GPU Cloud IndiaRent NVIDIA GPUs from ₹30/hr

GPU cloud computing provides on-demand access to Graphics Processing Units (GPUs) over the internet, eliminating the need to purchase and maintain physical hardware. It enables businesses to run AI training, machine learning inference, deep learning, and high-performance computing workloads without capital investment. E2E Networks offers NVIDIA B200, H200, H100 and earlier-generation GPUs on demand, running on infrastructure in Delhi NCR and Chennai.

GPU cloud pricing in India starts from ₹30/hour for NVIDIA T4 GPUs at E2E Networks. NVIDIA H100 GPUs are available from ₹379.05/hour and the latest H200 GPUs from ₹502.55/hour. Reserved terms from one month bring the effective rate down and are quoted per deployment. All pricing is in INR with no hidden fees or currency fluctuation risk.

For large language model (LLM) training, NVIDIA H200 and H100 GPUs are optimal due to their high memory bandwidth (4.8 TB/s and 3.35 TB/s respectively) and 4th generation tensor cores. B200 and H200 carry the most memory per GPU, and multi-node clusters scale past what any single GPU holds — there is no model size you are capped at, whether you are fine-tuning an open-source model or training a frontier-scale one from scratch. For smaller models and fine-tuning, A100 GPUs provide excellent price-to-performance. L40S is recommended for inference workloads.

Yes, E2E Networks offers hourly GPU rental without any long-term commitments. You can deploy on-demand and pay only for the time you use. Simply sign up, launch your GPU instance, and scale up or down as needed. Monthly and yearly commitments are also available for additional discounts.

For almost every team, renting. Buying an H100 means capex plus power, cooling, rack space, spares and a depreciating asset that is usually idle between projects. Renting the same GPU from E2E Networks costs ₹379.05/hour on demand, with no hardware to own or maintain and no lead time on delivery. Buying only starts to make sense at sustained near-100% utilisation over several years, which is rare outside dedicated research clusters. If you are close to that line, talk to sales about a committed plan before you buy — reserved terms are quoted per deployment and come in well below the on-demand rate.

Spot instances. They run on spare capacity at 65–82% off on-demand — an H100 at ₹70/hour instead of ₹379.05/hour — and can be reclaimed when demand rises, so checkpoint your work and they suit training runs, batch jobs and hyperparameter sweeps. If your workload cannot tolerate interruption, a committed-use plan brings the effective rate well below on-demand in exchange for a term from one month; those are quoted per deployment. Entry-level on-demand GPUs start at ₹30/hour.

The current fleet is NVIDIA B200, NVIDIA H200, NVIDIA H100, NVIDIA RTX PRO 6000, NVIDIA A100 80GB, NVIDIA A100 40GB, NVIDIA A40, NVIDIA L40S, NVIDIA V100, NVIDIA A30, NVIDIA L4 and NVIDIA T4. All are available on demand by the hour, running on infrastructure in Delhi NCR and Chennai; spot and committed-use rates are offered on a subset. Single GPUs and multi-GPU configurations (1x, 2x, 4x, 8x) can be launched from the dashboard, and larger clusters with InfiniBand interconnect are quoted by sales.

No. On-demand and spot GPUs have no minimum term and no contract — launch an instance, run your job, and terminate it when you are done. Billing stops when the instance is terminated, so a two-hour experiment costs two hours. Minimum terms apply only if you choose a committed plan, which starts at one month in exchange for a lower effective hourly rate.

No. If you only need to serve a model, Managed Inference Endpoints give you an autoscaling API endpoint without provisioning or operating a GPU instance — deploy a model from the catalog in one click, point an endpoint at any Hugging Face model or your own repository, or call the shared GenAI API without deploying anything at all. Endpoints scale to zero when idle, so you are not paying for a GPU between requests. Rent a raw GPU when you need control over the runtime; use a managed endpoint when you just need the model behind an API.

Yes. Multi-GPU instances in 1x, 2x, 4x and 8x configurations can be launched directly from the dashboard with NVLink between GPUs on the same host. Beyond a single host, E2E Networks runs multi-node clusters connected over 3.2 Tbps InfiniBand for distributed training, along with a shared parallel file system. Cluster deployments are quoted per configuration — talk to sales with your GPU count, interconnect and term.

The differences are mostly about predictability. E2E publishes a full rate card for every GPU, so you can price a workload without a quote; billing is in INR with a GST invoice, so there is no forex exposure; there is no minimum commitment, so the hourly rate is the rate; and 24/7 support in IST is included rather than sold as a separate plan. Infrastructure is in Delhi NCR and Chennai, and E2E is MeitY-empanelled, which matters for government and public-sector work. The major hyperscalers do run India regions, so the choice is less about where the compute sits and more about how it is priced and supported.

E2E Networks is India's leading GPU cloud provider with NSE listing (E2E) and MeitY empanelment. Our latest GPUs are the NVIDIA B200, H200 and H100, alongside earlier generations including the A100, L40S and L4, running on infrastructure in Delhi NCR and Chennai. Other options include international providers like AWS, Azure and GCP, which now run India regions but bill in USD.

Deploying AI models on E2E GPU Cloud is straightforward: (1) Sign up at myaccount.e2enetworks.com, (2) Choose your GPU type (H200, H100, A100, etc.), (3) Select a pre-configured AI stack with PyTorch, TensorFlow, or vLLM pre-installed, (4) Launch your instance in seconds, (5) SSH into your instance and start training or inference. REST APIs are also available for programmatic deployment.

GPU memory requirements depend on model size: 7B parameter models typically need 16-24GB (L4 or A100 40GB), 13B models need 40-48GB (A100 40GB or L40S), 30B+ models need 80GB (A100 80GB or H100), and larger models benefit from the 141GB HBM3e on H200 or the 180GB on B200. Single-GPU memory is not the ceiling — distributed training across NVLink-connected hosts and InfiniBand-connected clusters scales to custom and frontier-scale models.

E2E GPU Cloud offers pre-configured AI stacks with popular frameworks including PyTorch, TensorFlow, JAX, and vLLM for inference. CUDA, cuDNN, and NVIDIA drivers are pre-installed and optimized. You can also start with a bare OS and install your custom stack.