India's push to become a global AI powerhouse is facing a major hurdle — limited access to high-performance GPUs. While large enterprises are investing in AI, startups, businesses, and researchers struggle to secure the computing power needed to scale AI applications.
To address this, the government plans to roll out 18,000 high-end GPU servers and develop indigenous GPUs within the next three to five years. It has also allocated over US$1.2 billion under the IndiaAI initiative to procure 10,000 GPUs through public-private partnerships, aiming to boost computing capacity.
The private sector is also stepping in, with Lenovo manufacturing AI servers in Puducherry to produce 50,000 AI rack servers and 2,400 GPU servers annually. Despite these efforts, GPU shortages, high costs, and long lead times remain challenges, making it crucial for India to expand infrastructure and workforce development to sustain its AI growth.
Startups and small businesses struggle with GPU access
The demand for GPUs has surged as organizations seek to deploy AI for automation, analytics, and innovation. However, the prohibitive cost of acquiring and maintaining GPU infrastructure remains a significant challenge, particularly for startups and small enterprises.
"Startups and small businesses in India struggle with the high upfront costs of GPUs, limited cloud-based GPU availability, and long lead times due to supply constraints," said Anindya Das, co-founder and chief technology officer of Neysa. "Larger enterprises, while better funded, face scalability and cost optimization challenges when running large-scale AI workloads."
The constraints are not just financial — availability is another major concern. While global GPU supply chains have improved, businesses still face extended procurement times.
"GPU supply chains have gotten a lot better over the last several months, but there's still longer lead times for GPU systems as compared to regular non-accelerated compute," Das noted. "Having the GPUs is only one piece of the puzzle, though. The network and storage that are typically needed for accelerated compute requirements like AI/ML and HPC are non-trivial to set up and manage. Not having the right network, storage, and MLOps tools platforms can severely mitigate the return that clients get from their GPUs."
Impact across key sectors
The inability to access affordable GPU infrastructure is slowing AI adoption across key industries. In sectors like healthcare, agriculture, education, and smart cities, AI-driven solutions remain out of reach for many due to high computing costs.
"For instance, in healthcare, AI-driven diagnostics and treatment plans require powerful GPUs to process vast amounts of medical data and images," said Swastik Chakraborty, vice president of technology at Netweb Technologies. "Without these systems, healthcare providers in India struggle to implement AI solutions at scale, limiting their ability to improve patient care."
Agriculture, another crucial sector for India, faces similar constraints. AI-based crop monitoring and resource optimization tools could improve yields and reduce waste, but the high costs of GPUs prevent their widespread adoption.
"Smart city initiatives, which depend on AI for traffic management, waste management, and urban planning, are also impacted," Chakraborty added. "The affordability gap in GPU infrastructure limits innovation and slows down the digital transformation."
Government and industry must bridge the GPU gap
The Indian government has launched initiatives such as the India AI Mission to build AI-focused compute infrastructure. However, industry experts argue that public-private partnerships will be critical to bridging the gap.
"The government's India AI Mission is a crucial step toward building AI compute infrastructure, but public-private partnerships are key," Das said. "Initiatives like the India AI Mission, compute hubs, subsidies for AI startups, and incentives for data center providers to deploy GPU clusters will help."
Chakraborty emphasized the need for policy incentives to drive private sector investment. "Public-private partnerships can help build shared infrastructure, such as GPU-based AI research centers and cloud platforms, lowering entry barriers for startups," Chakraborty said.
Private companies also have a role to play in making AI more accessible. Leasing models and subscription-based GPU access could provide startups with cost-effective alternatives to large capital expenditures.
A skilled workforce: The missing piece in India's AI growth
Beyond hardware availability, the lack of skilled AI professionals is another bottleneck in India's AI expansion.
"AI adoption is constrained not just by access to GPUs but also by a shortage of skilled talent," Das said. "To bridge this gap, India needs stronger upskilling programs focused on model optimization, MLOps, and AI infrastructure management."
Chakraborty stressed the importance of academic-industry collaboration. "Universities, enterprises, and AI cloud providers must collaborate to deliver hands-on AI training -much like how cloud certifications helped upskill India's IT workforce. Specialized programs in LLM fine-tuning, AI orchestration, and efficient inferencing will be essential to building the expertise needed to scale AI adoption across industries," Chakraborty said.
Cloud-based AI: A cost-effective alternative
For startups and businesses grappling with the financial burden of AI adoption, cloud-based solutions offer a viable alternative.
"Unless you have an unlimited budget, deploying your AI/ML projects in the cloud is the smarter choice over a CapEx-heavy on-premise approach," Das said. "The true cost of maintaining on-prem GPUs extends far beyond hardware expenses, including hidden operational and infrastructure costs that, over time, often make it significantly more expensive than leveraging cloud-based GPUs with long-term commitments."
Chakraborty pointed to cloud platforms from AWS, Google Cloud, and Microsoft Azure as cost-effective solutions, allowing companies to pay for computing power on a usage basis rather than investing in expensive infrastructure.
"Businesses can adopt AI models that are optimized for lower computational power, using techniques such as model pruning or using smaller, more efficient networks," Chakraborty said.
Can India catch up?
India's AI sector has the potential to rival global leaders, but overcoming its current infrastructure and skills challenges will require a coordinated effort.
The future of AI in India depends on smart investments in compute infrastructure, talent development, and cost-efficient AI deployment strategies. A fully integrated AI cloud platform that streamlines development and deployment can be the key differentiator between AI projects that succeed and those that fail to deliver real value.
As India positions itself as a global AI hub, addressing these barriers will be critical to ensuring long-term industry growth and innovation.
