Liquid-Cooled GPU as a Service for Dense AI and HPC Workloads

GPU as a Service is a cloud model that provides on-demand access to GPU computing for AI, machine learning, HPC and rendering. You use and pay for GPU capacity without buying or maintaining the hardware.

You select a GPU type and deployment model, Cyfuture Cloud provisions the environment, and you run your workloads over the network. Usage is metered, and capacity can scale up or down as your needs change.

Cyfuture Cloud offers NVIDIA GPUs including the H100, A100, L40S, V100 and RTX PRO 6000, with newer options such as the B300 listed on dedicated pages. Availability changes, so confirm current inventory with the team.

Cost depends on the GPU model, hours used, cluster size, storage and support. As a reference, on-demand rates start at $0.60/hr for a V100 and $3.66/hr for an H100. Instances are billed hourly with a 1-hour minimum, storage is $0.05/GB/month, and there are no platform fees or egress charges within a region. Use the pricing calculator or request a custom quote for your workload.

Match the GPU to the job. The V100 suits development and prototyping, the L40S suits FP8 inference, and the A100 suits fine-tuning. The H100 is built for LLM pre-training, and the B300 targets frontier-scale training. If you are unsure, share your model size and batch requirements with the team and they will recommend a configuration.

Yes. Multi-GPU configurations include 2× A100 NVLink (160 GB) for 70B-parameter training, 4× H100 NVLink (320 GB) for multi-GPU scale, and 8× H100 NVLink (640 GB) for frontier models. For inference at volume, a 4× L40S (192 GB) fleet is available. For larger multi-node clusters, contact the team to scope your requirements.

Yes. For steady production inference, reserved capacity gives you lower rates and guaranteed availability. For bursty or experimental inference, on-demand lets you pay only for the hours you use.

On-demand is pay-per-hour and suits experiments and bursty jobs. Reserved means committing for 6 or 12 months to save up to 35% and lock in capacity priority. Flex or spot capacity is discounted but can be interrupted, so it fits fault-tolerant batch jobs. Enterprise covers dedicated clusters and custom terms for large or regulated deployments.

Start with on-demand while you size your workload, then move steady training or inference to a 6-month or 12-month reservation. Use flex or spot capacity for batch jobs that can resume from checkpoints, and pick the smallest GPU that fits your model in memory.

GPUaaS offers flexible, usage-based capacity. A dedicated GPU server reserves an entire physical machine for you, which suits steady, high-utilisation workloads. Cyfuture Cloud offers both approaches.

Rent when workloads vary, when you want to start quickly or when you want to avoid large upfront spend. Buying makes more sense when utilisation is consistently high and you have the team to run the hardware.

It can be, when the provider offers isolation, access controls and compliant facilities. Cyfuture Cloud hosts GPU workloads in MeitY-empanelled Tier III data centers in India. Your own data handling policies still apply.