New Delhi, March 3 -- India is entering a decisive era. With the rise of AI, the momentum has intersected with a unique set of national priorities: sustaining economic growth at scale, enabling digital inclusion across a vast and diverse population, and reinventing productivity across sectors such as manufacturing, healthcare, and agriculture. And the rise of agentic AI will supercharge this transformation further.
Unlike traditional AI models, agentic AI does not just respond to queries - it reasons, plans, and takes actions across systems. For example, instead of simply answering a question on travel recommendations, an agentic system would book your flights, update your calendar, send reminders, and even adjust your itinerary based on traffic, weather, or delays - all without being prompted for each step. This marks a shift from passive AI responses to proactive, collaborative systems that work alongside humans.
As the technology for agentic AI matures and adoption expands, the world is effectively adding billions of virtual users into the compute fabric. The question is whether India's AI infrastructure is ready to support this scale and complexity.
India's strategic opportunity
India is uniquely positioned to reap the benefits of AI. National initiatives such as Digital India, the IndiaAI Mission, and ongoing expansion in public digital infrastructure are accelerating AI development and adoption across the economy. The country also has deep strengths in software engineering, data platforms, and applied AI, with growing deployment across areas such as smart manufacturing, healthcare diagnostics, fintech, logistics, and urban infrastructure.
At the same time, India's scale introduces new challenges. As AI adoption expands across enterprises, startups, and government services, the demand for compute will grow dramatically - not just for isolated tasks or inference requests, but for extended, always-on workflows that involve reasoning, planning, and continuous adaptation that agentic AI requires.
The question is no longer "if" AI will transform India's economy, but whether we have the right infrastructure to support this next wave of AI.
AI is more than GPUs
High-performance graphics processing units (GPUs) often dominate AI discussions, particularly for training and running large-scale models. But central processing units (CPUs) are just as critical in powering AI systems behind the scenes - handling data movement, memory management, thread coordination, and orchestrating GPU workloads.
Many AI workloads, including language models with up to 13 billion parameters, image recognition, fraud detection, and recommendation systems, do not require accelerators and can run efficiently on CPU-only infrastructure built on modern architectures optimized for memory bandwidth, concurrency, and throughput.
As AI models evolve into more modular architectures, including mixture-of-experts approaches adopted by leading AI developers, the need for intelligent resource orchestration increases. CPUs must deliver high instructions per clock (IPC), fast input/output (I/O), and the ability to manage multiple concurrent tasks with precision.
Equally critical is connectivity - the "glue" that binds modern AI systems together. Advanced networking components, such as smart network interface controllers (NICs), help route data efficiently and securely between system components, offloading traffic from GPUs and reducing latency. High-speed, low-latency interconnects ensure data flows seamlessly across systems, while scalable fabrics tie nodes together into powerful distributed AI clusters.
In the era of agentic AI, heterogeneous system design becomes essential. AI infrastructure must integrate CPUs, GPUs, networking, and memory in a flexible and scalable way. Systems built this way can deliver the coordination, throughput, and responsiveness required to support real-time interactions across billions of intelligent agents. As adoption scales, rack-level optimization where compute, storage, and networking are tightly co-designed - will be key to unlocking the next wave of performance and efficiency.
Why openness matters in the AI race
As AI systems become more complex and distributed, openness in software, hardware, and systems design becomes a strategic imperative. Closed ecosystems limit choice and flexibility, and constrain innovation at a time when adaptability is critical to scaling AI.
This is why open software stacks and open-source AI frameworks are important. They provide developers and researchers the freedom to build, optimise, and deploy AI models across a wide range of environments. They also support popular machine learning frameworks, include advanced tools for performance tuning, and provide portability across hardware - all through an open-source approach. For India's rapidly growing AI ecosystem, spanning startups, academia, enterprises, and public-sector innovation, open AI software lowers barriers to entry and accelerates experimentation and deployment.
Openness at the hardware and systems level is equally critical. As AI compute evolves toward large-scale, heterogeneous deployments, rack-scale architecture becomes foundational. Open standards such as the Open Compute Project (OCP) enable modular system design, while emerging initiatives like Ultra Accelerator Link (UALink) aim to create open, high-bandwidth connections between AI accelerators across servers. In parallel, the Ultra Ethernet Consortium (UEC) is defining next-generation networking standards purpose-built for AI, enabling low-latency, high-throughput data movement across distributed systems.
These open initiatives allow cloud providers, enterprises, and government organizations in India to build flexible, interoperable infrastructure that can keep pace with AI's rapid growth. Embracing openness positions India to benefit from global innovation while building infrastructure tailored to local needs.
In the era defined by multi-agent AI systems, openness is not just a philosophy; it is a prerequisite for scale, resilience, and long-term competitiveness.
Looking ahead
As agentic AI reshapes how work gets done, the focus must extend beyond GPUs to include CPUs, high-speed interconnects, and intelligent networking - all of which are essential for orchestrating complex, real-time AI workflows at scale. Equally important is an open ecosystem, with open software frameworks, open industry standards for rack-scale design, and collaborative interconnect and networking initiatives enabling interoperability from edge to cloud.
For India, investing in open, heterogeneous, and scalable AI infrastructure is more than a technology decision it is a strategic foundation for long-term growth and competitiveness. As AI adoption accelerates across the economy, future-ready infrastructure will be critical to unlocking productivity, innovation, and resilience at the national scale.
Published by HT Digital Content Services with permission from TechCircle.