E2E Network Unveils India’s Largest NVIDIA H200 GPU Cluster
The deployment consists of two high-performance clusters, each featuring 1,024 NVIDIA H200 GPUs, strategically located in Delhi NCR and Chennai.
E2E Network has launched one of India’s most extensive NVIDIA H200 GPU clusters, significantly enhancing the country’s AI computing capabilities. The deployment consists of two high-performance clusters, each featuring 1,024 NVIDIA H200 GPUs, strategically located in Delhi NCR and Chennai.
With a total of 2,048 GPUs, the infrastructure delivers 288.8 TB of GPU RAM and 4.8 TB/s memory bandwidth, providing the power needed for training and fine-tuning open-source AI models like DeepSeek and Meta’s Llama.
The NVIDIA H200 GPUs are optimised for memory-intensive workloads, offering a 2.4× improvement in memory bandwidth.
“E2E Cloud’s investment in India’s largest NVIDIA H200 GPU deployment underscores our commitment to building the country’s most advanced AI infrastructure. By placing these clusters in Delhi NCR and Chennai and integrating them with our TIR AI/ML Platform, we are making cutting-edge AI computing more accessible and efficient for enterprises, researchers, and developers," Tarun Dua, MD of E2E Cloud, said.
The TIR AI/ML Platform simplifies GPU access, eliminating infrastructure complexities and allowing users to easily launch training, fine-tuning, and inference workloads.
Beyond AI model training, this deployment also supports real-time AI-driven applications across sectors such as healthcare, autonomous systems, and financial analytics.
Additionally, E2E Cloud’s Sovereign Cloud Platform ensures compliance with strict data residency regulations, making it an ideal choice for industries like government, finance, and healthcare that require high-performance AI computing within regulatory frameworks.
Last month, E2E Cloud launched its Sovereign Cloud Platform, an AI-powered cloud solution designed to give enterprises, governments, and data centers full control over their digital infrastructure, addressing concerns around data sovereignty, vendor lock-in, and cloud technology restrictions.
