Trusted by 20,000+ Businesses Rent NVIDIA Cloud GPU Server in India
Avoid quota waits and long procurement cycles. Get on-demand access to A100, L40S, L40 and more in minutes.
Pay-as-You-Go
Flexible Billing
AI Frameworks
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Up to 60%
Lower Cost*
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Transparent pricing for NVIDIA GPUs
| GPU Machine | VRAM | Best For | Configuration (Starting plan) | Monthly Price (Starting at) | Action |
|---|---|---|---|---|---|
| NVIDIA L40s GPU (Most Popular) | 48GB GDDR6 | GenAI, 3D, VFX | 1× GPU · 16 vCPU · 64GB RAM | ₹60,000/mo Save ₹72,000 annually | Select → |
| NVIDIA L4 GPU | 24GB GDDR6 | Inference, video AI | 1× GPU · 8 vCPU · 32GB RAM | ₹25,500/mo Save ₹30,600 annually | Select → |
| NVIDIA A2 GPU | 16GB GDDR6 | Edge inference | 1× GPU · 8 vCPU · 32GB RAM | ₹12,000/mo Save ₹14,400 annually | Select → |
| NVIDIA A30 GPU | 24GB HBM2 | AI and HPC | 1× GPU · 8 vCPU · 32GB RAM | ₹35,000/mo Save ₹42,000 annually | Select → |
| NVIDIA RTX A6000 | 48GB GDDR6 | AI and design | 1× GPU · 16 vCPU · 64GB RAM | ₹37,500/mo Save ₹45,000 annually | Select → |
| NVIDIA RTX A6000 Ada | 48GB GDDR6 | Rendering and AI | 1× GPU · 16 vCPU · 64GB RAM | ₹53,500/mo Save ₹64,200 annually | Select → |
| NVIDIA A100 (80GB) | 80GB HBM2 | AI supercomputing | 1× GPU · 16 vCPU · 128GB RAM | ₹90,000/mo Save ₹108,000 annually | Select → |
| NVIDIA H100 (HGX) | 80GB | LLM training | 1× GPU · 26 vCPU · 250GB RAM | ₹180,000/mo Save ₹216,000 annually | Select → |
| NVIDIA H200 (NVL) | 141GB HBM3e | GenAI at scale | 1× GPU · 24 vCPU · 282GB RAM | ₹220,000/mo Save ₹264,000 annually | Select → |
| RTX PRO 6000 (96GB) | 96GB GDDR7 (ECC) | AI factories | 1× GPU · 16 vCPU · 128GB RAM | ₹95,636/mo Save ₹114,760 annually | Select → |
Tap into fast, flexible GPU power for AI, ML, inference, and rendering.
Launch Open-Source Models on GPUs in a Few Clicks
Run DeepSeek, LLaMA, Mistral, Stable Diffusion and 40+ more models, and bring them live on AceCloud GPUs with minimal setup.
High-Performance Cloud GPU Solutions
Scale up effortlessly with 2, 4, or 8 GPUs on a single VM, perfect for high-performance computing, deep learning, and demanding workloads that need serious power.
Power your workloads with enterprise-grade NVIDIA GPUs, featuring CUDA, Tensor and RT cores for superior parallel processing, deep learning and real-time rendering.
Get ultra-fast, PCIe Gen5 NVMe storage directly attached to your instance delivering up to 12,00,000 IOPS for low-latency, high-throughput performance.
Leverage GPU passthrough to access dedicated GPU resources directly attached to your VM ensuring better performance, improved efficiency, and full control over your workloads.
Utilize popular deep learning frameworks and libraries such as TensorFlow, Scikit Learn, PyTorch to eliminate dependencies and simplify complex use cases at high speed.
We offer Tesla cards per instance and the required potency that helps businesses deliver 2X performance and simplify multiplex use cases of deep learning and graphic computing.
Why AceCloud Beats Hyperscalers for GPUs
| What Matters |  | Hyperscalers |
|---|---|---|
| GPU pricing Cost structure | Monthly plans with up to 60% savings. | Higher long-run cost for steady use. |
| Billing & Egress Transparency | Simple bill with predictable egress. | Many line items and surprise charges. |
| Data Location Regional presence | India-first GPU regions, low latency. | Fewer India GPU options, higher latency/cost. |
| GPU Availability Access to capacity | Capacity planned around AI clusters. | Popular GPUs often quota-limited. |
| Support Help when you need it | 24/7 human GPU specialists. | Tiered, ticket-driven support; faster help extra. |
| Commitment & Flexibility Scaling options | Start with one GPU, scale up. | Best deals need big upfront commits. |
| Open-source & Tools Ready-to-use models | Ready-to-run open-source models, standard stack. | More DIY setup around base GPUs. |
| Migration & Onboarding Getting started | Guided migration and DR planning. | Mostly self-serve or paid consulting. |
See how much you could save with AceCloud.
Why Businesses Trust AceCloud
Enterprise-Grade Security and Compliance
Trusted by Industry Leaders
See how businesses across industries use AceCloud to scale their infrastructure and accelerate growth.
Tagbin
“We moved a big chunk of our ML training to AceCloud’s A30 GPUs and immediately saw the difference. Training cycles dropped dramatically, and our team stopped dealing with unpredictable slowdowns. The support experience has been just as impressive.”
60% faster training speeds
“We have thousands of students using our platform every day, so we need everything to run smoothly. After moving to AceCloud’s L40S machines, our system has stayed stable even during our busiest hours. Their support team checks in early and fixes things before they turn into real problems.”
99.99*% uptime during peak hours
“We work on tight client deadlines, so slow environment setup used to hold us back. After switching to AceCloud’s H200 GPUs, we went from waiting hours to getting new environments ready in minutes. It’s made our project delivery much smoother.”
Provisioning time reduced 8×
You’ve seen their stories now start yours. Let AceCloud handle the infrastructure while you build what matters.
Strategic Technology Partners
Frequently Asked Questions
A GPU cloud server is a virtual machine with attached GPUs that you access over the internet. It lets you run parallel workloads like AI training, inference, rendering and simulations without buying or managing physical hardware.
You pick a GPU type, vCPU, RAM and storage, then launch a virtual machine from the AceCloud console or API. The GPU attaches to your VM, you connect over SSH or RDP, and you pay only for the resources you use on an hourly or monthly basis.
AceCloud offers multiple NVIDIA GPU families for different workloads, such as data center GPUs for large AI training jobs and workstation-class GPUs for rendering, visualization and smaller models. You can choose single-GPU or multi-GPU instances and tune vCPU, RAM and NVMe storage for each project.
Each GPU is attached directly to your virtual machine using passthrough. It is not shared with other customers, which keeps performance predictable and avoids noisy neighbours for training runs, inference services or rendering jobs.
Cloud GPUs work best for highly parallel tasks. Common use cases include LLM training and fine-tuning, model inference and RAG, computer vision, 3D rendering and VFX, video processing, simulations and data analytics at scale.
Yes. You can run popular frameworks such as PyTorch and TensorFlow, load open source or proprietary LLMs, and scale from a single GPU to multi-GPU setups for training and high-throughput inference. Many users also deploy Triton or similar inference servers on top of our GPU nodes.
Pricing depends on the GPU model, vCPU, RAM and storage you select, along with any additional services such as backups or extra bandwidth. You pay in a pay-as-you-go model, usually by the hour or month, which helps you align GPU spend with actual usage.
You can scale vertically by choosing instances with 2, 4 or more GPUs, and horizontally by adding more GPU nodes into a cluster. Many teams run Kubernetes or similar orchestrators to autoscale GPU workers and distribute training or inference across several machines.
You can deploy Linux or Windows GPU servers and install your preferred AI frameworks and libraries. Most users run containerized workloads with Docker or Kubernetes, Jupyter notebooks for experiments and standard DevOps tools for CI/CD pipelines.
GPU servers attach to high-performance block storage for datasets, checkpoints and logs. You can add or resize volumes and use object or shared storage for long-term data. Network bandwidth supports moving training data in and pushing predictions or rendered output back to your apps.
Workloads run in isolated virtual networks with firewalls and access controls. Data at rest can be stored on encrypted volumes and you can secure data in transit with TLS. You should also follow best practices such as key management, role-based access and regular backups for sensitive AI workloads.
New customers typically start with a free-credit trial so they can test GPU performance, workflows and costs before committing. Our team can advise on choosing the right GPU types, and many providers offer hands-on migration support for moving from on-prem or another cloud.
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