5 Best GPU Servers for Deep Learning 2024

Here is the list of the best GPU Servers for deep learning. You can use this ready-built server for your deep-learning applications.

The Top 5 Best GPU Servers for Deep Learning

1. OVHCloud

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  • Cloud servers designed for massively parallel processing
  • GPU instances integrate NVIDIA graphic processors
  • ISO/IEC 27001, 27701 and health data hosting compliance
  • NVIDIA cards with up to four cards combined
  • Starting at $0.77/hour
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  • Highly optimized for deep learning and AI processes
  • Multiple configuration options suitable for varied workflows
  • User-friendly experience and intuitive setup process
  • Great scalability and customization options


  • Utilizes slightly outdated NVIDIA Tesla V100S GPUs
  • Inadequate CPU information
  • Somewhat mixed customer reviews
  • Possible learning curve for beginners

OVHCloud is making waves in the hosting industry with its special focus on servers for deep learning and artificial intelligence applications. Their cloud servers are specifically designed for massively parallel processing which is beneficial for tasks such as image recognition, situation analysis, and human interaction.

The servers incorporate GPU instances that incorporate NVIDIA graphic processors allowing you to experience the benefits of on-demand resources and hourly billing. As a result, the hardware delivers all of its computing power to your application, enabling fast and efficient data processing.

When it comes to pricing and plans, OVHCloud offers a variety of options to accommodate various workflow requirements. Their GPU instances start at just $0.77 per hour, providing an affordable solution for businesses and individuals who require extensive parallel processing facilities.

In terms of security, they conform to ISO/IEC 27001, 27017, 27018 and 27701 certification requirements. This ensures that they adopt a robust information security management system for handling risks and vulnerabilities and implementing business continuity. They also have compliance for securely hosting health data.

One of the key selling points of OVHCloud is its partnership with NVIDIA. Together, they deliver a best-in-class GPU-accelerated platform, which simplifies GPU-accelerated container deployment and maintenance. This extensive GPU capability makes their cloud servers particularly attractive for machine and deep learning applications.

While user experience can vary, there is no doubting the technical prowess of OVHCloud and its deep learning capabilities. Although they have received some negative reviews, their commitment to deliver a secure, scalable, and intuitive deep learning platform cannot be ignored.

So if you’re looking for a hosting provider that genuinely caters to the needs of deep learning and artificial intelligence, OVHCloud definitely deserves your attention.

Read more: OVHcloud GPU Server Hosting Review

2. Paperspace

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  • Globally accessible GPU and CPU servers
  • One-click setup with pre-configured templates
  • Infrastructure abstraction and auto-scaling for deep learning
  • Provides real-time analytics and insights
  • Starting at $0.0045/hour
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  • Simple management interface & API
  • Excellent for AI developers with a no-fuss environment
  • Predictable on-demand pricing
  • Provides excellent speed and performance


  • Complexities with Conda Environments
  • Auto-shutdown limitations
  • Occasional lag and performance issues on lower-tier machines
  • Some billing concerns reported

Paperspace is a global platform that provides on-demand computational resources, making it perfect for AI and deep learning developers. With its straightforward, no-fuss setup, you can go from signing up to training a model in a matter of seconds.

Aside from the easy setup, Paperspace also takes care of all the technicalities for you. With infrastructure abstraction, it automatically handles job scheduling, resource provisioning, and more. You won’t have to worry about managing servers, making it a stress-free, seamless experience.

One of Paperspace’s standout features is its ability to scale rapidly. It offers a comprehensive range of GPU options with no runtime limit, allowing developers to quickly ramp up training as needed. In addition, Paperspace’s versioning and lifecycle management features ensure full reproducibility, a crucial requirement in AI development.

Collaboration is also made easy with Paperspace. With visibility into team utilization, permissions, and more, entire teams can effortlessly collaborate and work on the platform.

For cost efficiency, Paperspace offers predictable on-demand pricing. That means you only pay for what you use, so you can easily scale your resources up or down as required. Plus, with no long-term commitment, you can easily change plans whenever necessary.

From loaded ML frameworks to lightning-fast networking speed and a wide variety of GPU options, Paperspace has positioned itself as a leading choice for high-performing GPU servers for deep learning. With its ease of use and flexible pricing schemes, it’s perfect for AI developers seeking a robust, scalable, and user-friendly cloud computing solution.

3. Cherry Servers

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  • Dedicated GPU servers for high-performance computations
  • Innovative in-memory computing up to 1536GB RAM
  • Secure data storage options: directly attached, block or object storage
  • Private networking via robust 10G virtual LAN
  • Free data flow up to 100TB of free monthly traffic
  • Wide variety of GPUs including Nvidia’s A2, A10 and A16 Tensor Core, Quadro K2200, and Tesla P4
  • Starting at $81/month
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  • Good and affordable cloud servers
  • Reliable service with minimal downtime
  • Responsive customer support
  • Supports Crypto Proof of Stake (PoS) Validator
  • No hidden costs or bandwidth issues


  • Limits on SMTP services
  • Delayed server availability
  • Identity verification requirement
  • Some issues with customer support

Cherry Servers offers an industry-leading platform for dedicated GPU servers, designed specifically for high-performance computations and deep learning model training. With a huge array of available GPU accelerators, users can configure a custom server to meet their precise computational demands.

With an impressive in-memory computing option of up to 1536GB RAM, Cherry Servers ensures that users can process large data sets without compromising on speed or efficiency. Additionally, with a variety of secure data storage options, users can store their complex datasets in a manner that best suits their needs.

The firm also provides robust private networking allowing for seamless server intercommunication. Moreover, free data flow provision ensures users do not have to stress over data limits, with a generous allowance of 100TB per month.

When it comes to pricing and plans, Cherry Servers offers a comprehensive range of options tailored to suit different budgets and user requirements. Their pricing starts at an affordable $81 per month, allowing users flexible access to powerful GPU accelerators for their deep learning needs.

Taking into account the seamless user experience, strong customer support, and high-performance server specifications, Cherry Servers stands as a compelling choice for businesses and individuals seeking a reliable and cost-effective dedicated GPU server hosting provider.

4. TensorDock

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  • 80% cheaper GPUs
  • VMs provisioned in less than 2 minutes
  • Pre-installed essential software
  • Secure, reliable infrastructure
  • Infinitely scalable with access to over 65,000+ GPUs
  • Starting at $0.05/hour
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  • Competitive pricing
  • Quick deployment and provisioning
  • Multiple GPU options
  • Responsive support team
  • No Strict NAT issues


  • Storage costs when VM is off
  • Limited global reach
  • Complexity for non-technical users
  • Prepaid model
  • Limited storage options

TensorDock is an emerging player in the hosting industry, specializing in GPU-optimized servers primarily intended for deep learning and computational tasks. They have carved out a niche for themselves by offering significantly cheaper GPU resources compared to other cloud service providers, which can be beneficial for developers and organizations on tight budgets.

With an impressive array of over 24 GPU models, TensorDock ensures that customers have the computing power needed for a variety of workloads, especially for demanding tasks like deep learning and massive number crunching.

Performance is a strong point for TensorDock, with users reporting smooth, lag-free experiences. Setup is fairly easy, with virtual machines being ready for use within 2 minutes of configuration.

They offer a flexible service with hourly pricing rates starting from $0.05, and the freedom to adjust configurations as needed. However, it’s worth noting that they follow a prepaid model – customers need to deposit funds before provisioning resources.

While the majority of their services are US-based, making them a preferable choice for US customers, this does limit their appeal to global customers, as latency can be an issue for non-US users.

Overall, TensorDock is a compelling option for those needing GPU-based servers, particularly for deep learning applications, offering competitive pricing and a variety of hardware options.

5. Lambda

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  • Trusted hosting partner for corporations and research labs
  • NVIDIA H200 Tensor Core GPUs available on-demand in a public cloud
  • Custom Lambda dashboard for easy cloud management
  • Comprehensive GPU instances from 1X to 8X setups for varied workloads
  • Starting at $2.49/hour
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  • Ready-to-use for machine learning tasks
  • Cost-effective compared to other cloud providers
  • Networking capabilities suitable for AI model training
  • Reliable performance for professional use


  • Higher pricing compared to traditional hosting
  • Some hardware reliability issues
  • Absence of NVlink in some multi-GPU systems
  • Quality of technical support is inconsistent

Lambda is recognized as a robust hosting solution especially designed for computational and machine learning tasks. With clients ranging from Intel and Microsoft, to Stanford and Harvard, Lambda promises superior performance and reliability.

One of its main strengths lies in its offering of NVIDIA H200 Tensor Core GPUs available on-demand in a public cloud; creating a powerful platform for deep learning and research work

They provide comprehensive GPU instances options, ranging from 1X to 8X, catering to a wide spectrum of needs and workloads. Making it highly scalable and adaptable for individual projects.

Their prices start at $2.49 per hour, providing affordable access to powerful machine learning tools. The cost of using Lambda can be offset in a matter of days when compared to other cloud providers, making it a cost-effective solution for machine learning professionals.

The hardware is ready to use out of the box for PyTorch or TensorFlow models, which allows professionals to immerse themselves in their work without any delay. You can also check TensorFlow GPU hosting.

Lambda’s solutions have proven reliability across various teams of developers. However, the lack of NVlink in some multi-GPU systems and inconsistent quality of technical support have raised concerns for users.

In conclusion, if you require powerful hosting solutions that are tailor-made for machine learning tasks, and are ready to use right out of the box, Lambda could be a great choice.

1 thought on “5 Best GPU Servers for Deep Learning 2024”

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