---
title: "TIR AI Platform"
description: "TIR is E2E Networks' AI platform: GPU dev nodes, container-based training jobs and autoscaling OpenAI-compatible endpoints, on NVIDIA GPUs in India."
url: "https://www.e2enetworks.com/tir"
canonical: "https://www.e2enetworks.com/tir"
provider: "E2E Networks Limited"
type: "Product"
keywords: ["tir", "tir ai platform", "e2e networks tir", "ai platform india", "fine-tune llm india", "deploy llm india", "gpu notebooks india", "model endpoints india"]
priceCurrency: "INR"
region: "India"
generated: "2026-09-11"
---

# TIR AI Platform

> The platform for training, fine-tuning and deploying AI models. Dev nodes for experiments, container jobs for training runs, and autoscaling endpoints for serving, all on NVIDIA GPUs in India. You bring the model and the data. TIR handles the GPUs, the scaling, and the plumbing in between.

Canonical page: https://www.e2enetworks.com/tir

## At a glance

- **5,000+:** GPUs across the fleet, including B200, H200 and H100, all of them in India
- **7:** Security and quality certifications, every report published
- **99.9%:** Uptime SLA on the underlying infrastructure
- **Capability pages:** /training-fine-tuning, /inference-endpoints, /ai-dev-nodes

## What's on the platform

Six parts, one console and one bill. Most teams start with a dev node and grow into the rest.

- **Train and fine-tune** — Fine-tune an open-weight model, re-train one you already have, or start from scratch. Training runs as a container job. Kick it off on demand or put it on a schedule, and it doesn't need a machine you sit and watch.
- **Serve on endpoints** — Pre-built containers for vLLM, SGLang, Triton and Dynamo, or bring your own image. Endpoints are OpenAI-compatible and authenticated, they scale on request metrics, and they scale to zero when the traffic stops.
- **Dev Nodes** — Jupyter-style notebooks on NVIDIA GPUs, with pre-built images for PyTorch and Transformers, or your own image if you have one. Start a node in seconds, stop it when you're done, pay for the hours it ran.
- **Data and models** — Object Storage for datasets, a Model Repository for weights and config files, and Container Registry for private images. When data loading is the bottleneck rather than the GPU, move the dataset onto the parallel file system.
- **Integrations** — GitHub, Hugging Face and Weights & Biases connect directly, so code, models and run history stay where your team already keeps them.
- **Tenancy and access** — Dedicated clusters when you need the hardware to yourself, VPC attachment and private endpoints when traffic shouldn't touch the public internet, and identity and access management for teams who have to show an auditor who did what.

## Where each capability is documented

The overview links out to the page that carries the detail.

- **Train and fine-tune** — Training & fine-tuning — /training-fine-tuning
- **Serve on endpoints** — Inference & endpoints — /inference-endpoints
- **Dev Nodes** — AI dev nodes — /ai-dev-nodes
- **Data and models** — AI object storage — /ai-object-storage
- **Tenancy and access** — Certifications & compliance — /certifications

## Models you can deploy today

One click puts any of these behind an endpoint. Anything on Hugging Face or GitHub works too, and so does your own custom model once it's in the Model Repository.

- **One-click catalog models:** DeepSeek R1, DeepSeek V3, Llama 4 Maverick, Llama 4 Scout, GPT-OSS 120B, Hermes 3 405B, Dolphin Mixtral 8x22B, Sarvam-2B
- **Bring your own:** Any model from Hugging Face or GitHub, or your own custom model
- **No GPU to manage:** Blaze serves the same models as a plain API — /blaze

## From dataset to endpoint in four steps

None of them involve writing a Kubernetes manifest.

1. **Explore** — Start a dev node, mount your dataset from Object Storage, and try the idea against a catalog model before you spend anything on training.
2. **Fine-tune** — Point a job at your dataset and a base model. TIR provisions the GPUs, runs the container, and logs the run to Weights & Biases if that's where you track them.
3. **Deploy** — Promote the checkpoint to an endpoint. You get an authenticated HTTPS URL, an API token, and a playground to test in before you wire up the app.
4. **Scale** — Set the endpoint to scale on concurrent requests, requests per second, or a runtime metric like `vllm:num_requests_running`. Change GPU or model later without rebuilding around it.

## OpenAI-compatible, out of the box

Every endpoint gets a root URL; append the standard routes.

```bash
# illustrative — copy the exact URL from your endpoint's API Request tab
curl https://infer.e2enetworks.net/project/p-XXXX/endpoint/is-XXXX/v1/chat/completions \
  -H "Authorization: Bearer $TIR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-r1",
    "messages": [{"role": "user", "content": "Summarise this contract clause:"}],
    "max_tokens": 400
  }'
```

## Documentation

- [TIR API reference](https://docs.e2enetworks.com/api/tir/)
- [TIR platform guides and tutorials](https://docs.e2enetworks.com/docs/tir/)

## What teams build on it

A few of the workloads that run on TIR today, and why the shape of the work suits dedicated Indian capacity.

- **Payments** — Transaction risk scoring inside the authorisation path, where the latency budget is measured in milliseconds. Routing and success-rate models. Dispute triage as an overnight batch on the same cluster.
- **Lending** — Underwriting decisioning. Document intelligence over bank statements, ITR and GST filings. Vernacular collections agents with a hard escalation path to a human.
- **Insurance** — Claims triage, document fraud detection and underwriting risk. The work is batch-shaped, which is exactly where reserved capacity costs less than paying by the hour.
- **Medical imaging** — Radiology and pathology models that read scans and flag findings for a clinician to confirm. Patient data stays on Indian infrastructure, which is usually the first question a hospital asks.
- **Voice and support** — Speech recognition and support agents that handle Indian languages and code-mixed speech. Endpoints absorb the evening call peak and scale back down overnight.
- **Analytics at speed** — GPU-accelerated pipelines with Spark and RAPIDS, for teams whose nightly job stopped fitting in the night. Retail demand, pricing, and churn models sit here.

## How it runs

The parts that decide whether a platform is pleasant to live with after month three.

- **Billing you can predict** — Rates are published on the rate card, in rupees, with no quote to chase first. Dedicated capacity bills at a fixed rate, so a busy month and a quiet month cost the same.
- **Observability you already own** — Request-level logs, latency percentiles and the distribution of model decisions, pulled into the monitoring stack you already run. No second dashboard nobody opens.
- **Support from engineers** — The people who work on the platform are reachable directly, and they're in your timezone. The escalation path is published, so you know who to reach when something can't wait.

## What it costs

TIR bills the GPU capacity your nodes, jobs and endpoints consume. /pricing is the source of truth for every rate below.

- **Lowest on-demand GPU:** ₹49/hr — NVIDIA L4
- **B200 on-demand:** ₹671/hr
- **B200 committed:** ₹290/hr
- **H100 committed:** ₹155.90/hr
- **Idle endpoints:** Scale to zero, and stop billing while idle

- [See the full rate card](https://www.e2enetworks.com/pricing)
- [Talk about a committed plan](https://www.e2enetworks.com/contact-sales)

## TIR, or your own stack

Rolling your own is the right answer for some teams. Here's the honest split.

- **TIR handles this for you** — GPU provisioning, drivers and images, so nodes and endpoints come up in seconds
- **TIR handles this for you** — Autoscaling, health checks and rollouts for serving, including scale-to-zero
- **TIR handles this for you** — Training orchestration, scheduled jobs, and weight downloads from Hugging Face or the Model Repository
- **TIR handles this for you** — Compliance posture you inherit: SOC 2, ISO 27001, PCI DSS, and data that stays in India
- **Stay on raw GPUs if** — You already run a mature Kubernetes and serving stack, with a platform team behind it
- **Stay on raw GPUs if** — You need custom kernels, an unusual scheduler, or a network topology of your own design
- **Stay on raw GPUs if** — You want root on the node, which E2E's GPU cloud gives you on the same hardware (/gpu-cloud)

Either way the GPUs and the regions are the same. TIR is the layer above them.

## Frequently asked questions

### What is TIR, exactly?

TIR is E2E Networks' AI platform. It gives you GPU dev nodes for experiments, container-based jobs for training and fine-tuning, and autoscaling endpoints for serving, plus the storage, model registry and access controls around them. Everything runs on NVIDIA GPUs in India.

### Can I use TIR for both training and inference?

Yes, and most teams do both in the same project. Train with notebooks, scripts or scheduled pipeline jobs, then deploy the result as an endpoint using a pre-built container or your own image. The checkpoint doesn't have to move anywhere in between.

### Which models can I deploy?

The catalog covers DeepSeek R1 and V3, Llama 4 Maverick and Scout, GPT-OSS 120B, Hermes 3 405B, Dolphin Mixtral 8x22B and Sarvam-2B, each deployable in one click. Beyond the catalog you can point an endpoint at any Hugging Face or GitHub model, or serve your own custom model from the Model Repository.

### How big a model can TIR serve?

Large enough for frontier open-weight models. A single B200 carries 192 GB of GPU memory and an H200 carries 141 GB, and multi-GPU configurations go well beyond a single node. E2E runs 1,024 B200s in one Indian cluster. Past a certain size it becomes a capacity conversation rather than a platform limit, so talk to us about the specific model.

### How is TIR priced?

You pay for the GPU capacity your nodes, jobs and endpoints consume, at the published INR rates, from ₹49/hr on an L4 up to ₹671/hr on a B200. Commit for a term and the rate drops substantially: a B200 goes to ₹290/hr, an H100 to ₹155.90/hr. Endpoints that scale to zero stop billing when idle. You can check the current pricing here.

### Where does my training data live?

In India, under Indian jurisdiction. E2E's GPUs run in two regions, Delhi NCR and Chennai, and your data stays on that infrastructure. Seven security and quality certifications cover the platform, including SOC 2, ISO 27001 and PCI DSS, and the reports are published rather than described. For workloads that shouldn't touch the public internet, endpoints can sit behind VPC attachment and private networking.

### Where are the API docs?

The API reference is at docs.e2enetworks.com/api/tir, and the platform guides and tutorials are at docs.e2enetworks.com/docs/tir. Each endpoint also has an API Request tab in the console with a ready-made cURL and Python sample for that exact deployment.

## Get started

Start a dev node, fine-tune something small, and put it behind an endpoint before you decide on anything bigger. Our engineers will help you size the run. Pricing: https://www.e2enetworks.com/pricing · Talk to sales: https://www.e2enetworks.com/contact-sales

- [Get started with your first AI workload](https://myaccount.e2enetworks.com/accounts/signup)
- [Talk to an engineer](https://www.e2enetworks.com/contact-sales)

---

This Markdown is generated from the same data that renders https://www.e2enetworks.com/tir. Provider: E2E Networks Limited (NSE: E2E), India. Site index for AI agents: https://www.e2enetworks.com/llms.txt