Tata Communications and E2E Networks Boost India’s AI Infrastructure

Tata Communications plans to invest up to $450 million to develop 10,000 kilometers of AI-ready connectivity infrastructure across India. The initiative aims to support the country’s growing data center and compute requirements through a high-speed fiber network.

Key Developments in AI Infrastructure

• Financial investment: Tata Communications has allocated $400 million to $450 million for the project, funded through operating cash flows.
• Expansion timeline: The capacity increase is scheduled for completion between fiscal years 2030 and 2036.
• Network capacity: The project will add 6 petabits per second of capacity, bringing the company’s total to approximately 7.25 petabits per second.
• Strategic focus: The initial phase targets the Mumbai-Pune-Hyderabad-Chennai corridor, a significant route for hyperscale growth.
• E2E Networks growth: E2E Networks separately announced plans to increase its AI factory capacity by 59%, reaching 14.6 megawatts.

Tata Communications Network Strategy

The new network is designed to link major data centers and AI hubs, facilitating rapid data transfer with minimal latency. This infrastructure will connect to Tata Communications’ existing global network, integrating domestic facilities with international cable landing stations. The company reported that its current network utilization is between 70% and 80%. According to Rupesh Chokshi, Chief Product and Technology Officer, the AI economy requires a network built for resilience and scale to support hyperscalers, cloud providers, and global capability centers.

E2E Networks Capacity Expansion

Parallel to the Tata Communications announcement, E2E Networks stated it would add 5.4 megawatts of capacity using NVIDIA HGX B300 systems and Blackwell GPUs. The company intends to support large-scale model training, inference, and multimodal applications. E2E Networks noted increased demand from research institutions, public-sector programs, and enterprises operating in data-sensitive environments, highlighting a trend toward hosting AI infrastructure domestically.