India’s aggressive push to build sovereign artificial intelligence infrastructure is set to bolster, rather than cannibalize, established tech hubs in Southeast Asia, according to industry leaders speaking at the Gitex AI Asia 2026 conference in Singapore. Executives from Indian datacenter giant Yotta Data Services and UK-based Gorilla Technology detailed how the subcontinent's massive scale and government backing will create a testing ground for the broader Asian market.
Sunil Gupta, co-founder and CEO of Yotta, and Jay Chandan, chairman and CEO of Gorilla Technology, recently signed a landmark agreement to deploy thousands of graphics processing units across India. The partnership aims to address the growing demand for sovereign AI capabilities, driven by concerns over data privacy and security. India, with a population of 1.4 billion and over a billion smartphone users, processes more than half of the world's digital payment transactions, generating immense amounts of data that must be stored and processed domestically.
The Indian government's IndiaAI Mission heavily subsidizes computing costs, paying infrastructure providers to allocate GPUs to local model builders, researchers, and academia. Yotta and Gorilla plan to initially deploy 5,000 GPU cards for AI workloads in the first six months, scaling up to 36,000 GPUs eventually. Gorilla will provide the GPU infrastructure, while Yotta will operate it at its Navi Mumbai datacenter, offering GPU clusters, bare-metal GPUs, AI lab workstations, and AI model endpoints.
However, many enterprises have developed AI use cases that never reach production because CFOs remain unconvinced about return on investment (ROI). Gupta noted that bringing down costs is essential to help enterprises cross the threshold into production. By offering an elastic, low-cost consumption model, Yotta and Gorilla aim to make enterprise AI commercially viable, generating ROI within three to five years.
The build-up of India's AI infrastructure has raised concerns that it could syphon investments away from Southeast Asian digital hubs like Singapore, Malaysia, and Vietnam. Chandan dismissed these fears, stating that India is not here to replace anybody but to help build scale and velocity. He emphasized that India's sprawling datacenters can solve global supply chain challenges, especially given GPU shortages elsewhere. Enterprises from Europe and the Middle East are increasingly looking to India to host AI training and inference workloads due to its geopolitical stability and cost efficiency.
Background on India's sovereign AI drive
India's focus on sovereign AI stems from its unique digital landscape. The country's Unified Payments Interface has revolutionized digital payments, generating vast amounts of transactional data. With AI adoption growing, citizens and businesses are demanding that their data remain within India's borders. The IndiaAI Mission, launched in 2024, provides subsidized computing power to democratize AI access. This has spurred partnerships between global chipmakers like Nvidia and local infrastructure providers, enabling large-scale model training.
The Yotta-Gorilla alliance is a prime example. Yotta operates one of India's largest datacenter parks in Navi Mumbai, while Gorilla specializes in AI-powered smart city applications. The deployment of thousands of GPUs will support not only smart city projects but also finance, media, entertainment, and manufacturing use cases. Gupta highlighted that many enterprises have developed proofs of concept but need cost-effective infrastructure to scale up.
Beyond the partnership, other initiatives underscore India's AI ambitions. Microsoft announced a major expansion of its AI footprint in India, teaming up with four of the country's largest IT services companies to deploy agentic AI capabilities across enterprises. Open source AI gained recognition during the India AI Summit, though divisions over governance and market concentration persist. Nvidia has also announced partnerships with India's leading infrastructure and technology providers to build sovereign AI capabilities.
The ROI challenge remains a key barrier. CFOs hesitate to invest heavily in GPUs, models, datasets, and skill sets without guaranteed returns. Gupta emphasized that absorbing GPU costs through subsidized models helps enterprises experiment and succeed, leading to scale. Chandan added that the 36,000-GPU target will create a robust ecosystem for AI development in India.
As global AI demand surges, India's role as a geopolitically stable hub becomes more critical. The country's ability to provide cost-efficient, scalable infrastructure positions it as a major hotspot for serving global AI demand. While some fear that India's growth will overshadow Southeast Asian hubs, industry leaders argue that India's success will lift the entire region by demonstrating large-scale AI deployment and creating new opportunities for collaboration.
Source: ComputerWeekly.com News