Hourly cloud GPUs get expensive when a run lasts weeks.
You rent the card for the month at one price. A training job that runs for twenty days costs the same as one that runs for two.
Model training
GPU servers for model training and fine-tuning in India.
Fine-tune or train on A100, H100, H200 or B200 cards rented by the month, from ₹99,000 per A100. One card or a multi-GPU node, with storage and checkpointing set up before the first epoch.
Common problems
What we fix.
The run died at epoch 40 and we lost all of it.
We set up fast NVMe scratch and regular checkpoints to persistent storage, so a crash costs you minutes, not days.
We need more than one GPU and don’t know whether NVLink matters.
For a model that fits one card, PCIe is fine. For sharded training across cards, interconnect speed decides throughput. We quote the node with the topology written down.
The GPU sits idle because data loading is the bottleneck.
We size host CPU, RAM and local NVMe for your dataset and check the input pipeline on a short run before you commit to the full one.
Ways to start
Choose where to start.
Each GPU card is rented for at least one month at the monthly price shown. Prices exclude applicable taxes.
NVIDIA A100 80GB
₹99,000per month
For LoRA and QLoRA fine-tuning, vision models and research runs that fit in 80GB.
- 80GB of HBM on each card
- Mature PyTorch and CUDA support
- Lowest monthly cost for an 80GB card
NVIDIA H100 80GB
₹1,80,000per month
For teams that iterate often and want each run to finish sooner. The default choice for transformer fine-tuning.
- FP8 and transformer engine support
- Markedly higher throughput than A100 on the same job
- Scales to multi-card nodes
H200 or B200 node
Custom quote
For full fine-tunes of 70B-class models or pre-training runs that need several cards with fast interconnect.
- Card count and interconnect stated in writing
- Shared NVMe scratch and checkpoint storage
- Environment set up and validated on a test run
Common questions.
A100 or H100 for fine-tuning?
Both have 80GB, so the same models fit. The H100 at ₹1,80,000 a month finishes the same job much faster than the A100 at ₹99,000, so it wins when you run many experiments. The A100 wins when the budget matters more than wall-clock time.
How many GPUs do I need to fine-tune a 70B model?
With QLoRA, one H100 or H200 is enough. A full-parameter fine-tune needs the weights, gradients and optimiser state in memory at once, which means a multi-card node. The H200 at ₹2,20,000 per card and B200 at ₹4,59,000 per card are the usual choices there.
Can I rent a GPU for just a few days?
The minimum term is one month. If you only need a short burst, we can help you plan the run so the month is used well, for example by lining up several experiments back to back.
Do you set up the training environment?
Yes. Single-node deployment starts at ₹50,000 as a one-time project: drivers, CUDA, PyTorch or your container, dataset storage, checkpoint paths and a short validation run.
How long will my training run take?
It depends on the model, dataset, precision and code. We run a short timed test on the quoted card first, measure throughput and give you a realistic estimate before you commit to the month.
Where does our training data sit?
On NVMe storage attached to your node in India, reserved for you. You can upload it over SSH or from object storage, and we wipe it when the term ends if you ask us to.
Can the same server hold our data pipeline and experiment tracking?
Yes. The node has host CPU, RAM and NVMe sized for the dataset, so preprocessing, data loading, checkpoints and a tracker such as MLflow or Weights & Biases run next to the GPU instead of across a network. If several people share it, we set up separate users, shared dataset storage and a job scheduler so runs do not collide.