Cloud GPU nodes
Dedicated GPU instances and multi-GPU nodes, hourly or on commitment.
Teams training or fine-tuning who cannot get hyperscaler capacity at a price that works.
We build and run on thisGPUs and compute that stay inside India.
E2E Networks runs data centres in India and sells GPU capacity from them. We build and run workloads there — training, inference, and the ordinary compute around them — and can place committed spend through our partnership. We build and run workloads there — training, inference, and the ordinary compute around them — and can place committed spend through our partnership.
Not a boxed licence — we design and operate workloads here, and can place your committed spend through us.
What we work with
The products, and who each one suits.
5 listed
TIR AI Platform
E2E's managed environment for notebooks, fine-tuning and model endpoints.
Data science teams who would rather not build the platform before the model.
CPU compute instances
Ordinary virtual machines for the application layer sitting next to the GPUs.
The API, the queue and the service that the model is actually serving.
Managed Kubernetes
A managed control plane on E2E infrastructure.
Teams that want the same delivery pipeline they already use elsewhere.
Object and block storage
Storage for datasets, checkpoints and artefacts in the same region as the compute.
Training runs where moving the dataset is half the cost.
What buying through us changes
A partnership is only worth what it changes.
Better than list
On committed GPU and compute capacity. Price moves with term and volume — we place your commitment through our partnership rather than at list, and in rupees.
Named contacts
An escalation path into E2E, in your own time zone. When a node goes unhealthy mid-run, that matters more than the rate card.
Startup credits
Where E2E is running a credits or startup programme, we sponsor the application and prepare the technical case. The award is E2E's decision.
How we help
The work around the product.
- Sizing — what the training or inference job actually needs, in GPU class and count, before anyone signs up to a term.
- Benchmarking — the same workload run here and on a hyperscaler, with the numbers and the assumptions handed over, not summarised.
- Build — node and cluster setup, drivers, CUDA, container images, schedulers and a storage layout that suits the dataset.
- Pipelines — training and fine-tuning pipelines with checkpointing, plus inference endpoints that survive a node failure.
- Data residency — keeping datasets, checkpoints and logs inside India, and documenting where each one lives.
- Utilisation — idle GPU is the whole problem. Queueing, scheduling and shutdown so you pay for work rather than for waiting.
Where this is not the answer
E2E is a good answer for GPU capacity and for keeping data in India. It is not a hyperscaler: the managed service catalogue is narrower, and if your architecture leans on a specific managed product from AWS, Azure or Google, this is not a like-for-like swap. GPU capacity is finite everywhere — we can ask for a node, we cannot conjure one. Where a workload genuinely belongs on a hyperscaler, we will tell you that.
Also in this category
Cloud & infrastructure
Next step
Buying E2E Networks? Talk to us first.
Tell us what you are trying to do and we will come back with the sizing, the licensing and the price — from a partner, within two working days.