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GPU Cloud India
for AI & HPC — Deploy in Seconds

NVIDIA RTX PRO 6000 — hosted inside India. Train LLMs, run inference, render, HPC. DPDP compliant. INR billing, no forex risk.

Nvidia RTX PRO 6000From ₹49/hr99.9% UptimeDPDP Compliant

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GPU PLATFORM

Not just GPUs.
A full AI platform.

Multi-GPU clusters, distributed training, model serving, storage — everything to go from experiment to production.

Multi-GPU Clusters

Scale from 1 to 8 GPUs per node. NVLink for high-bandwidth GPU-to-GPU communication. InfiniBand networking for multi-node distributed training.

NVLinkInfiniBand1–8 GPU/node

AI Inference Serving

Deploy production LLM endpoints with vLLM, TGI, or TRT-LLM. Auto-scaling replicas, A/B testing, model versioning, and sub-10ms latency for India users.

vLLMTGIAuto-scale

MLOps Pipeline

End-to-end ML pipeline management. Kubeflow, MLflow experiment tracking, Jupyter Hub, dataset versioning, automated retraining — India-hosted.

KubeflowMLflowJupyter Hub

High-Speed Storage

NVMe local storage for hot data, distributed NFS for shared datasets, and S3-compatible object storage for model checkpoints — all in India.

NVMe LocalNFS ShareS3 Buckets

Spot Instances

Save up to 70% with spot GPU pricing for fault-tolerant training jobs. Automatic checkpoint integration ensures no work is lost on preemption.

70% SavingsAuto-checkpointPreemptible

Kubernetes for AI

GPU-enabled Kubernetes clusters with NVIDIA device plugins, GPU operator, and Helm chart marketplace. Auto-scale your AI workloads dynamically.

GPU OperatorDCGMHelm Charts

WORKLOADS

What will you
build on India's GPU?

🧠

LLM Training & Fine-tuning in India

MULTI-GPU · NVLINK

Train or fine-tune large language models — Llama, Mistral, Gemma, Falcon, or your custom architecture — on India's H100 and H200 clusters. NVLink ensures maximum GPU-to-GPU bandwidth. All training data stays in India, meeting DPDP requirements for Indian AI companies.

Recommended GPU

H100 / H200 × 8

Frameworks

PyTorch + DeepSpeed

Supported models

7B → 405B params

Spot savings

Up to 70%

Deploy Training Cluster →

GPU DATA CENTRES

Train in India.
Serve in India.

Major GPU hyperscalers (AWS, Azure, GCP) don't offer their latest GPUs in Indian regions. You'd send data to Singapore or the US — adding 100–200ms latency and violating data residency laws. CloudTechTiq runs H100 and H200 inside India.

Mumbai GPU DCIN-MUM-1Online

H200 · H100 · A100 · V100 · L4 · Tier III · Jio + Airtel

Noida GPU DCIN-NOI-1Online

H100 · A100 · V100 · Tier III · BSNL + Airtel · Delhi NCR <3ms

🇮🇳 DPDP Act · RBI · SEBI · MeitY — all training & inference data stays in India by default

CloudTechTiq vs India GPU competitors.

E2E Networks, NeevCloud, DigitalOcean — how do they really compare?

Feature	CloudTechTiq ✦	E2E Networks	NeevCloud	DigitalOcean
RTX 6000 GPU in India	✓	✓	✓	✗
Fully Managed GPU	✓	Self-service	Self-service	Self-service
INR Billing + UPI	✓	✓	✓	USD only
Mumbai + Noida DCs	Both	Multi-zone	Indore only	Bangalore only
MLOps Platform (Managed)	✓	✗	✗	✗
VPS + Dedicated + GPU	All three	GPU + VPS	GPU only	VPS + GPU
Office 365 / Azure Managed	✓	✗	✗	✗
DPDP Act Compliance Advisory	✓	Infra only	Infra only	✗
24/7 India Support Team	✓ Human	Ticket	Ticket	No India team

Frequently Asked Questions

What NVIDIA GPUs are available in India?+

CloudTechTiq offers NVIDIA H200 SXM5 (141GB HBM3e), H100 SXM5 (80GB HBM3), A100 SXM4 (80GB HBM2e), V100 SXM2 (32GB HBM2), and L4 PCIe (24GB GDDR6) GPU instances hosted in Mumbai and Noida data centres in India.

How much does GPU cloud cost in India?+

Pricing starts at ₹49/hr for L4 GPU on-demand, ₹149/hr for V100, ₹249/hr for A100, ₹349/hr for H100, and ₹430/hr for H200. Spot instances save up to 70% — H100 spot is ₹105/hr. All pricing is in INR with no forex conversion risk.

How is CloudTechTiq different from E2E Networks for GPU?+

E2E Networks is a strong self-service GPU compute platform. CloudTechTiq is fully managed — we set up your GPU environment, configure CUDA/frameworks, manage MLOps pipelines, and provide 24/7 human support. We also bundle VPS, dedicated servers, Office 365, Azure, and cybersecurity under one India-based team — E2E does none of this.

Can I train LLMs on your GPU cloud?+

Yes. H100 and H200 clusters with NVLink are optimised for LLM training and fine-tuning. We support Llama 3, Mistral, Gemma, Falcon, and custom architectures using PyTorch + DeepSpeed + Megatron-LM. Multi-node distributed training via InfiniBand is available for clusters requiring 16+ GPUs.

Does GPU data stay in India?+

Yes. All GPU compute, model training data, and inference data stays within our Mumbai and Noida data centres inside India — fully compliant with DPDP Act 2023, RBI data localisation, and SEBI cloud guidelines. Unlike AWS, Azure, or GCP which route GPU workloads to Singapore or the US.

What is a spot GPU instance?+

Spot instances are unused GPU capacity offered at up to 70% discount compared to on-demand pricing. They may be preempted with ~2 minutes notice when demand rises. For training jobs, we integrate automatic checkpointing so no work is lost on preemption. Ideal for batch training, fine-tuning, and non-latency-sensitive inference.

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