How do I deploy GPU as a Service on Cyfuture Cloud?

Cyfuture Cloud offers GPU as a Service (GPUaaS) for on-demand access to high-performance NVIDIA GPUs like A100 and H100, ideal for AI, ML, and HPC workloads. Deployment is streamlined through their dashboard with one-click provisioning, eliminating hardware management.​

Overview of GPUaaS on Cyfuture Cloud

Cyfuture Cloud's GPUaaS provides virtualized or dedicated NVIDIA GPU instances hosted in secure Indian data centers, optimized for APAC low-latency workloads. Users access resources via intuitive portals, SSH, or Kubernetes orchestration without upfront CapEx, paying only for usage—saving up to 70% versus on-premises setups. This service supports AI training, inference, rendering, and HPC, with pre-configured environments for TensorFlow, PyTorch, and Jupyter Notebooks.​

Key features include RDMA interconnects for multi-node scaling, NVMe storage, and integrations with cloud storage for hybrid workflows. Security complies with GDPR, ISO 27001, and SOC 2, featuring encrypted transfers and tenant isolation. Compared to traditional clouds, Cyfuture emphasizes India-based regions for faster APAC performance.​

Step-by-Step Deployment Guide

Deploying GPUaaS follows a simple, dashboard-driven process:

Account Setup: Register at cyfuture.cloud, verify via email, and add payment for pay-as-you-go or reserved instances.​

Instance Selection: Navigate to GPU section; filter by model (A100/H100), cores (e.g., 8x GPUs), RAM (up to 2TB), and OS (Ubuntu/CentOS).​

Configuration: Attach storage, set network (public/private IP), and upload workloads (e.g., Docker images with CUDA libraries).​

Launch & Connect: One-click deploy; access via web console, SSH (with key pairs), or API for automation.​

Management: Monitor GPU utilization, temperature, and throughput in real-time; enable auto-scaling or Slurm for clusters.​

For advanced users, integrate via APIs/SDKs for CI/CD pipelines or Kubernetes for orchestration. Deployment completes in minutes, with 24/7 support for migrations.​

Benefits and Best Practices

Cyfuture's GPUaaS reduces TCO by 60% with flexible billing (hourly to yearly) and no maintenance overhead. Best practices include starting with single-GPU dev instances, using spot pricing for non-critical jobs, and leveraging managed services for optimization. For large-scale AI, opt for multi-GPU clusters with high-speed networking.​

Common pitfalls to avoid: Oversizing instances (use rightsizing tools) or ignoring data persistence (enable snapshots). Test workloads in staging before production scaling.​

Conclusion

Deploying GPU as a Service on Cyfuture Cloud is efficient, cost-effective, and tailored for AI/HPC innovation, transforming complex hardware needs into accessible cloud resources. With rapid provisioning and robust support, businesses achieve scalable performance without infrastructure hassles, driving faster time-to-insights.​

Follow-Up Questions

Q1: What GPU models are available?
A: NVIDIA A100, H100, and others like Hopper architecture for AI/HPC; select via dashboard for workload-specific optimization.​

Q2: How much does it cost?
A: Pay-as-you-go starts hourly (e.g., ~₹X/hour for A100); reserved plans offer discounts up to 60% off on-premises equivalents.​

Q3: Can I use custom containers?
A: Yes, upload Docker images with CUDA/TensorFlow; platform supports one-click deployment and Jupyter integration.​

Q4: Is it suitable for production AI training?
A: Absolutely; scales to multi-GPU clusters with RDMA, auto-scaling, and 99.99% SLA for enterprise reliability.​

Q5: How to migrate from another cloud?
A: Use managed migration services; APIs facilitate seamless data/workload transfers with minimal downtime.​