing capacity to 32,000 graphic processor units (GPUs) from Nvidia by March 2025, months after placing orders for 16,000 high-powered GPUs from the US-based chip giant, the company CEO said.

Nvidia’s most advanced chips are essential for training large language models and for the development of artificial intelligence (AI), a technology that has set off a frenzy in industries worldwide.

“We have a soft commitment from Nvidia, and these chips will be delivered to us by 2025,” said Sunil Gupta, CEO, Yotta Data Services. The company has already invested around $1 billion to procure 16,000 H100 GPUs and develop infrastructure for servers, including associated networks, storage, software, and additional layers.

Gupta did not divulge fund details for the additional chips. He, however, said the capital requirements for the project will be backed by the promoters and they may also look for financing from banks.

The Hiranandani Group-backed company’s first GPU-based data centre, which is expected to go live by May 15, 2024, will provide computing capacity for AI processing, powered by the first batch of 4,096 GPUs it has received.

The market for high-performance computing capabilities in India is expected to grow big, Gupta said, adding that it is still early days for the sector in the country.

On the demand front for GPU servers, he said Yotta’s first slot of more than 4,000 GPUs was already booked by enterprises and was expected to go live by May 15, 2024.

He also said that apart from serving India, the company was also looking to cater to the global demand.

“We can serve not only our respective country but also the nearby geographies. So our growth pattern in terms of sourcing GPUs possibly will not stop, simply because we are just starting,” he said.

Gupta also said that because of the global shortage of GPUs, there was a demand from European regions as well. “I can see the demand going up globally in the last four to five months, and a bigger part of my sales funnel today is actually requirements from Europe, the Asia-Pacific region, and from West Asia,” he added.

The company is aiming to compete with hyperscalers like Amazon and Meta by providing GPU servers at a competitive price of $2- $2.5 per hour. Contrasting it with the global rates of GPU servers, Gupta said the company was looking at making money through the right pricing.

“If you go to hyperscalers today, which many of the startups have done, you will get GPUs at somewhere between $9 to $12 per hour. Similarly, in the US, for specialised GPU providers like Corebeam and Lambda, the prices range between $3 to $5. My price point for the same thing is between $2 to $2.5,” Gupta said.

Pricing can vary depending on the contract, he said, adding that a short-term GPU usage might cost more per hour or week, whereas committing to a longer contract with upfront payments could bring the price down to as low as $1.8 per hour of usage.

“For the next year or so, the GPU subscription would be taken by entities such as startups, or research labs – who wanted to train AI models,” he added.

He also highlighted the need for ‘intermediaries’ between the data servers and enterprises that can help the companies understand the use cases and changes that AI can bring to the organisation.

“We require companies like Accenture, Deloitte, and others to engage with enterprises, grasp their business situations and challenges, and demonstrate how AI could potentially boost their productivity. Subsequently, they can identify use cases, utilise pre-trained models, train them using enterprise data to tailor specific models for each enterprise, and integrate these models into their existing applications,” he said. Business Standard