India’s AI infrastructure challenge: Moving from GPU availability to enterprise readiness

By Abhisyant Anasapurapu

Imagine a bank demonstrating a multilingual AI assistant for loan applications. In the demo, it answers a customer’s question in seconds. In production, it must verify identity, retrieve permitted records, interpret the bank’s current policy, connect to core systems, record what it did and hand uncertain cases to a person. It must work during a surge in demand, at a cost the bank can justify.

The GPU that powers the assistant matters. So does every decision around it. India’s AI infrastructure challenge is shifting from securing compute to making compute ready for enterprise work.

The country has made progress on access. The IndiaAI Mission reported more than 45,000 GPUs in its shared compute capacity by June 2026. Subsidised access is lowering the cost of experimentation for researchers, startups and innovators. This is a substantial foundation. But a count of available GPUs tells an enterprise where it might begin; it does not tell it how to get an application into daily use.
Indian businesses are discovering that difference. In EY’s 2026 survey of Indian enterprises, 47 per cent had multiple generative AI use cases live, yet only 10 per cent were scaling use cases across the business. Integration and data readiness were cited as barriers by 78 per cent; 59 per cent cited a shortage of AI engineers, MLOps specialists and domain experts. These are signs of a market moving forward, but finding that implementation requires more than a promising model.

The pattern is visible among India’s global capability centres too. A Zinnov–Dell study found that nearly 70 per cent remained at the pilot stage. It identified fragmented data, legacy systems, governance, security, compute planning and talent as obstacles to sustained adoption. The gap between pilot and production is where much of the engineering work lies.

Even access to compute is more complicated than access to a machine. A model being trained and an agent serving thousands of customer requests place different demands on memory, networking, storage, latency and cost. The operating system, orchestration layer and runtime must fit the workload and the customer’s existing environment. NVIDIA’s enterprise architecture guidance makes the point plainly: workload and infrastructure strategy have to be solved together. IndiaAI’s requirements for compute providers likewise extend beyond GPUs to operational data centres, monitoring, networking, storage and AI platform services.

This is why building a large cluster first and looking for an offtaker later can be a costly mistake. A provider may choose the hardware layout and software stack it prefers, only to find that an enterprise needs a different configuration for its data, security requirements or applications. The customer then pays for compromises it did not choose.

At NxtGen, we are building more than 20 bespoke private GPU clusters around large, identified enterprise workloads. We work with the customer on the cluster design, platform choices and operating model before committing the architecture. Our supplier relationships help us secure priority compute allocations, but allocation alone does not create value. A workload-led design gives both parties a reason to commit for the long term, improve the system together and keep measuring its economics as usage grows.

That work draws on operational experience. NxtGen has delivered managed services across the cloud stack for 13 years. More than 200 operations professionals run services for over 1,000 customers. We engineer and operate our SpeedCloud platform and the applications we deliver with our own teams, building on open-source and partner technologies rather than buying a turnkey cloud platform. This depth matters when an AI application needs to be monitored, secured, updated and supported long after its launch.

Infrastructure, however, is only part of the assignment. An enterprise should begin with a business goal: faster claims processing, better service resolution, fewer manual checks or a new source of revenue. Engineers must then work alongside the people who own that process, connect the right data, build the application, test its decisions and measure what changes after deployment.

We have delivered AI solutions for 120 customers across GPU infrastructure, platforms and bespoke engineering, including agents and applications running in production. More than 100 of our forward-deployed engineers work across technologies including NVIDIA, Red Hat and OpenAI, with technical training and certifications. We are also training them in communication, product thinking and consultation. An engineer who understands the model but cannot understand the customer’s workflow will struggle to deliver a useful application.

The measure of success should be agreed before the first cluster is ordered: cost per completed task, turnaround time, accuracy, utilisation, reliability and the value of the work accomplished. When one partner designs the compute, runs the platform, builds the application and supports it in production, the enterprise has one accountable team to call when those measures fall short. The customer must still own its business decisions, data and adoption; its partner must own the delivery commitments it makes.

In a recent Sequoia Capital essay, Julien Bek argued that the next trillion-dollar company may look like a services firm because it sells completed work rather than a tool. For Indian enterprise AI, that idea points to a practical model: a company capable of bringing compute, platform operations and implementation engineering together around a business outcome.

India is assembling the compute it needs. Enterprise penetration and returns will depend on who can turn that compute into applications people trust and use every day. The opportunity is for one accountable partner to deliver the entire stack—and to stay responsible when the demonstration becomes a production system.

The writer is with NxtGen Cloud Technologies.