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Artificial intelligence, machine learning, generative AI, and high-performance computing are changing the way businesses use computing infrastructure. Training large language models, running AI inference, developing computer vision applications, and processing complex datasets require substantial GPU computing power.
However, purchasing and maintaining high-performance GPU servers can involve significant upfront investment, infrastructure management, cooling, networking, and hardware maintenance. This is where GPU as a Service (GPUaaS) provides an alternative.
GPU as a Service allows businesses, startups, researchers, and developers to access GPU-powered computing through cloud infrastructure. Instead of purchasing an entire GPU server, users can rent computing resources based on their workload and usage requirements.
In India, GPU cloud pricing depends on the GPU model, memory capacity, billing duration, dedicated or shared infrastructure, storage, networking, and provider. Popular enterprise GPU options include NVIDIA H100, B300, GB300, GB200, and RTX PRO 6000.
This guide explains GPU as a Service pricing in India, hourly and monthly GPU rental costs, dedicated GPU infrastructure, and how to select the right GPU for your AI project.
GPU as a Service is a cloud computing model that provides on-demand access to Graphics Processing Units (GPUs) through a service provider.
A GPUaaS provider manages the underlying infrastructure, including servers, GPU hardware, networking, virtualization, and infrastructure operations. Customers can access GPU resources through cloud instances, virtual machines, containers, or dedicated GPU servers.
GPUaaS is commonly used for:
The primary benefit is flexibility. Organizations can access GPU computing without necessarily purchasing and managing their own physical GPU infrastructure.
There is no single fixed GPUaaS price in India. The cost varies according to GPU architecture, available VRAM, number of GPUs, server configuration, billing model, and workload requirements.
For example, Cyfuture publishes INR pricing for several cloud GPU instances. Its pricing page lists NVIDIA H100, NVIDIA RTX PRO 6000, NVIDIA B300, and other GPU configurations with different hourly and reserved billing rates.
The following factors influence the total cost of GPU as a Service:
Hourly GPU pricing is suitable for customers who need flexible computing access. Users can start GPU instances for training, testing, inference, or development and stop them when the work is completed.
This model is useful for:
Cyfuture AI’s official pricing page provides the following published examples for GPU instances. These are listed in INR and should be checked on the provider’s pricing page before making a purchase.
| GPU model | Published on-demand rate | GPU memory |
|---|---|---|
| NVIDIA H100 SXM | ₹329/hour | 80 GB |
| NVIDIA RTX PRO 6000 | ₹256.50/hour | 96 GB |
| NVIDIA B300 | Pricing not published as on-demand in the displayed listing | 288 GB |
| NVIDIA L40S | ₹274/hour | 48 GB |
| NVIDIA MI300X | ₹542/hour for 2-GPU configuration | 384 GB total |
| NVIDIA V100 | Not listed as an active on-demand price in the displayed pricing table | 32 GB |
Important: The rates above are examples from the Cyfuture pricing page and are subject to change. The B300 and other models may have reserved or custom availability instead of an active on-demand rate.
Monthly GPU rental is a practical option for organizations running AI workloads continuously. Instead of paying for short sessions, customers can select a monthly billing arrangement or a reserved commitment.
Monthly pricing is influenced by:
A simple monthly cost estimate is:
Monthly GPU Cost = Hourly GPU Rate × Running Hours per Month
For example, using an illustrative 720-hour month:
| GPU | Example hourly rate | Estimated monthly cost at 720 hours |
|---|---|---|
| NVIDIA H100 SXM | ₹329 | ₹2,36,880 |
| NVIDIA RTX PRO 6000 | ₹256.50 | ₹1,84,680 |
| NVIDIA L40S | ₹274 | ₹1,97,280 |
These are arithmetic estimates using the published hourly rates above. They do not include any additional storage, networking, taxes, or other charges.
Actual monthly billing can differ if the provider offers reserved pricing, discounts, minimum commitments, or a different number of billable hours.
Reserved GPU plans are designed for workloads that require computing resources for a longer period.
Cyfuture’s pricing page shows lower hourly rates for certain reserved durations. For example, the listed H100 SXM rate is ₹329/hour on demand, ₹296/hour for the displayed one-month reserved option, ₹263/hour for the six-month reserved option, and ₹219/hour for the twelve-month reserved option.
Longer commitments can be useful when:
Before selecting a reserved plan, compare the effective monthly cost with your expected GPU utilization.
A dedicated GPU server provides exclusive access to a physical server or dedicated GPU resources, depending on the provider’s deployment model.
Dedicated infrastructure can be suitable for organizations that need:
Dedicated GPU pricing is generally quoted based on the complete configuration rather than GPU hardware alone.
| Configuration | Impact on pricing |
|---|---|
| GPU quantity | More GPUs increase infrastructure cost. |
| GPU memory | Higher VRAM supports larger workloads and can increase cost. |
| CPU and RAM | Higher system resources increase server pricing. |
| Storage | NVMe SSD and additional storage affect monthly cost. |
| Network | High-bandwidth networking and GPU interconnects can add cost. |
| Dedicated support | Enterprise support and managed operations may affect the quote. |
| Contract length | Long-term commitments may offer different pricing. |
For a dedicated GPU server, customers should request a complete quote that includes the GPU model, memory, CPU, RAM, storage, network, location, support, and billing terms.
Different GPU architectures are designed for different AI and computing workloads. The right selection depends on model size, memory requirements, throughput, latency, and budget.
The NVIDIA H100 is a high-performance data center GPU designed for AI training, inference, and high-performance computing.
It is commonly associated with demanding workloads such as:
Cyfuture’s official pricing page lists an H100 SXM instance with 80 GB of GPU memory at ₹329/hour on demand, with lower listed rates for longer reserved durations.
H100 is a strong option for organizations that need enterprise AI compute and high-performance GPU infrastructure. However, the total cost of a multi-GPU H100 cluster depends on the number of GPUs and the complete server configuration.
The NVIDIA B300 is part of NVIDIA’s Blackwell Ultra GPU platform and is designed for demanding AI and accelerated computing workloads.
The B300 is relevant for organizations working on:
Cyfuture AI’s pricing page includes NVIDIA B300 configurations with 288 GB of AI compute memory for a single GPU configuration. The displayed table shows reserved pricing for the listed B300 instances, while the on-demand field is not populated in the displayed listing.
For B300 GPUaaS, customers should confirm:
Because B300 is an advanced enterprise GPU, pricing and availability may vary by provider and deployment model.
NVIDIA GB300 refers to a Grace Blackwell Ultra superchip platform that combines NVIDIA Grace CPU technology with Blackwell Ultra GPU technology.
GB300 is designed for advanced AI infrastructure and high-performance computing environments. It is relevant to organizations developing large-scale AI systems that require substantial GPU memory, compute performance, and high-speed communication between components.
GB300 pricing should be treated separately from the price of an individual B300 GPU. A GB300 system can involve a more complex integrated platform, including CPU, GPU, memory, networking, and system-level infrastructure.
Before purchasing GB300 GPUaaS, request a provider quote covering:
GB300 should be selected for workloads that require advanced AI infrastructure rather than simply choosing a GPU based on its name.
NVIDIA GB200 is an integrated Grace Blackwell platform designed for large-scale AI and accelerated computing.
GB200 systems are relevant for workloads such as:
GB200 pricing is generally dependent on the complete system configuration and deployment requirements. It should not be confused with the hourly cost of a single H100 or RTX PRO 6000 GPU.
For GB200 GPUaaS, organizations should evaluate the complete platform, including system architecture, memory, networking, capacity, availability, and support.
Cyfuture customers interested in GB200 or GB300 should request current availability and a customized quotation rather than relying on a generic GPU hourly estimate.
The NVIDIA RTX PRO 6000 is a professional GPU designed for demanding AI, graphics, rendering, and workstation-class computing workloads.
It can be suitable for:
Cyfuture’s official pricing page lists a single NVIDIA RTX PRO 6000 instance with 96 GB of AI compute memory at ₹256.50/hour on demand. The page also lists lower reserved hourly rates for longer commitments.
The RTX PRO 6000 can be a useful option for users who need substantial GPU memory and professional GPU performance without necessarily requiring the full infrastructure of a large multi-GPU AI cluster.
The following table summarizes the GPU options discussed in this article.
| GPU model | Best suited for | Pricing approach |
|---|---|---|
| NVIDIA H100 | AI training, inference, fine-tuning, HPC | Published hourly and reserved rates |
| NVIDIA B300 | Advanced AI and accelerated computing | Confirm current provider availability and quote |
| NVIDIA GB300 | Large-scale AI infrastructure | System-level pricing |
| NVIDIA GB200 | Large-scale AI and distributed computing | System-level pricing |
| NVIDIA RTX PRO 6000 | AI inference, graphics, rendering, professional workloads | Published hourly and reserved rates |
GPU specifications, availability, and prices should be verified with the provider before making a purchasing decision.
Businesses typically compare GPUaaS with buying and operating physical GPU servers.
| Factor | GPU as a Service | On-premises GPU server |
|---|---|---|
| Upfront investment | Lower initial infrastructure investment | Higher hardware purchase cost |
| Deployment | Cloud provisioning | Procurement and installation |
| Scalability | Add or reduce GPU resources based on provider capacity | Requires additional hardware |
| Maintenance | Provider manages underlying infrastructure | Customer manages hardware and operations |
| Billing | Hourly, monthly, or reserved | Hardware ownership and operating costs |
| Flexibility | Useful for variable workloads | Useful for predictable, dedicated workloads |
GPUaaS is often attractive for organizations that want flexible access to advanced GPUs. On-premises infrastructure may be appropriate when a business has predictable long-term utilization, specific hardware requirements, or existing data center capabilities.
Choosing a GPU provider involves more than comparing the hourly GPU rate.
Check whether the provider offers the specific GPU model required for your workload. H100, B300, GB300, GB200, and RTX PRO 6000 may have different availability and deployment options.
GPU memory is important for large language models, deep learning, and high-resolution workloads. A GPU with insufficient VRAM may not support the model or batch size you need.
Compare on-demand, monthly reserved, and long-term pricing. Make sure the quote explains how billing works when the GPU is stopped or terminated.
Ask about CPU, RAM, storage, networking, GPU interconnects, and whether the GPU is dedicated or shared.
For businesses operating in India, data residency and compliance requirements may influence provider selection. Ask where workloads are hosted and how data is handled.
The final cost should include all required infrastructure components and any additional fees.
Cyfuture provides GPU cloud infrastructure for AI, machine learning, and accelerated computing workloads.
Its official GPU pricing page lists GPU instances across NVIDIA and other accelerated computing platforms, including H100, RTX PRO 6000, B300, and additional GPU configurations.
Flexible GPU access: Select GPU resources based on workload requirements.
Hourly and reserved pricing: Choose a billing model that fits short-term experiments or longer AI projects.
Enterprise GPU options: Access GPU configurations for AI training, inference, and high-performance computing.
India-focused cloud infrastructure: Evaluate available GPU infrastructure and deployment options based on business requirements.
Scalable workloads: Request configurations ranging from individual GPUs to larger GPU deployments, subject to availability.
| GPU configuration | Published hourly price |
|---|---|
| NVIDIA H100 SXM, 80 GB | ₹329/hour |
| NVIDIA RTX PRO 6000, 96 GB | ₹256.50/hour |
| NVIDIA B300, 288 GB | Reserved pricing displayed; confirm on-demand availability |
| NVIDIA L40S, 48 GB | ₹274/hour |
These rates are examples from Cyfuture AI’s pricing page and may change.
Explore GPU pricing: https://cyfuture.ai/pricing
Before renting a GPU, estimate your expected usage.
Suppose a business uses one H100 GPU for 100 hours.
Using an illustrative rate of ₹329/hour:
Estimated GPU cost = 100 × ₹329 = ₹32,900
Suppose a business uses one RTX PRO 6000 GPU for 720 hours in a month.
Using the published example rate of ₹256.50/hour:
Estimated GPU cost = 720 × ₹256.50 = ₹1,84,680
These examples do not include additional charges such as storage, networking, applicable taxes, or other infrastructure services.
GPUaaS pricing varies by GPU model and provider. Published Cyfuture examples include H100 at ₹329/hour and RTX PRO 6000 at ₹256.50/hour. Lower-cost GPUs and other configurations may have different rates.
GPUaaS can reduce upfront investment and maintenance responsibilities. Whether it is cheaper overall depends on GPU utilization, rental duration, hardware requirements, and infrastructure costs.
Cyfuture’s pricing page lists an NVIDIA H100 SXM instance at ₹329/hour on demand. The displayed reserved rates are lower for longer commitments.
Cyfuture lists a single RTX PRO 6000 instance at ₹256.50/hour on demand. Monthly costs depend on actual running hours and the selected billing plan.
NVIDIA B300 appears in Cyfuture’s GPU pricing listing. The displayed page includes B300 configurations and reserved rates, but the on-demand price is not populated in the displayed listing. Confirm current availability and pricing with the provider.
GB200 and GB300 are integrated NVIDIA Grace Blackwell platform options designed for advanced AI and accelerated computing. Their complete system configurations and deployment requirements differ, so pricing should be obtained based on the exact platform.
The best GPU depends on model size, training duration, VRAM, networking, and budget. H100 is a strong option for demanding AI training, while B300, GB200, and GB300 may be considered for advanced large-scale AI infrastructure.
Yes, dedicated GPU infrastructure can be requested from GPU cloud providers. The exact configuration, pricing, availability, and deployment model should be confirmed with the provider.
Monthly GPU rental is generally estimated by multiplying the hourly rate by the number of billable hours, then adding any applicable infrastructure charges.
Choose a provider based on GPU availability, pricing, VRAM, infrastructure, networking, support, data location, and the requirements of your workload. Cyfuture is one option to evaluate for India-focused GPU cloud infrastructure.
GPU as a Service makes advanced computing resources accessible without requiring every organization to purchase and maintain its own GPU server. From short-term AI experiments to enterprise-scale model training, GPUaaS offers flexible access to GPU infrastructure.
In India, the cost of GPUaaS depends on the selected GPU, memory, billing model, and complete infrastructure configuration. NVIDIA H100 and RTX PRO 6000 are useful options to evaluate for AI and accelerated workloads, while B300, GB300, and GB200 are relevant to advanced AI infrastructure requirements.
Cyfuture provides published GPU pricing examples and GPU configurations that businesses can evaluate for their AI workloads. To select the right GPU, compare performance, VRAM, pricing, availability, and total infrastructure cost.
