What Are the Deployment Models for GPU as a Service?

GPU as a Service (GPUaaS) deployment models offered by Cyfuture Cloud include public cloud, private cloud, hybrid cloud, multi-cloud, on-demand instances, reserved instances, spot instances, dedicated instances, and serverless GPU options.​

GPUaaS Deployment Models Explained

Cyfuture Cloud provides flexible GPUaaS deployment models tailored for AI, ML, and HPC workloads, enabling users to select based on security, scalability, cost, and compliance needs.​

Public Cloud Deployment

In public cloud models, users access shared GPU resources over the internet from Cyfuture Cloud's data centers, ideal for cost efficiency and rapid scaling in non-sensitive workloads like AI prototyping. This model offers pay-as-you-go pricing starting at $0.57/hour for NVIDIA L40s GPUs, with automatic provisioning for tasks such as model training.​

Private Cloud Deployment

Private deployments dedicate isolated GPU environments, either on-premises or hosted by Cyfuture Cloud, prioritizing security for regulated industries like finance or healthcare. Cyfuture Cloud supports this through Multi-Instance GPU (MIG) technology for secure partitioning and Kubernetes orchestration, ensuring compliance with SOC 2 and GDPR standards.​

Hybrid and Multi-Cloud Strategies

Hybrid models blend on-premises data with Cyfuture Cloud's public GPUs for sensitive data residency while offloading compute-intensive tasks. Multi-cloud approaches integrate Cyfuture Cloud with providers like AWS or Azure via APIs, avoiding vendor lock-in and optimizing costs through spot instances across platforms. Cyfuture Cloud enables unified management with container tools for seamless workload portability.​

Instance-Based Models

On-Demand Instances: Flexible for short-term use, no commitments, perfect for experimentation.​

Reserved Instances: Discounted for predictable long-term workloads like ongoing inference.​

Spot/Preemptible Instances: Cheapest for fault-tolerant batch jobs, though interruptible.​

Dedicated Instances: Exclusive physical GPU access for production reliability.​

Serverless GPU: Auto-scales without infrastructure management, suited for variable inference demands.​

Cyfuture Cloud's NVIDIA H100, A100, and L40s GPUs power these models, with one-click deployment reducing setup time to minutes. Features like high-speed interconnects and pre-configured frameworks (TensorFlow, PyTorch) enhance performance across all options.​

Conclusion

Cyfuture Cloud's diverse GPUaaS deployment models empower businesses to balance performance, security, and costs effectively, supporting hybrid strategies and eliminating hardware ownership barriers. Enterprises benefit from up to 60-70% cost savings, instant scalability, and global data center access for mission-critical AI initiatives.​

Follow-up Questions & Answers

Q: How does Cyfuture Cloud ensure security in hybrid GPUaaS deployments?
A: Through end-to-end encryption, isolated environments, Kubernetes multitenancy, and MIG for resource partitioning, compliant with SOC 2, GDPR, and HIPAA.​

Q: What GPUs are available in Cyfuture Cloud's GPUaaS?
A: NVIDIA H100, A100 (40/80GB), V100, T4, L40s; AMD MI300X; Intel GAUDI 2, optimized for training and inference.​

Q: Can Cyfuture Cloud GPUaaS integrate with existing infrastructure?
A: Yes, via APIs, SDKs, and Kubernetes for hybrid/multi-cloud setups, enabling workload portability without lock-in.​

Q: What pricing models does Cyfuture Cloud offer for GPUaaS?
A: On-demand (e.g., H100 at $2.34/hr), reserved for discounts, spot for savings, and serverless pay-per-use.​