E2E Networks Share Price Hits 5% Upper Circuit as it Plans 5.4 MW Capacity Addition for NVIDIA GPUs

E2E Networks Limited has proposed adding approximately 5.4 megawatts (MW) of capacity for NVIDIA HGX B300 systems and other Blackwell GPUs. The company informed the exchanges about the proposal in a regulatory filing dated October 26, 2026. 

E2E Network's Current Infrastructure 

E2E Networks has an existing capacity of approximately 9.2 MW. The company uses this infrastructure to provide GPU cloud infrastructure and other artificial intelligence (AI)-related services. 

The filing does not specify the current capacity utilisation level. The company stated that the precise figure may be determined from its internal operational data. 

Deployment Schedule and Cost 

The company has not announced a completion date for the proposed expansion. The additional capacity will be deployed according to its infrastructure plan and the expected demand for AI computing services. 

The total investment required for the project has also not been disclosed. According to the filing, the expenditure will depend on the infrastructure deployed and the capital costs associated with it. 

Funding Arrangements 

E2E Networks plans to fund the capacity addition through a combination of internal accruals and debt. The company has not specified how much funding will come from each source. 

The proposed addition is intended to meet demand for GPUs and other services. The infrastructure plan includes NVIDIA HGX B300 systems and other Blackwell GPUs, although the company has not provided further details on the number of systems to be installed. 

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E2E Networks Share Price Performance  

As of October 26, 2026, 3:30 pm, E2E Networks share price closed at ₹594.35, a 5.00% increase from the previous closing price. 

Conclusion 

E2E Networks has proposed increasing its capacity by approximately 5.4 MW from the existing 9.2 MW. The company is yet to specify the project's investment amount and completion timeline, which will depend on infrastructure deployment and demand for AI computing services. 

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