What is the Difference Between Shared and Dedicated GPU Cloud Servers?

Shared GPU cloud servers divide resources among multiple users for cost savings, while dedicated GPU cloud servers provide exclusive access to the entire GPU for maximum performance and consistency.​

Shared GPU servers allow multiple tenants to use fractions of a physical GPU via virtualization, offering affordability but variable performance due to contention. Dedicated GPU servers reserve the full GPU for one user, delivering predictable high throughput ideal for intensive workloads like AI training, at a higher cost.​

Shared GPU Cloud Servers

Shared GPU cloud servers partition a single physical GPU into virtual instances using technologies like NVIDIA MIG, enabling multiple users to access portions simultaneously. This setup suits low-to-moderate workloads such as development testing or small-scale inference, where cost trumps peak performance. Performance fluctuates based on demand from other users, potentially leading to higher latency or reduced memory access speed.​

Dedicated GPU Cloud Servers

Dedicated GPU cloud servers allocate an entire physical GPU exclusively to one tenant, eliminating resource sharing and ensuring full compute power, VRAM, and bandwidth. They excel in demanding tasks like large-scale deep learning training, 3D rendering, or real-time simulations requiring stable low latency. Users gain complete control over configuration, making them reliable for production environments.​

Key Differences

The table below compares core aspects:

Cyfuture Cloud offers both options through its GPU infrastructure, allowing scalable choices based on needs.​

Use Cases

Shared servers fit cost-sensitive projects like prototyping AI models or lightweight graphics processing. Dedicated servers power high-stakes applications, such as training complex neural networks or scientific simulations, where downtime costs exceed savings. Cyfuture Cloud's virtualization expertise optimizes these for Indian data centers, reducing latency for regional users.​

Cost and Scalability

Shared options minimize expenses for sporadic use, often 50-70% cheaper than dedicated equivalents. Dedicated plans provide better long-term value for continuous workloads via fixed pricing and no performance throttling. Cyfuture Cloud supports seamless scaling from shared to dedicated as projects grow.​

Conclusion

Choose shared GPU cloud servers for budget-friendly entry into GPU computing with tolerable variability, or dedicated servers for mission-critical reliability. Cyfuture Cloud tailors both to diverse workloads, balancing cost and power effectively. Evaluate your performance needs and budget to select optimally.​

Follow-Up Questions

Q: When should I upgrade from shared to dedicated GPU servers?
A: Upgrade when workloads demand consistent performance, such as production AI training or real-time apps, as shared resources cause bottlenecks under high demand.​

Q: Does Cyfuture Cloud support NVIDIA MIG for shared GPUs?
A: Yes, Cyfuture Cloud leverages NVIDIA MIG and similar tech for efficient shared GPU partitioning, maximizing resource utilization.​

Q: How do GPU cloud servers compare to on-premises hardware?
A: Cloud options like Cyfuture's eliminate upfront costs and maintenance, offering 70% savings and instant scalability over local setups.​

Q: What GPUs does Cyfuture Cloud offer in dedicated plans?
A: Cyfuture provides enterprise-grade NVIDIA GPUs suited for AI/HPC, with configurations matching workload intensity.​