CHENNAI: Tamil Nadu’s challenges in training students in artificial intelligence (AI) may have less to do with a shortage of computing hardware than with the institutional failure to tap resources earmarked for them, according to Ministry of Electronics and Information Technology (MeitY) Secretary S Krishnan.
In a response to Tamil Nadu AI Association’s concerns over only a very few colleges in the state having access to computing systems with research-grade Graphics Processing Units (GPUs), Krishnan said that academia and students are eligible end-users under IndiaAI Compute — a scheme offering subsidised access to GPU capacity through empanelled cloud providers. Some 10,571 GPUs have been allocated through the initiative to date, he told TNIE.
TN AI Association said that typical AI coursework requires 15 to 20 units of research-grade GPUs for practical training. Venkatesh Rajendran, chairman of the association, said that a single GPU costs roughly Rs 20 lakh, putting dedicated infrastructure out of reach for many institutions.
The ministry’s position is that ownership of the hardware is unnecessary. IndiaAI Compute operates on a shared, cloud-based model, allowing eligible users to access high-performance infrastructure remotely rather than purchasing hardware outright. Krishnan pointed to additional union government capacity — C-DAC’s AIRAWAT facility and the PARAM Siddhi-AI system — which together offer close to 48 petaflops of processing power and 656 GPUs.
Yet, there is no guaranteed carve-out for higher education within that pool. “No fixed percentage of compute capacity is earmarked exclusively for universities or undergraduate students,” Krishnan said, even as he reiterated that academic users remain eligible to apply.
That gap between eligibility and entitlement carries weight, as the state has set an ambitious target under its 2026-27 budget: an AI Economy Mission to train 5,00,000 people in AI skills by 2031, delivered through existing engineering colleges, polytechnics and ITIs rather than new dedicated infrastructure.
The association has applied separately for access to around 15 GPUs through the union ministry, with plans to partner with a university to distribute the capacity to students, if approved. The MeitY secretary said the application would be assessed under the IndiaAI Compute End-User Policy, weighing eligibility, project justification, and technical need through the standard approval process.
In practice, this reframes the challenge facing Tamil Nadu’s colleges - from raising capital to buy hardware to navigating a union government application and approvals process for shared access. For institutions needing sustained, high-frequency use for classroom teaching, that distinction is not trivial, since cloud allocations require project justification and case-by-case sign-off, while coursework demands routine, large-scale student access to train models and handle datasets. A NITI Aayog committee is now examining the broader question, Krishnan said.
Compute is only one constraint, according to Dr N Bhalaji, a higher education expert in computing science. Colleges also lack curated datasets, sandboxed environments, and faculty with production-level AI experience. “I have watched thousands of students arrive with courage, but not always with the skills and tools their ambition deserves,” he said, citing limited compute, restricted LLM credits and inadequate access to frontier tools as recurring campus constraints. Rajendran added that AI coursework remains heavily theoretical even as employers demand hands-on experience.
The test for TN’s AI-skilling drive is not whether the national compute pool exists, but whether it can be converted into classroom-level access — a distinction that may have a say in the state’s efforts to produce AI-certified graduates.