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India's AI infrastructure just got a serious upgrade. Cyfuture AI — one of the country's most established cloud and AI providers — has officially launched GPU as a Service (GPUaaS), making enterprise-grade NVIDIA GPUs available on-demand to developers, enterprises, startups, and research institutions across India.
We're talking GPU cloud access — H100 SXM5, L40S, A100, and V100 — starting at just ₹39/hr, deployed from Indian data centres, fully compliant with India's DPDP Act, and provisioned in under 60 seconds. This isn't just a product launch. It's a pivot point for how Indian teams build and deploy AI.
What Cyfuture AI Just Launched
The announcement is straightforward: Cyfuture AI has opened its GPU as a Service platform to the public, giving any team in India — from a solo ML engineer to a 10,000-seat enterprise — on-demand access to the exact GPUs that power the world's most advanced AI workloads.
Cyfuture AI GPU as a Service — Now Live Across India
NVIDIA H100 SXM5, H100 PCIe, A100 (80 GB & 40 GB), L40S, and V100 GPUs are now available on-demand from Cyfuture AI's Indian data centres in Noida, Jaipur, and Raipur. Pay per hour. No upfront commitment. No capital expenditure. Fully aligned with the IndiaAI Mission's goal of democratising access to high-performance AI compute.
The launch also aligns directly with the IndiaAI Mission, under which the Government of India has been aggressively scaling the nation's common compute capacity. Union Minister Ashwini Vaishnaw announced in May 2025 that India's national compute pool had crossed 34,000 GPUs — and Cyfuture AI has been recognised as a key infrastructure partner in this push.
What this means practically: an AI startup in Pune can now train a 7B parameter LLaMA model this afternoon on an 8×H100 cluster, pay ₹1,752 for the hour, and not have to buy a single piece of hardware. A bank in Mumbai can run fraud detection inference in a dedicated, India-hosted environment that's already DPDP compliant. A research lab at an IIT can access InfiniBand-connected multi-node GPU clusters without queue waits.
This is the most competitively priced, India-compliant GPU cloud platform available to Indian teams today — with the infrastructure depth (NVLink clusters, InfiniBand, pre-built AI stacks) to support everything from startup experimentation to enterprise production workloads.
Why This Launch Matters for India's AI Ecosystem
To appreciate what's happening here, consider the alternative. Before platforms like this existed, an enterprise wanting to train a serious AI model had three options: spend ₹2–5 crore on on-premise GPU hardware and wait 6–12 months for delivery and setup, use a global hyperscaler and pay 60–70% more while watching data leave Indian jurisdiction, or simply not do it.
India-First Infrastructure
All GPU compute stays within Indian jurisdiction — Noida, Jaipur, and Raipur data centres, fully aligned with DPDP Act 2023 requirements.
No CapEx Barrier
Teams that couldn't justify ₹2–5 crore in GPU hardware can now access the same performance at ₹39–₹219/hr with zero upfront cost.
60-Second Provisioning
From signup to running workload in under a minute. No procurement cycles, no hardware setup, no driver configuration headaches.
Scales With You
Start with one GPU. Scale to 8×H100 NVLink within a node, or hundreds of GPUs across multi-node InfiniBand clusters — same platform, same API.
Built-In Compliance
ISO 27001:2022, SOC 2 Type II, and DPDP-aligned Data Processing Agreements — available out of the box, not as expensive add-ons.
Pre-Built AI Stack
PyTorch, TensorFlow, JAX, vLLM, CUDA 12.x, Hugging Face, DeepSpeed — all pre-installed, so you code instead of configure.
The Complete GPU Lineup: H100, L40S, A100 & V100
Here's what's available on day one. Each GPU occupies a distinct performance and price tier — picking the right one for your workload matters significantly for both speed and cost-efficiency.
- CUDA Cores 16,896
- FP16 TFLOPs 989
- Bandwidth 3.35 TB/s
- Best For LLM Training
- CUDA Cores 14,592
- FP16 TFLOPs 756
- Bandwidth 2.0 TB/s
- Best For AI Inference
- CUDA Cores 18,176
- FP16 TFLOPs 733
- Bandwidth 864 GB/s
- Best For Inference + VFX
- CUDA Cores 6,912
- FP16 TFLOPs 312
- Bandwidth 2.0 TB/s
- Best For ML Training
- CUDA Cores 6,912
- FP16 TFLOPs 312
- Bandwidth 1.6 TB/s
- Best For Research & NLP
- CUDA Cores 5,120
- FP16 TFLOPs 125
- Bandwidth 900 GB/s
- Best For ML Research
Which GPU Should You Choose?
| If You're Doing This… | Recommended GPU | Why |
|---|---|---|
| Pre-training or fine-tuning LLMs (30B+ params) | H100 SXM5 (multi-GPU cluster) | 989 TFLOPS FP16 + NVLink delivers lowest cost-per-token at scale |
| Production AI inference API (variable load) | L40S via Serverless GPU | Lowest cost-per-token for inference; auto-scales to zero idle cost |
| Fine-tuning mid-size models (7B–30B params) | H100 PCIe or A100 80 GB | 80 GB HBM accommodates full model + optimizer states in one pass |
| Computer vision, GenAI image/video pipelines | L40S | Ada Lovelace RT Cores + FP8 support — designed for this workload |
| Scientific HPC, FP64 simulations | A100 80 GB | 9.7 TFLOPS FP64 — unmatched for molecular dynamics, CFD |
| Prototyping, learning, small experiments | V100 | ₹39/hr is the cheapest entry point to real GPU compute |
GPU Pricing: What You'll Actually Pay
No confusing instance families. No hidden egress fees. Here's the complete pricing breakdown with reserved and spot savings included.
| GPU Model | VRAM | On-Demand | Reserved Savings | Spot Savings | Status |
|---|---|---|---|---|---|
| NVIDIA H100 SXM5 | 80 GB HBM3 | ₹219/hr | Up to 40% off | Up to 70% off | Instant |
| NVIDIA H100 PCIe | 80 GB HBM3 | ₹195/hr | Up to 40% off | Up to 70% off | Instant |
| NVIDIA A100 (80 GB) | 80 GB HBM2e | ₹195/hr | Up to 35% off | Up to 65% off | On-demand |
| NVIDIA A100 (40 GB) | 40 GB HBM2 | ₹170/hr | Up to 35% off | Up to 65% off | On-demand |
| NVIDIA L40S | 48 GB GDDR6 | ₹61/hr | Up to 40% off | Up to 70% off | Instant |
| NVIDIA V100 | 32 GB HBM2 | ₹39/hr | Up to 30% off | Up to 60% off | Scalable |
| NVIDIA H200 (Coming Q2 2026) | 141 GB HBM3e | TBD | — | — | Waitlist |
An equivalent H100 80 GB instance on AWS costs an estimated ₹650–740/hr. Cyfuture AI's H100 SXM5 starts at ₹219/hr — roughly 65–70% less, with Indian data residency and DPDP compliance documentation included. For a team running 8×H100 for a 30-day training run, that difference is several lakhs.
Start Running AI Workloads on India's Fastest GPU Cloud
H100, L40S, A100 & V100 GPUs. No commitment. No CapEx. Provision in 60 seconds from Indian data centres — DPDP compliant, ISO certified, 24/7 India-based support.
How Cyfuture AI's GPUaaS Works (Under the Hood)
For developers and architects evaluating this platform, it's worth understanding what actually happens between clicking "Launch Instance" and running your first training script. This is where the real infrastructure differentiation shows up.
Select Your Configuration
Choose your GPU model, instance count, NVMe storage size, and networking requirements through the dashboard or API. Select your pricing model: on-demand, reserved, spot, dedicated, or serverless. Configuration validates in real time — no waiting for a quote.
Instant Provisioning (Under 60 Seconds)
The orchestration layer allocates GPU resources from the nearest available Indian data centre. Resources are isolated at the hypervisor level — your workload shares no GPU memory, no network path, no storage with other tenants. This is the same level of isolation you'd get from owning dedicated hardware.
Pre-Configured Environment Ready
Your instance boots with the full AI stack already installed: PyTorch 2.x, TensorFlow 2.x, JAX/Flax, CUDA 12.x, cuDNN 9.x, NCCL 2.x, Hugging Face Transformers, vLLM, TGI, and JupyterLab. One-click templates for LLM fine-tuning (Axolotl + DeepSpeed) and inference serving (vLLM + Triton) are ready to use immediately.
Connect and Run Immediately
Access via SSH, JupyterLab, or the web terminal. Mount your datasets from Cyfuture Object Storage or any S3-compatible store. Your training scripts, inference servers, or batch jobs run immediately — no driver configuration, no library installation, no environment debugging.
Scale or Terminate — Billing Stops Within a Minute
Scale horizontally to GPU clusters — up to 8×H100 via NVLink within a single node, or multi-node configurations via InfiniBand HDR (200 Gb/s) for distributed training. Terminate any time — billing stops within the minute.
5 Deployment Models — Pick What Fits Your Workload
Not every AI workload has the same compute pattern. Cyfuture AI's GPUaaS offers five deployment models so you're never paying for a structure that doesn't match how you actually work.
If you're building an AI-powered product where inference load is variable — most GenAI apps, chatbots, image generation APIs — the Serverless GPU model eliminates idle cost entirely. Traditional GPU deployments mean paying for capacity during low-traffic hours (often 70–85% of the time). The inferencing as a service layer handles auto-scaling automatically.
Use Cases: Who Should Be Using This Right Now?
LLM Training & Fine-Tuning
Teams building or fine-tuning large language models need sustained multi-GPU throughput. With Cyfuture AI, you can spin up an 8×H100 NVLink cluster in minutes, mount your dataset, and use pre-configured Axolotl or DeepSpeed templates to start fine-tuning immediately. The fine-tuning as a service layer simplifies this further. An AI startup that fine-tuned a 13B LLaMA model completed the run in 18 hours at a total cost of ₹31,536 — vs ₹2.8 crore for equivalent on-premise hardware.
Banking, Fraud Detection & Credit Scoring
India's banking sector is deploying AI for fraud detection, credit scoring, KYC automation, and customer service at scale — but all of this requires data to stay within India and comply with RBI guidelines and the DPDP Act. Cyfuture AI's dedicated GPU instances provide fully isolated compute in Indian data centres with end-to-end encryption, audit logs, and full DPDP compliance documentation.
Startup AI Products — From Day One
If you're building an AI product and don't want to spend ₹2 crore on hardware before validating your idea, on-demand GPUaaS is the obvious answer. Access enterprise H100 or L40S compute from day one, pay only for what you use, and scale as your product grows. The startup credits programme gives early-stage teams free compute to build on Cyfuture AI infrastructure. For teams building enterprise AI solutions, this removes the infrastructure barrier that has historically delayed Indian AI startups.
Medical Imaging AI & Drug Discovery
Medical imaging AI and drug discovery simulations demand high GPU memory and strict data privacy. Cyfuture AI's dedicated instances support DICOM workloads, genomics pipelines (NVIDIA Clara, GATK), and molecular dynamics simulations (GROMACS, AMBER). ISO 27001-certified data centres with private VPC networking satisfy the privacy requirements that healthcare data demands.
Scientific HPC & National Research Labs
IITs, IISc, CSIR labs, and national research institutions running GPU-accelerated simulations can now access MPI-based multi-node Slurm clusters with InfiniBand interconnects — without competing for shared HPC queue slots. A national climate research lab reduced simulation wall-clock time from 19 days to 14 hours, with on-demand access that lets researchers iterate daily instead of monthly.
Video Rendering, VFX & Generative AI Apps
Animation studios and teams building GenAI image or video applications will find the L40S particularly compelling. Its Ada Lovelace architecture with third-generation RT Cores handles Blender Cycles, DaVinci Resolve, Unreal Engine 5, and Stable Diffusion workloads — while also delivering strong AI inference performance. The L40S is the only data centre GPU that handles both AI compute and 3D rendering natively.
Data Sovereignty & DPDP Compliance: The India Advantage
This is the section that matters most for enterprise buyers in regulated industries. Under the DPDP Act 2023, processing sensitive personal data on infrastructure outside Indian jurisdiction carries meaningful regulatory risk. Cyfuture AI is one of very few GPU cloud providers that eliminates this risk entirely — because the infrastructure is Indian by design, not by geographic accident.
Cyfuture AI vs AWS vs Google Cloud: Honest Comparison
| Feature / Metric | Cyfuture AI | AWS (India billing) | Google Cloud | Azure |
|---|---|---|---|---|
| H100 80 GB starting price | ₹219/hr | ~₹680–740/hr est. | ~₹620–700/hr est. | ~₹650–720/hr est. |
| India data residency | Yes — 3 DCs | Foreign jurisdiction | Foreign jurisdiction | Foreign jurisdiction |
| DPDP compliance docs | Full DPA + audit pack | Not available | Not available | Not available |
| Deployment time | <60 seconds | 5–15 minutes | 5–10 minutes | 5–10 minutes |
| 24/7 India-based support | GPU engineers | Generic global | Generic global | Generic global |
| NVLink multi-GPU (8×H100) | Available | Available | Available | Available |
| InfiniBand multi-node HPC | HDR 200 Gb/s | Available | Available | Limited |
| Pre-installed AI frameworks | 15+ frameworks | Basic AMIs | Basic images | Basic images |
| Startup credits programme | Available | Competitive | Competitive | Limited |
When Cyfuture AI Wins Clearly
- Any workload subject to DPDP Act or RBI data localisation requirements
- Cost-sensitive teams — 60–70% price advantage vs global hyperscalers
- Teams that need 24/7 India-based GPU infrastructure support
- Startups that need enterprise H100 access without enterprise-scale budget
- Inference workloads where serverless GPU eliminates idle cost
When a Hyperscaler Might Be the Right Call
- You're deeply embedded in an existing AWS/GCP ecosystem with tight service integrations
- You need globally distributed GPU compute across 20+ regions simultaneously
- Your workload relies on hyperscaler-native ML services with no migration path
- You need SLAs backed by trillion-dollar balance sheets for mission-critical production
Switch to India's Most Cost-Efficient GPU Cloud — No Migration Headaches
Our technical team helps containerised workloads move from AWS or GCP to Cyfuture AI in under a day. S3-compatible data transfer, Docker image pull from any registry, full Kubernetes GPU device plugin support. Enterprise SLAs available.
How to Get Started in Under 60 Seconds
Create Your Account
Sign up at cyfuture.ai. New accounts receive ₹100 in credits — enough to run an L40S instance for over an hour with no commitment. No credit card required to explore the dashboard.
Choose Your GPU & Environment
From the dashboard, select your GPU (H100, L40S, A100, or V100), instance count, storage, and pricing model. Choose a pre-built environment template: LLM fine-tuning, inference serving, computer vision, or a blank Ubuntu + CUDA instance for custom setups.
Launch and Connect
Click Launch. Within 60 seconds, your instance is ready. Connect via SSH with your key pair, open JupyterLab in the browser, or use the web terminal. Your full AI framework stack is already installed — no setup, no waiting.
Upload Data and Run
Mount your dataset from Cyfuture Object Storage, pull from an S3-compatible bucket, or transfer via rsync/rclone. Run your training script or inference server. For teams building production AI infrastructure, the GPU clusters documentation covers multi-node configuration, InfiniBand setup, and distributed training frameworks.
Scale or Wind Down
Add GPUs to your cluster, switch to a reserved instance for ongoing workloads, or terminate when you're done. Billing stops within the minute of termination. There's no penalty for stopping — and no minimum spend for on-demand instances.
Frequently Asked Questions
Cyfuture AI's GPUaaS platform offers six GPU options: NVIDIA H100 SXM5 (80 GB HBM3) from ₹219/hr, NVIDIA H100 PCIe (80 GB HBM3) from ₹195/hr, NVIDIA A100 80 GB from ₹195/hr, NVIDIA A100 40 GB from ₹170/hr, NVIDIA L40S (48 GB GDDR6) from ₹61/hr, and NVIDIA V100 from ₹39/hr. All prices are on-demand with no minimum commitment. Reserved instances (3–12 months) offer up to 40% savings; spot instances offer up to 70% off. The NVIDIA H200 (141 GB HBM3e) is launching in Q2 2026 — join the waitlist at cyfuture.ai/gpu-as-a-service.
No minimum commitment for on-demand instances. You can launch a single V100 for one hour at ₹39 and stop there. Reserved instances have a minimum term of 3 months. Spot instances also have no minimum commitment — they can be reclaimed with a 2-minute warning, making them suitable for checkpointable workloads. Enterprise contracts with dedicated capacity and custom SLAs are available for teams with large, predictable workloads.
All Cyfuture AI GPU infrastructure is hosted exclusively in Indian data centres (Noida, Jaipur, Raipur). Your data never leaves Indian jurisdiction. The company provides Data Processing Agreements aligned with the Digital Personal Data Protection Act 2023, ISO 27001:2022 certification, SOC 2 Type II attestation, and a DPDP compliance pack (audit-ready documentation for your DPO). For BFSI customers, the infrastructure architecture aligns with RBI's 2023 cloud adoption framework.
Yes. Start with a single GPU and scale horizontally. Within a single node, up to 8×H100 GPUs can be connected via NVLink (900 GB/s bidirectional bandwidth). For larger distributed training jobs, Cyfuture AI supports multi-node clusters connected via InfiniBand HDR (200 Gb/s), compatible with MPI, NCCL, and PyTorch DDP/FSDP. Slurm-based HPC scheduling is also supported for research institutions running large simulation workloads.
All Cyfuture AI GPU instances include PyTorch 2.x (with FlashAttention 2 and FSDP), TensorFlow 2.x, JAX/Flax, CUDA 12.x, cuDNN 9.x, NCCL 2.x, Hugging Face Transformers, vLLM, TGI (Text Generation Inference), llama.cpp, DeepSpeed, and JupyterLab. One-click environment templates are available for LLM fine-tuning (Axolotl + DeepSpeed) and inference serving (vLLM + Triton). NVIDIA Nsight Systems for GPU profiling is also pre-installed.
For equivalent GPU specs in India, Cyfuture AI is typically 60–70% less expensive than AWS or Google Cloud. The H100 SXM5 starts at ₹219/hr on Cyfuture AI vs an estimated ₹650–740/hr equivalent on AWS. Beyond the price gap, Cyfuture AI offers India-specific advantages that global hyperscalers can't match: domestic data residency for DPDP compliance, DPDP-aligned Data Processing Agreements, and 24/7 India-based GPU infrastructure support.
Meghali covers AI infrastructure, GPU computing, and enterprise cloud technology for Cyfuture AI. She specialises in translating complex hardware and systems topics into clear, actionable content for developers, ML engineers, and enterprise technology leaders evaluating AI compute solutions.
