---
title: "AI Dev Nodes: GPU VMs & Containers on TIR | GPU Notebook Cloud India"
description: "GPU-backed JupyterLab instances and containers for AI development in India. Prebuilt PyTorch/TensorFlow/vLLM images, SSH access, persistent workspace from 30GB free, stop-to-pause billing. H200 & H100 on demand."
url: "https://www.e2enetworks.com/ai-dev-nodes"
canonical: "https://www.e2enetworks.com/ai-dev-nodes"
provider: "E2E Networks Limited"
type: "Service"
keywords: ["gpu notebook cloud", "gpu vm for ai development india", "jupyter gpu cloud india", "cloud gpu instance for machine learning", "gpu jupyterlab india", "ai dev nodes"]
priceCurrency: "INR"
taxNote: "Prices exclude GST"
region: "India (Delhi NCR, Chennai)"
generated: "2026-09-21"
---

# AI Dev Nodes: GPU VMs & Containers on TIR | GPU Notebook Cloud India

> GPU-backed JupyterLab instances and containers for AI development in India. Prebuilt PyTorch/TensorFlow/vLLM images, SSH access, persistent workspace from 30GB free, stop-to-pause billing. H200 & H100 on demand.

Canonical page: https://www.e2enetworks.com/ai-dev-nodes

A GPU workspace that's ready before your coffee is. JupyterLab in the browser, SSH from your terminal, CUDA already configured. Launch PyTorch, TensorFlow or vLLM environments on H200/H100 — stop them when you're done and stop paying for compute.

## At a glance

Note: Indicative — see the live rate card for current pricing.

- **15+** — Prebuilt ML images
- **30GB** — Free persistent workspace
- **₹49/hr*** — GPU nodes start
- **99.9%** — Uptime SLA

## Pre-installed frameworks

- **Prebuilt images:** PyTorch, TensorFlow 2, Transformers, Diffusers, NVIDIA NeMo, RAPIDS, vLLM, ComfyUI, Triton, FastAI

## Platform capabilities

- **JupyterLab, zero setup** — Prebuilt images ship with JupyterLab, tested CUDA versions and framework dependencies configured. Click Launch App and you're in a notebook — run nvidia-smi to see your GPU.
- **Prebuilt, Base OS or your container** — Choose managed ML images, a minimal Ubuntu Base OS for full control, or any public/private Docker image. Build team-standard images with the TIR Image Builder and Container Registry.
- **Storage that fits the workload** — Persistent workspace at /home/jovyan (30GB free, expandable to multi-TB), EOS/Disk Datasets for large data, Shared File System for team access, Parallel File System for high-throughput jobs.
- **SSH like a real machine** — Toggle SSH access, attach one or more keys, open port 22 via a security group, and use your own tools — VS Code Remote, rsync, tmux. Start scripts automate environment setup on every boot.
- **Save Image** — Snapshot your whole environment — packages, configs, scripts, frameworks — as a reusable image. Restore it after experiments or launch identical nodes for the whole team.
- **Monitoring, alerts, events** — Live gauges for memory, workspace and ephemeral usage, CPU/RAM utilisation charts, threshold-based email alerts, and a full instance event log for troubleshooting.

## How it works

1. **Pick an image** — Pre-built (JupyterLab-ready), Base OS, or Custom. Enable "JupyterLab Supported" if your custom image was built for it.
2. **Choose compute** — GPU, CPU, Spot or Private Cluster. Pick the hardware plan (e.g. NVIDIA H200 SXM) and the vCPU/RAM configuration.
3. **Pricing & storage** — On-demand hourly or committed (1/3/6 months). Workspace starts at 30GB free; AES-256 storage encryption is a checkbox.
4. **Network & security** — Attach an SSH key (new keys download as .pem), pick a security group, optionally reserve a static IP or attach a VPC.
5. **Launch** — Review and launch — or do the whole thing via the REST API or Terraform.
6. **Work, stop, resume** — Compute bills only while Running. Stop overnight; your workspace is waiting in the morning.

## Storage tiers on a dev node

What survives a stop/start, and what does not.

| Storage tier | What it is | Persists across restarts | Use it for |
| --- | --- | --- | --- |
| Workspace | Pre-attached disk at /home/jovyan — 30GB free, expandable (grow-only) | Yes | Code, checkpoints, working files |
| Datasets (EOS / Disk) | Object-storage or disk-backed data mounts; disk attaches to one instance at a time | Yes | Training corpora, large files kept out of workspace |
| Shared File System (SFS) | Concurrent read/write mounts (e.g. /my_sfs) across instances | Yes | Team-shared data and configs |
| Parallel File System (PFS) | Striped high-throughput storage for simultaneous access | Yes | HPC-style and multi-process data loading |
| Ephemeral | Temporary space at /home/user, fixed 50GB | No | Scratch only — never production data |

## Terminal-first? Same node, your tools

Attach a key and a security group with port 22 open, then:

```bash
# illustrative — use the connection details from your instance page
chmod 400 my-tir-key.pem
ssh -i my-tir-key.pem jovyan@<instance-ip>

# verify your GPU
nvidia-smi

# keep persistent work in the workspace mount
cd /home/jovyan && git clone https://github.com/your-org/your-model.git
```

Files outside /home/jovyan may be lost on container restart — keep anything that matters in workspace, Datasets, SFS or PFS.

## Outgrown a single node?

Graduate to managed fine-tuning, multi-node Slurm clusters, or production endpoints without moving clouds.

- [Training & Fine-tuning](https://www.e2enetworks.com/training-fine-tuning)
- [Inference and Endpoints](https://www.e2enetworks.com/inference-endpoints)
- [Explore TIR - AI Platform](https://www.e2enetworks.com/tir)
- [GPU Pricing](https://www.e2enetworks.com/pricing#gpu-pricing)

## Frequently asked questions

### What's the difference between a TIR node and a regular GPU VM?

TIR nodes are container-based AI workspaces with JupyterLab, prebuilt ML images, dataset mounts and Save Image built in. If you want a plain virtual machine with full OS control, use Cloud Compute — same GPUs, different abstraction.

### Which GPUs can I attach?

Plans span current NVIDIA hardware — including H200 SXM and H100 — plus CPU-only and spot capacity. Configurations pair each GPU with vCPUs and RAM (e.g. 1× H200 / 30 vCPU / 375GB RAM). See the rate card for what's live.

### Do I pay while the node is stopped?

Compute billing stops with the node; workspace storage persists (storage charges may apply). Committed plans of 1, 3 or 6 months lower the hourly rate for always-on work.

### Can my team share an environment?

Yes — save your configured instance as an image and share it via the TIR Container Registry, so everyone launches from the identical environment. Shared File System mounts give common data access across instances.

### Is spot capacity available?

Yes, Spot is one of the instance types in the create flow — useful for interruptible experiments at lower cost. Checkpoint to persistent storage as a habit.

## Get started

Get hours of GPU notebook time, on demand. Pricing: https://www.e2enetworks.com/pricing · Talk to sales: https://www.e2enetworks.com/contact-sales

- [Start Free](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/ai-dev-nodes. Provider: E2E Networks Limited (NSE: E2E), India. Site index for AI agents: https://www.e2enetworks.com/llms.txt