---
title: "Model Training & Fine-tuning on TIR | LLM Training Platform India"
description: "Fine-tune LLMs and diffusion models or run multi-node distributed training on H100/H200 clusters in India. Managed pipelines, HuggingFace datasets, WandB tracking, Slurm scheduling — billed in INR."
url: "https://www.e2enetworks.com/training-fine-tuning"
canonical: "https://www.e2enetworks.com/training-fine-tuning"
provider: "E2E Networks Limited"
type: "Service"
keywords: ["fine-tune llm india", "llm training platform", "distributed training gpu cloud", "fine tuning platform h100", "slurm cluster cloud india", "llm fine-tuning india", "gpu training cluster india"]
priceCurrency: "INR"
taxNote: "Prices exclude GST"
region: "India (Delhi NCR, Chennai)"
generated: "2026-09-11"
---

# Model Training & Fine-tuning on TIR | LLM Training Platform India

> Fine-tune LLMs and diffusion models or run multi-node distributed training on H100/H200 clusters in India. Managed pipelines, HuggingFace datasets, WandB tracking, Slurm scheduling — billed in INR.

Canonical page: https://www.e2enetworks.com/training-fine-tuning

Fine-tune and train LLMs without building the stack. Launch a managed fine-tuning job in five clicks, or spin up a Slurm-native multi-node cluster on H100 and H200. Datasets, checkpoints, tracking and scheduling — handled. You bring the data.

## At a glance

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

- **H100 / H200** — Training-grade GPUs
- **₹362/hr*** — H100 starting rate
- **Slurm** — Native scheduling
- **99.9%** — Uptime SLA

## Platform capabilities

- **Managed fine-tuning pipelines** — Foundation Studio walks you from model selection to launch: pick a base model, connect your Hugging Face token, configure data and hyperparameters, choose a GPU, hit Launch. Guides cover Mistral-7B, Gemma-7B, Llama 3.2 Vision, Stable Diffusion and SDXL.
- **Dataset management built in** — Bring custom .jsonl, .json or parquet data via EOS-backed TIR Datasets, or link any public/private Hugging Face dataset. Set a validation split ratio and map your fields with a prompt configuration template.
- **Hyperparameters you control** — Training type, epochs, learning rate, max steps, batch size, gradient accumulation and quantization options — plus a debug mode for fast dry runs before you commit GPU hours.
- **WandB experiment tracking** — Add your Weights & Biases integration at job creation for full run tracking. Every run also streams training logs in the TIR console for debugging failed steps.
- **Checkpoints → Model Repository** — Completed jobs push every checkpoint and adapter to a TIR Model Repository on E2E Object Storage — deployable to an inference endpoint in a few clicks.
- **Slurm-native Training Clusters** — For pre-training and large-scale runs: multi-node clusters with Slurm scheduling (via Slinky + Pyxis), pre-installed CUDA/NCCL images, PyTorch DDP, Lightning and OpenMPI support, and custom containers via Enroot.

## How it works

1. **Create the job** — Open Foundation Studio → Fine-Tune Models → Create Fine-Tuning Job. Name it, pick your base model, and attach your Hugging Face token (needed for gated models like Llama).
2. **Prepare the dataset** — Choose CUSTOM (upload .jsonl/parquet to an EOS dataset) or HUGGING FACE. Set the task, validation split ratio, and prompt configuration mapping your input/output fields.
3. **Set hyperparameters** — Epochs, learning rate, max steps, batch size, gradient accumulation, quantization. Optionally wire up WandB and enable debug mode for a quick validation pass.
4. **Pick a GPU & launch** — Select your machine — H100, H200 or A100 configurations — and click LAUNCH. Watch live logs per run while the job trains.
5. **Collect the model** — The fine-tuned model lands in your Model Repository with all checkpoints and adapters preserved.
6. **Deploy it** — Hit Deploy on the model repo: choose GPU, replicas and endpoint name, and serve it as an autoscaling API.

## Fine-tuning studio vs training cluster

Which path fits the job.

| Dimension | Foundation Studio fine-tuning | Dedicated training cluster |
| --- | --- | --- |
| Best for | LoRA/adapter fine-tunes of open LLMs & diffusion models | Multi-node distributed training and pre-training |
| Setup | Guided console wizard, no code required | Cluster in minutes; submit jobs via sbatch/srun |
| Frameworks | Managed pipeline (HF Transformers under the hood) | PyTorch DDP, PyTorch Lightning, Slurm, OpenMPI |
| Images | Managed by TIR | Ubuntu Slurm pre-built (CUDA, NCCL, drivers) or any Docker/OCI image via Enroot |
| Data | EOS datasets or Hugging Face | Datasets, Shared File System (SFS), Parallel File System (PFS) |
| Scaling | Single-job GPU selection | Elastic node scaling on a live cluster, no teardown |
| Resilience | Checkpoints saved to Model Repository | Node-level fault isolation, auto recovery, XID error visibility with one-click worker restart |
| Observability | Run logs + WandB | Live squeue, per-node DCGM GPU metrics, controller & node logs |
| Team access | Project-based | Persistent Login User Management — per-user SSH keys and isolated home directories |

## Submit a distributed job the way HPC teams already do

Training Cluster nodes arrive with drivers, CUDA, NCCL and PyTorch ready. Checkpoint to shared storage so runs resume after any node event.

```bash
# illustrative — adapt paths/partitions to your cluster
#!/bin/bash
#SBATCH --job-name=llama-ft
#SBATCH --nodes=4
#SBATCH --gres=gpu:8
#SBATCH --partition=slinky
#SBATCH --time=24:00:00

srun torchrun \
  --nnodes=$SLURM_NNODES \
  --nproc_per_node=8 \
  --rdzv_backend=c10d \
  train.py --data /my_pfs/tokenized \
           --checkpoint-dir /my_sfs/checkpoints
```

Monitor the run from the console: Jobs tab for queue state, Monitoring tab for per-node GPU utilisation, Logs tab for controller and node output.

## Training is half the loop

Serve what you train, on the same platform and the same GPUs.

- [Inference and Endpoints](https://www.e2enetworks.com/inference-endpoints)
- [AI Dev Nodes](https://www.e2enetworks.com/ai-dev-nodes)
- [Explore TIR - AI Platform](https://www.e2enetworks.com/tir)
- [GPU Pricing](https://www.e2enetworks.com/pricing#gpu-pricing)

## Frequently asked questions

### Which models can I fine-tune?

Foundation Studio has guided flows for popular open models — documented walkthroughs include Mistral-7B, Gemma-7B, Llama 3.2 11B Vision, Stable Diffusion (Dreambooth/Textual Inversion) and SDXL — and any public or gated Hugging Face model works with your HF token. Prefer code? Run LLaMA-Factory on a TIR node.

### What data formats are supported?

Custom datasets in .json, .jsonl or parquet uploaded to EOS-backed TIR Datasets, or any Hugging Face dataset linked directly. Incorrect formats fail the run, so the wizard shows the expected structure up front.

### Do I need Kubernetes or Slurm expertise for distributed training?

No. Training Clusters deliver full Slurm via Slinky (Kubernetes-native Slurm) with queueing, priorities and multi-node coordination managed for you. You submit jobs; the console shows queue, node health and GPU metrics.

### What happens if a node fails mid-run?

Failures are isolated to the node; the cluster recovers automatically after restart. Checkpoint periodically to SFS/PFS and resume from the last checkpoint. The Nodes tab surfaces NVIDIA XID hardware errors in real time with one-click worker restart.

### What does training cost?

GPU time is billed hourly in INR — H100 from ₹362/hr*, H200 from ₹436/hr* (₹189.2/hr* committed) — with committed plans for longer runs. *Indicative — see the live rate card for current pricing.

## Get started

Real GPUs, no waitlist. 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 ML Engineer](https://www.e2enetworks.com/contact-sales)

---

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