NVIDIA Wants To Solve India’s GPU Compute Problem, But At What Cost?

Chip giant NVIDIA is all set to change the GPU game across its major markets, and India is no exception.

It plans to do this by solving the capital constraint for AI cloud providers, or neoclouds. Under its new global revenue-sharing financing model, AI cloud companies that offer GPU-as-a-service can now procure NVIDIA’s infrastructure through a revenue-sharing and credit-support model. 

The model is designed to make it easier for AI cloud providers with limited financial muscle to finance GPU infrastructure by combining revenue sharing with credit support. In return, NVIDIA will make money both by selling its GPUs and taking a share of the revenue earned from the cloud services running on them. 

For Indian neoclouds, such as Yotta Data Services, NxtGen, NeevCloud, E2E Networks and Neysa, the announcement comes at an opportune time. The reliance of Indian neocloud providers on NVIDIA GPUs is increasing by the day. As of February 2025, the compute pool under the IndiaAI Mission comprised around 18,700 GPUs, of which nearly 14,500 were NVIDIA chips alone. 

While NVIDIA’s new model does solve the biggest constraint for Indian startups (capital), it also comes at the cost of deepening the chipmaker’s influence over the country’s AI infrastructure.

The chipmaker is yet to disclose details of the new financing model it launched earlier this month. The story will be updated when NVIDIA responds.

Are Indian Neoclouds Happy?

NVIDIA’s new programme is designed to make it easier for AI cloud providers to raise money for expensive GPU infrastructure through a credit system. As per industry experts and analysts, NVIDIA is expected to offer backstop financing under which a guaranteed rate for cloud providers’ unsold GPU capacity will be offered in exchange for a share of their cloud revenues. 

With this, NVIDIA hopes to help AI cloud companies secure financing for its GPUs, expand faster and make AI compute more widely available beyond a handful of hyperscalers and large AI labs.

AI cloud requires billions of dollars in upfront investment. This challenge is even greater in India, where GPUs are imported and paid for in US dollars, while cloud providers earn revenue gradually in Indian rupees. Globally, companies overcome this by raising debt against their GPUs and long-term customer contracts with companies like Microsoft and OpenAI. Indian AI cloud providers, however, lack such large anchor customers, and domestic lenders remain cautious about financing GPU infrastructure.

Instead of relying on offshore partners alone, AI cloud providers can use NVIDIA’s backing to reduce financing risk and expand their infrastructure. 

Similar arrangements have set precedent. Here is a case in point: In March, Nasdaq-listed Gorilla Technology agreed to finance and supply more than 5,000 NVIDIA GPUs for Yotta’s Navi Mumbai AI data centre. The partnership was later expanded to include another 20,736 Blackwell GPUs worth about $2.8 Bn. Instead of Yotta bearing the upfront cost, much of the investment sits on Gorilla’s balance sheet.

Similarly, Nashik-headquartered data centre and AI services provider ESDS has signed a $1.25 Bn agreement with Australia’s Sharon AI that will own and operate an 8,000-GPU cluster, which ESDS will then use for serving its domestic and global customer needs for compute.

NVIDIA’s new revenue-sharing model formalises a similar financing approach, and smaller Indian players are already taking notice. For example, Indore-based NeevCloud, which plans to deploy 40,000 GPUs, told Inc42 it is open to exploring NVIDIA’s new model.

“We already have a demand for more than 12,000 GPUs, backed by signed contracts and letters of intent, in addition to the 1,000 GPUs allocated under the IndiaAI Mission. Based on current demand, we expect to deploy at least 10,000–12,000 more GPUs over the next year,” said Narendra Sen, the cofounder of NeevCloud. 

Noida-headquartered Utho Cloud has 10,000 GPUs coming into the pipeline. It depends on structured financing partnerships with select vendors and financial institutions for accessing AI infra.

According to Manoj Dhanda, the founder and CEO of Utho, GPUs are expensive, demand is lumpy in the early stages, and the time between hardware deployment and revenue realisation creates real financial pressure. 

“A revenue-share structure with NVIDIA as a backstop partner changes that calculus significantly. We are actively evaluating whether this programme extends to Indian cloud providers and at what scale. If it does, it could meaningfully accelerate our GPU roadmap,’ he said.

A New GPU Problem In Motion

The model, however, also raises concerns about NVIDIA’s growing influence over the Indian AI ecosystem. Critics argue that the company is no longer just selling chips. By financing AI infrastructure and taking a share of cloud revenues, NVIDIA is expanding its role across the AI value chain while continuing to earn industry-leading margins.

Some analysts have also pointed to NVIDIA’s investments in companies such as OpenAI and xAI as signs of an increasingly interconnected ecosystem, ‘circular financing’, in which the chipmaker has financial interests at multiple levels.

For India, the bigger concern is whether this could affect access to GPUs. NVIDIA already controls the supply of its latest AI chips, such as Blackwell. Reportedly, it has also created a whitelist of verified customers to prevent its AI chips from being diverted to China.

The move follows tighter US export controls and investigations into alleged chip smuggling through intermediaries in Singapore and Taiwan.

If cloud providers participating in its revenue-sharing programme receive priority access to future GPU allocations, companies outside the programme could find it harder to secure chips, regardless of demand. While NVIDIA maintains that the programme is optional and intended to expand AI infrastructure, it also strengthens the company’s influence over who builds and scales AI cloud capacity.

“NVIDIA is already supporting multiple providers through investments and capacity arrangements. For Indian providers, the immediate relief will be on the balance sheet, through lower financing friction and better risk sharing. However, the actual benefit will depend on the programme’s financing terms, revenue-sharing obligations and eligibility criteria,” said Sushovan Mukhopadhyay, director analyst at Gartner.

He added that if NVIDIA gives supported partners priority for new products, delivery schedules or technical resources, smaller independent providers could face a widening disadvantage.

Now the debate is how much control Indian neoclouds are willing to cede to a company that is increasingly becoming their chip supplier, financing partner, investor and, in some cases, even a customer.

[Edited by Shishir Parasher]

The post NVIDIA Wants To Solve India’s GPU Compute Problem, But At What Cost? appeared first on Inc42 Media.