From GPUs to Zero Trust the tech behind India AI plans

India’s AI challenge is no longer about models. It is about compute, power, data architecture, and security at scale.

At the India AI Impact Summit 2026 in New Delhi, Dell Technologies unveiled its AI India Blueprint, a technical execution framework focused on building national-scale Artificial Intelligence (AI) infrastructure. The document shifts attention away from AI hype and toward the engineering realities required to support population-scale workloads.

AI at exaFLOP scale requires new infrastructure thinking

India’s AI workloads are projected to grow at around 30% CAGR through 2030, with national compute demand expected to reach 12–15 exaFLOPS by the end of the decade. That scale fundamentally changes infrastructure requirements. High-density GPU clusters, low-latency interconnects, large memory bandwidth, and disaggregated storage architectures are no longer optional. They become baseline requirements.

The blueprint proposes a National AI Compute Strategywith measurable GPU and exaFLOP targets, regional compute zones tied to research clusters, and transparent allocation models. The focus is sovereign compute capacity so that strategic models, sensitive datasets, and critical workloads remain under national control.

Technically, this means:

• Scaling high-performance computing (HPC) clusters

• Designing racks for higher power density

• Supporting always-on inference workloads

• Enabling agentic AI systems that require continuous reasoning cycles

This is infrastructure engineered for sustained inference, not just model training bursts.

Energy becomes a core AI design constraint

AI infrastructure is energy intensive. Projections suggest data centers could consume up to 8% of India’s electricity by 2030. That makes grid coordination and cooling design central engineering issues. The blueprint recommends aligning compute expansion with renewable procurement, advanced cooling systems, and improved Power Usage Effectiveness (PUE) targets.

High-density AI racks require:

• Liquid cooling or hybrid cooling systems

• Stable grid capacity with redundancy

• Optimized transmission networks in compute clusters

In short, AI scaling becomes a power engineering challenge as much as a computing one.

Federated data architecture over centralization

Instead of consolidating national datasets into a single repository, the blueprint supports federated AI models. AI systems operate where data resides, reducing data movement risks and aligning with the Digital Personal Data Protection (DPDP) Act.

This architecture depends on:

• Strong identity and access management

• Encryption and provenance tracking

• Privacy-enhancing technologies

• Standardized dataset documentation

Federation reduces central attack surfaces while maintaining analytical capability.

Security must be embedded, not layered on

AI systems expand the cyber-attack surface. Reported cyberattacks rose approximately 28% in 2024. The blueprint emphasizes Zero Trust architectures, adversarial model testing, and provenance verification.

Key technical safeguards include:

• Model integrity checks

• Data poisoning detection

• Secure workload scheduling

• Red-team simulation exercises

Security, in this framework, is built into model pipelines and infrastructure orchestration.

The technical shift underway

India’s AI roadmap is entering an engineering phase. The focus is moving from pilot deployments to sustained, high-availability AI infrastructure capable of supporting public systems, research institutions, and industry workloads.

The real transformation is not the model layer. It is the compute fabric, energy backbone, federated data systems, and Zero Trust security architecture that will determine whether AI can run reliably at national scale.

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