Rent Cloud GPUs
in India
On-demand NVIDIA GPUs with INR pricing, minute-level billing, and instant provisioning. No setup fee, no minimum commitment.
Trusted by leading Indian companies and institutions
What is GPU Cloud Computing?
GPU cloud computing provides on-demand access to powerful NVIDIA GPUs for AI training, deep learning, machine learning, and inference workloads — without buying hardware. Instead of investing ₹30 lakh+ in an H100 GPU, you can rent one by the minute from a cloud provider like JarvisLabs.
JarvisLabs makes GPU cloud computing accessible in India with INR pricing, instant provisioning, and pre-configured ML environments. Launch a GPU instance in under 60 seconds with PyTorch, TensorFlow, or JAX pre-installed. Scale from a single GPU for prototyping to 8x H100s for distributed training — and only pay for what you use.
Built for AI developers in India
INR Pricing
All prices displayed in Indian Rupees for easy budgeting. No currency conversion surprises.
Minute-Level Billing
Pay only for the exact GPU minutes you use. No hourly minimums, no reserved instance lock-in.
Instant Provisioning
Launch GPU instances in under 60 seconds with pre-built ML frameworks. No waiting, no manual setup.
No Setup Fee
Zero upfront cost. No setup fee, no minimum commitment, no long-term contracts. Start with $10.
GPU Cloud Pricing in India
Transparent pricing in INR. No hidden fees, no setup cost, no minimum commitment. Minute-level billing across all GPU types.
Prices shown in INR (1 USD = ₹81). Billing in USD at the displayed rate. Minute-level billing. Paused instances only incur storage fees.
JarvisLabs vs AWS, Azure & GCP
See why AI developers in India choose JarvisLabs over hyperscaler GPU offerings.
Launch AI Workspaces
in Minutes
GPU Cloud for Every AI Workload
LLM Training & Fine-Tuning
Recommended: H100 / H200Train and fine-tune large language models like LLaMA, Mistral, and Gemma. Use LoRA or QLoRA for efficient adaptation on a single GPU, or scale to 8x H100s for full fine-tuning.
AI Inference & Deployment
Recommended: A6000 / L4Deploy production inference endpoints with vLLM, TGI, or Triton. Serve models with sub-second latency using optimized GPU instances. Scale up during peak hours, pause when idle.
Computer Vision & Image Generation
Recommended: A100 / A6000Run Stable Diffusion, ComfyUI, or custom vision models. Train object detection, segmentation, and classification models on high-VRAM GPUs with pre-installed CUDA and cuDNN.
Research & Experimentation
Recommended: A5000 / RTX 6000 AdaPrototype quickly with pre-built PyTorch, TensorFlow, and JAX environments. Minute-level billing means you can experiment freely without worrying about costs. Pause anytime, resume later.
Your GPU,
Your Terminal
Manage your entire GPU lifecycle from the command line or Python code.
Instance Management
Create, pause, resume, and destroy GPUs from the CLI or Python.
jl create --gpu A100Managed Runs
Upload code, install deps, run scripts, stream logs — one command.
jl run train.py --gpu A100File Transfer & SSH
Copy files and SSH into instances without leaving your terminal.
jl ssh <id>Agent-Native
Let Claude Code, Cursor, or Codex drive GPU experiments.
jl setupbash$ pip install jarvislabsLoved by AI Practitioners
“The cheapest would probably be Jarvis Labs. They're very popular in our community. https://jarvislabs.ai”
“Looking to run a bigger model on GPU at a cheaper price, give @jarvislabsai a try and thank me later 😀 Got my machine up and running in a few mins 🔥 Thank you @vishnuvig!”
“If you haven't tried http://jarvislabs.ai you should. Fast start times, well priced, simple UI, easy billing. I have always chosen between config crap, expensive price, or long launch times. This is the first platform that I've seen that I think gets all of these items right!”
“The incredible https://jarvislabs.ai by @vishnuvig IMO offers one of the best pricing for renting compute 💰 TIL that they are completely bootstrapped & operate out of India! 🙏 It's really fulfilling to hear one of the best startups from @fastdotai classroom is from the country!”
“@jarvislabsai is the best GPU cloud provider for DL practitioners out there, period. More than once I had a question and support helped me in minutes, not only fast but so so friendly...”
“Addict to @jarvislabsai. Less branded than others on the surface but super simple. Great GPUs (training on 8 x A100s is amazing). This beats Paperspace premium accounts, Colab with custom VMs... I loved RunwayML as well ...”
Start Training in 3 Steps
Sign Up & Add Credits
Create a free account at jarvislabs.ai. Add credits starting from $10. No setup fee required.
Choose GPU & Template
Select your GPU (H200, H100, A100, A6000, etc.) and pick a pre-built template with PyTorch, TensorFlow, or JAX.
Launch & Code
Your instance launches in under 60 seconds. Access via JupyterLab, VS Code Web, or SSH. Start training immediately.
JarvisLabs offers NVIDIA GPUs at competitive hourly rates in INR: H200 SXM at ₹271/hr (141 GB VRAM), H100 SXM at ₹218/hr (80 GB), A100 40GB at ₹72/hr, A6000 at ₹64/hr (48 GB), and L4 at ₹36/hr (24 GB). Compare this to buying an H100 outright at ₹30 lakh+ — you can rent one for over 13,000 hours before reaching the purchase price. All billing is per-minute with no minimum commitment.
It depends on your workload. For training large language models (70B+ parameters), the H100 or H200 with 80-141 GB VRAM are ideal — they support NVLink for multi-GPU scaling. For fine-tuning and inference, the A100 (40/80 GB) or A6000 (48 GB) offer the best performance per rupee. For students and researchers doing experiments, the A5000 at ₹40/hr or L4 at ₹36/hr are cost-effective starting points. All GPUs come with pre-installed CUDA, cuDNN, PyTorch, and TensorFlow.
Yes. JarvisLabs is typically 2-3x cheaper than hyperscalers for equivalent GPU hardware. An A100 on AWS costs roughly ₹250-300/hr, while JarvisLabs offers it at ₹72/hr. Beyond pricing, we offer per-minute billing with no minimums, no setup fee, and no reserved instance complexity. AWS charges per-second for on-demand but reserved instances require 1 or 3-year commitments. Our INR pricing display lets you budget accurately without worrying about USD exchange rate fluctuations.
GPU-as-a-Service (GPUaaS) means you get on-demand access to NVIDIA GPUs without owning hardware. On JarvisLabs: sign up, add credits (starting from $10), pick a GPU and a pre-built template (PyTorch, TensorFlow, JAX, ComfyUI, etc.), and your instance launches in under 60 seconds. You get full root access via JupyterLab, VS Code Web, or SSH. Pause when idle to stop GPU charges, resume anytime. Your data and environment persist across sessions.
Absolutely. JarvisLabs bills per minute — there is no minimum rental period. You can rent an H100 for 30 minutes of training, pause it, and resume later. This makes it ideal for intermittent workloads like fine-tuning runs, Stable Diffusion image generation, or weekend research projects. Many Indian researchers and students use this to run experiments without committing to monthly plans.
All prices on JarvisLabs are displayed in INR for transparent budgeting. Payments are processed via Stripe in USD at the displayed INR rate. You can pay with international credit/debit cards and net banking. UPI is not supported currently. There are no hidden currency conversion fees from our side — what you see in INR is what you pay.
JarvisLabs does not offer a free tier, but you can start with as little as $10 (approximately ₹810) — which gives you over 20 hours on an A5000 at ₹40/hr. This is significantly more practical than free-tier options like Google Colab, which have session limits, queue times, and random disconnections. Several IITs and universities use JarvisLabs for ML coursework and research projects because of the reliable access and per-minute billing.
Buying a GPU server with an H100 costs ₹30-50 lakh upfront, plus electricity, cooling, and maintenance. Renting on JarvisLabs, you pay ₹218/hr only when the GPU is running. For context: at 8 hours/day usage, the monthly cost is approximately ₹52,294 — a fraction of the purchase price. You also get instant access to multiple GPU types without procurement delays.
Yes. JarvisLabs supports up to 8 GPUs per instance for distributed training. Multi-GPU configurations are available for H200, H100, A100, A6000, and other GPU types. This is essential for training large language models, running DeepSpeed or FSDP distributed training, and processing large datasets. Each multi-GPU instance comes with high-speed NVLink interconnects for maximum throughput.
JarvisLabs operates GPU infrastructure accessible from India with low-latency connectivity. Your instances run in isolated environments with private networking, ensuring data isolation and security. Each instance has persistent storage that survives pauses and restarts — your code, datasets, and model checkpoints are preserved until you explicitly delete the instance.
Three steps: (1) Sign up at jarvislabs.ai — no documents or KYC required. (2) Add credits starting from $10 via credit/debit card or net banking. (3) Select a GPU, pick a pre-built template (PyTorch, TensorFlow, JAX, etc.), and launch. Your instance is ready in under 60 seconds with JupyterLab, VS Code Web, and SSH access. The entire process takes under 5 minutes from signup to running your first training job.
Pausing stops all GPU charges immediately. You only pay for storage while paused (₹0.0113/GB/hour). Your environment, installed packages, datasets, and model checkpoints are all preserved. Resume anytime to continue exactly where you left off. Deleting an instance is permanent — all data is removed and no further charges apply. We recommend downloading important files before deletion.