India’s AI industry is getting a major infrastructure boost. Cloud provider Vultr has announced a 20 MW AI supercluster in Chennai, while Indian cloud company E2E Networks plans to add 5.4 MW of NVIDIA Blackwell-based capacity and has launched its Blaze AI inference service.

These are not consumer products like a new smartphone or app. They are large computing systems that can help startups, enterprises, researchers and government bodies build and run AI tools inside India.

The announcements are important because advanced AI needs huge amounts of computing power. Local access to that power can reduce delay, improve data control and make it easier for Indian companies to move AI projects from testing to real-world use.

Vultr’s 20 MW AI supercluster in Chennai

Vultr has announced a new 20 MW AI supercluster in Chennai, powered by AMD Instinct MI355X GPUs. The company says the initial setup will include around 10,000 GPUs and will be part of its new Chennai cloud region.

A megawatt, or MW, is a measure of electrical power. So, 20 MW indicates the scale of electricity needed to operate the servers, GPUs, cooling equipment and supporting infrastructure. It does not mean the system delivers 20 MW of AI performance.

A supercluster is a large group of connected high-performance computers. In this case, the GPUs can work together on demanding AI jobs such as training large language models, fine-tuning a company’s AI model or processing thousands of AI requests at the same time.

Vultr has said the Chennai capacity will start at 20 MW, with further expansion planned from 2027. The company is also targeting a much larger AI infrastructure presence in India over the next few years. However, these future targets should be viewed as plans, not completed capacity.

What businesses can do with the Chennai facility

A startup building an Indian-language AI assistant may need to train and test its model on powerful GPUs. A bank may need to run fraud-detection models while keeping sensitive data close to home. A university may need computing resources for scientific research.

Instead of buying and maintaining a large number of expensive GPUs, these organizations can rent cloud computing power. This makes advanced AI infrastructure more accessible, though the final cost will depend on the workload, usage time and service terms.

Vultr’s use of AMD GPUs also adds another option to a market often associated with NVIDIA hardware. More infrastructure choices may give businesses greater flexibility when selecting AI platforms.

E2E Networks plans Blackwell expansion

E2E Networks has proposed adding approximately 5.4 MW of AI computing capacity using NVIDIA HGX B300 systems and other GPUs based on NVIDIA’s Blackwell architecture. The proposed addition would take its AI factory capacity from about 9.2 MW to around 14.6 MW.

Blackwell is NVIDIA’s newer GPU architecture designed for AI workloads. It is intended to support large model training, fine-tuning and inference. Inference is the stage where a trained model answers a user’s question, generates text, analyses a document or creates an image.

E2E has said it plans to fund the expansion through internal funds and debt. The company has not disclosed a final investment amount, GPU count or commissioning date, so the expansion should be treated as a plan rather than an already completed deployment.

E2E Networks was founded by Tarun Dua, who is also its founder and managing director. The company is positioning itself as an India-hosted cloud provider for AI developers and businesses that want their workloads closer to local users.

E2E Networks plans 5.4 MW NVIDIA Blackwell expansion of India’s AI Factory https://t.co/OW224nNgDi

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Blaze AI service – how it works

E2E Networks launched Blaze on November 2 as a pay-per-token AI inference service for open models. A token is a small unit of text processed by an AI model. Depending on the model, a word may be one token or several tokens.

With Blaze, developers can use one OpenAI-compatible API to access selected open models, including families such as Sarvam, DeepSeek, Qwen, Gemma and NVIDIA Nemotron. Instead of hosting and scaling the models themselves, customers send a request through the API and pay according to the tokens used.

For example, a customer-support tool can send a user’s question to an AI model through Blaze and receive a drafted response. E2E manages the underlying GPU infrastructure and model serving.

The service is aimed at use cases such as voice tools, coding assistants, document analysis, optical character recognition and image or video generation. Its India-hosted infrastructure may be especially useful for organizations with strict data-handling requirements.