Rent GPU Cloud Servers in India

On-demand GPUs let you pay by the hour without a long-term commitment. Spot GPUs cost less but can be interrupted when spare capacity is reclaimed, making them better suited to jobs you can restart. Committed plans suit regular usage, with fixed pricing and listed savings of approximately 5% for six months or 10% for twelve months.

The price includes your selected GPU, vCPUs, RAM and one public IP address. Bandwidth is unmetered, with no charges for incoming or outgoing data transfers. Applicable GST is added to the listed price. Check your plan or quotation for boot disk capacity and any extra storage charges.

After account verification, an available GPU instance can be ready for SSH access in under five minutes from launch. You’ll first need to complete the sign-up and KYC steps. The total setup time depends on verification, your chosen configuration and GPU availability.

The minimum rental period depends on your billing plan. Hourly instances offer flexibility for short jobs, while monthly and committed plans carry fixed charges. Shutting down a monthly instance does not cancel its subscription; you need to deprovision the service. Upgrades depend on configuration availability and plan rules. Reserved commitments and minimum billing amounts are non-refundable.

Your workload can be interrupted, so save regular checkpoints to persistent storage rather than relying only on the instance’s local disk. You can then restart from the latest saved checkpoint when capacity becomes available. Before running a job, check the interruption notice, disk-retention policy and any continuing storage charges with our support team.

Indian customers can pay through UPI, credit cards or debit cards. Prepaid wallet and postpaid billing options are available, depending on your account arrangement. You can view and download invoices from the console. For bank transfers or enterprise billing terms, speak with our team.

The GPUs in our passthrough instances are dedicated to your VM and are not shared with other customers. Your workloads have direct access to the allocated GPU, whether you’re training models, serving inference requests or rendering.

Yes. Supported configurations offer 2, 4 or 8 GPUs in one VM. You can also run workloads across multiple GPU nodes using Kubernetes or another orchestration tool. For larger models, your framework must support distributing the workload across GPUs; their VRAM does not automatically become one shared memory pool.

Yes. Eligible new customers can get ₹20,000 in credits to test workloads before choosing a paid plan. Confirm the eligible GPU models, available capacity and credit expiry during onboarding so you have enough time to benchmark your setup.

Yes. You can rent NVIDIA GPU instances from AceCloud’s Indian data centres for AI training, inference, rendering and other compute-intensive tasks. Choose an available configuration and the billing option offered for it. Start with one GPU or select a supported multi-GPU instance for larger workloads.