Whenever it comes to selecting a GPU Dedicated server providers, we think about which one is the best dedicated GPU server. GPU Dedicated servers are the best choice for you because they offer you the ability to increase power and maintain speed on computing devices such as video rendering, data analytics, and machine learning. GPU Dedicated servers work for AI and machine learning, which will be very useful tools in the future. GPU Dedicated servers are based on various key factors such as performance, pricing, availability, support, and specific use cases. GPU Dedicated Server is open-source and user-friendly. GPU Dedicated servers offer higher computational permanence in comparison to CPUs. In terms of the CPU, GPU servers are cheaper and offer higher performance. In addition, multiple GPU servers run on a single server. You can choose the GPU Dedicated server that understands your AI application performance requirements. NVidia is one of the best GPU dedicated servers and fastest GPU Server, which is a game changer in the field of performance and acceleration. 

A detailed comparison of GPU server providers like AWS, Google Cloud, and Azure

GPU Dedicated Server are the various numbers of GPU server providers that provide the best cloud services to their users. There are the best examples of GPU server providers like Amazon Web Services, Google Cloud, Azure, and many more. Here, we compared all these GPU server providers and found which service was the best.

Amazon Web Services (AWS): 

• Performance: AWS offers a comprehensive range of GPU instances, including the NVIDIA A100, V100, T4, and older models like the K80 and M60. It offers high-performance network data services.
• Pricing: AWS’s pricing varies based on standard on-demand pricing for flexibility. AWS offers Reserved Instances, which can give up to a 75% discount for long-term commitments.
• GPU Options: T4 and G4ad (Radeon Pro V520) offer a range of performance options.
• Best for: AWS Cloud Service is best for large enterprises with demanding workloads. 
• Features: AWS includes a comprehensive range of services such as advanced analytics, machine learning services, extensive storage options, and robust security measures.

Google Cloud Platform (GCP): 

• Performance: GCP offers services such as NVIDIA A100, V100, T4, P100, and K80 GPUs. Enables them to work on resource allocation to optimise cost and performance. 
• Pricing: GCP offers an on-demand price per second. It offers up to a 57% discount for committing to 1-3 years of usage. Additional charges for the storage and network may be applied.
• GPU Options: K80, P4, T4, P100, and V100 offer a range of performance options.
• Best for: GCP Service is best for specific AI and machine learning tasks.
• Features: GCP is best for features such as AI and ML training, data analytics, big data processing, rendering, and graphics-intensive applications.

Microsoft Azure:

• Performance: Microsoft Azure offers services such as NVIDIA A100, V100, T4, M60, and older GPU models. They also offer various networking options for high-priced computing and distributed ML workloads.
• Pricing: Accept the on-demand price based on the specific needs and requirements. It offers up to a 90% discount for interruptible workloads.
• GPU Options: There are various GPU options, including T4, A100, V620, M60, and MI25.
• Best for: Microsoft Azure is best for Azure machine learning and scientific computing that handles complex tasks
• Features: Microsoft Azure has various features such as data analytics and visualisation, HPC and large-scale simulations, enterprise applications, and workloads.

Pros and cons of each platform for different use cases

GPU Dedicated Server is the best platform to increase power and maintain speed on computing devices such as video rendering, data analytics, and machine learning. The GPU Dedicated Server is three cloud based providers named as Amazon Web Services, Google Cloud, Azure. In the above article, you learn about the difference between all these three platforms. Now we explain about the pros and cons of each GPU Dedicated Server platform.

Amazon Web Services (AWS): 

AWS Pros: 

• Most extensive cloud services: It provides on-premises public, private, hybrid, and multi-cloud cloud services.
• High Networking in Data Centers: Amazon’s cloud infrastructure provides the largest networking in the various data centres.
• Best cloud service experience: AWS provides public cloud services and continues to innovate and provide reliable services.
• User-friendly: AWS is an easier service than others and is also more suitable for small and medium businesses. AWS is a low-cost, free, and open-source solution.
• Ecosystem Integration: Deep integration with a vast array of AWS services such as SageMaker for ML and ECS/EKS for containers
• Flexible Pricing: Provide on-demand, reserved, spot instances, and savings plans.
• Third Party Software Solutions: AWS Marketplace provides a wide variety of third-party software that enhances your cloud services.
• Data Backup Facility: The vendor provides high-backup data services across multiple locations.

AWS Cons:

• Multiple Services options: There are multiple options to select from among the many cloud products, services, and features available that may create confusion.
• Expensive support: Technical support can be very costly and expensive.
• Cost: On-demand pricing is very expensive as compared to competitors for certain workloads.

Google Cloud Platform (GCP): 

GCP Pros:

• Affordable Price: GCP offers an affordable price along with the best service.
• Available open-source support: GCP has tied up with the open-source community, which is involved in numerous projects with partners such as RedHat.
• Advanced machine learning and blockchain capabilities:  The GCP platform has a huge database that supports advanced machine learning and blockchain capabilities.
• Integrated with the Tools: GCP is using tools like Gmail for Business, Spreadsheets, Forms, and Google Docs to integrate their services with the companies.
• Support multi-cloud deployments: The Google Cloud Platform supports multi-cloud deployments.
• Pricing: more user-friendly pricing and more straightforward.
• Security and compliance: Provide a high level of security and compliance.

GCP Cons:

• Limited number of cloud servers and data centres 
• Limiting the usage of data in some regions
• It supports only limited enterprise solutions.

Microsoft Azure:

Microsoft Azure Pros: 

• Superior enterprise support: it is hybrid cloud and on-premises services.
• Extensive Network of global data centres: It is the second-largest network of cloud servers that are accessible anywhere and anytime
• Integrated with Microsoft business applications: Various Applications integrated in this service include Microsoft 365, Azure Virtual Desktop, Microsoft SQL Server, and Power BI.
• Existing Microsoft licences integrate: It is easy to integrate existing Microsoft licences to prevent extra costs.
• Affordable cost: It is lower cost than AWS but costly than SMBs
• Supportable: Support Linux OS on a large scale. 

Microsoft Azure Cons: 

• Complexity: Azure service offers the service that is ineviatable for new users.
• Pricing: Price is very high on demand.
• Services: Technical Support service is very poor and there are huge complaints from the customers’ side.

Conclusion

Infinitive Host is the best GPU Dedicated Server that works for AI and Machine learning tasks. Select the right GPU dedicated server, complete regular tasks on a regular basis, and give the best and most reliable results. Choosing the right GPU Dedicated server provider depends on their basic needs. 

AWS: machine learning, high-performance computing, and extensive integration with AWS services. 

GCP: flexible pricing, custom machine types, and ideal for AI and ML workloads with strong TensorFlow integration. 

Microsoft Azure: excellent hybrid cloud solutions, the best enterprise applications, and users heavily invested in the Microsoft ecosystem. Evaluate each cloud service provider based on factors related to your projects and make the decision based on performance needs, budget constraints, and integration requirements. Choose the best GPU Dedicated server platform that aligns best with and fulfils your business needs; check their previous performance, pricing, integration, and support factors that are relevant to your projects. Now it’s your choice which GPU Dedicated Server you want to choose to meet your specific business requirements. As per your requirements, Select the best GPU Dedicated server that fullfils your business needs.

Read More : Optimising GPU Server Performance for Deep Learning