The Best Cloud GPU Rentals for AI, Deep Learning, Cloud Compute & ML

Your GPU computing strategy is essential regardless of the heavy computing applications that your business does, such as artificial intelligence, machine learning, or 3D visualization.

In enterprises, deep learning models used to be computationally and trainingly laborious. Because it required a lot of their time, was costly, and created storage and space concerns, their productivity dropped as a result.

This problem is fixed in the most recent GPU generation. Because of their exceptional efficiency in parallel processing, they can manage huge calculations and speed up the training of your AI models.

Deep learning neural networks may be trained 250 times faster on GPUs than on CPUs; additionally, a new breed of cloud GPUs is revolutionizing data science and other future technologies by offering even faster

Our Top Picks for the Best Cloud GPU Providers

1. Latitude
2. OVHCloud
3. Vast AI
4. Paperspace
5. Vultr
6. G Core
7. Genesis Cloud
8. Tensor Dock
9. Microsoft Azure
10. IBM Cloud
11. FluidStack
12. Leader GPU
13. DataCrunch
14. Google Cloud GPU
15. Amazon AWS
16. Linode
17. Runpod
18. Run.ai
19. CoreWeave

#1: Latitude.sh

Deploy and administer high-performance bare metal servers in seconds using the cloud native tools you already know.

Latitude.sh stands out as a comprehensive cloud infrastructure service provider, offering scalable, high-performance solutions tailored to enterprise needs. Their diverse range of offerings includes dedicated bare metal servers, robust cloud acceleration, customizable builds, cost-effective storage solutions, and robust network infrastructure, making them a preferred choice for businesses aiming to expand their cloud capabilities.

Services

Latitude.sh caters to a spectrum of corporate requirements through its versatile features:

Bare Metal Servers

• Rapid deployment
• Remote access
• RAID configurations
• Support for various operating systems
• Blend of physical server performance with virtual environment flexibility

Cloud Acceleration (Accelerate)

• GPU instances for resource-intensive operations like AI and machine learning
• Efficient management of heavy workloads, beneficial for data scientists and researchers

Custom Builds (Build)

• Tailored infrastructure solutions to meet specific business needs
• Flexible configurations, from RAM capacity to rack setups

Storage Solutions

• NVMe drive-based storage ensuring high performance
• Fault tolerance and zero egress costs, ideal for latency-sensitive applications and data-intensive operations

Network Infrastructure

• Carrier-grade architecture with features like 20 TB bandwidth/server, DDoS protection, and private networking capabilities
• Crucial for handling large internet traffic volumes securely and reliably

Products

Latitude.sh offers a suite of products optimized for performance and security:

• Metal servers with SSD and NVMe disks for optimal speed and reliability
• Accelerate GPU instances designed for computationally intensive tasks such as machine learning
• Build: Automated deployment of customized bare metal servers
• High-performance storage options tailored for data-intensive applications

Plans

Latitude.sh offers flexible plans with features like:

• Quick deployment of Metal Compute instances
• Remote access and RAID configurations
• Hourly billing for cost-effective resource utilization

Pros

• Comprehensive customizable cloud solutions
• High-performance storage and networking capabilities
• Zero egress costs for storage
• 24/7 support and user-friendly interfaces

Cons

• Pricing transparency could be improved

Solutions: AI Acceleration

Latitude.sh’s Accelerate service offers dedicated instances equipped with NVIDIA H100 GPUs, ideal for high-performance AI infrastructure development. Key features include:

• NVIDIA H100 GPUs for up to 9x faster model training
• Pre-configured Deep Learning Tools like TensorFlow and PyTorch
• Global Edge sites for reduced latency
• API and Integration Ready for streamlined operations
• Intuitive Dashboard for easy management

Web 3 Infrastructure

Latitude.sh provides a globally distributed node infrastructure tailored for Web3 and DeFi projects, offering scalability and optimized bandwidth costs.

Online Gaming

Latitude.sh offers low-latency, high-performance bare metal servers optimized for online gaming, featuring custom infrastructure, improved performance, custom connectivity, and DDoS protection.

Use Cases: DDoS Protection

Latitude.sh’s DDoS protection service safeguards dedicated servers from various network threats, offering comprehensive mitigation, managed defense mechanisms, and no additional cost.

Containers

Latitude.sh highlights the benefits of running containers on bare metal, offering increased performance and resource utilization.

Streaming

Latitude.sh’s streaming solution ensures high-performance on-demand and live video streaming, featuring excellent-quality network, origin and edge services, and secure content delivery.

Features

Latitude.sh provides a range of features including:

• Global edge locations for optimal performance
• Carrier-grade network with 20 TB bandwidth/server
• Programmable Network for efficient resource management
• Elastic IPs and private networking for enhanced flexibility and security
• Developer-friendly APIs and integrations like Terraform Provider and SDKs

2. OVHCloud

OVHcloud: Empowering Cloud Solutions with NVIDIA Tesla V100 GPUs

OVHcloud’s cloud servers are engineered to handle extensive concurrent workloads, leveraging multiple instances of NVIDIA Tesla V100 graphics processors to fulfill deep learning and artificial intelligence requirements effectively.

GPU Acceleration

Collaborating with NVIDIA, OVHcloud delivers top-notch GPU-accelerated systems tailored for high-performance computing, AI, and deep learning tasks.

Container Management

Easily install and manage GPU-accelerated containers through a comprehensive catalog, maximizing the potential of each NVIDIA Tesla V100 card without any virtualization layer hindrance.

Compliance and Security

OVHcloud’s services and facilities adhere to ISO/IEC 27017, 27701, 27001, and 27018 standards, ensuring robust information security management systems (ISMS) and privacy information management systems (PIMS) to mitigate risks and vulnerabilities effectively.

NVIDIA Tesla V100 Features

The NVIDIA Tesla V100 boasts an array of features, including:

• PCIe 32 Gbps
• 16 GB HBM2 memory
• 900 GB/s bandwidth
• Single precision: 14 teraFLOPs
• Double precision: 7 teraFLOPs
• Deep learning: 112 teraFLOPs

Vast AI

Vast AI stands out as an innovative player in the cloud GPU market, offering a decentralized cloud computing platform.

Potential for Cost Efficiency

By tapping into underutilized GPU resources from various sources, including commercial and private individuals, Vast AI presents the potential for lower costs and a wide range of available hardware options. However, this approach may result in increased variability in terms of performance and reliability.

Cost-Effective GPU Workloads

Vast AI is particularly attractive to clients seeking cost-effective solutions for intermittent or less critical GPU workloads, such as experimental AI projects, small-scale data processing, or individual research endeavors.

Pros

• Potential for cost savings
• Wide variety of available hardware
• Cost-effective for sporadic or less critical GPU workloads
• Ideal for experimental AI projects and individual research

Cons

• Decentralized resources may lead to variability in performance and reliability

Paperspace

Paperspace stands out in the cloud GPU service market with its user-friendly approach, making sophisticated computing accessible to a broader audience.

User-Friendly Cloud GPU Service

Paperspace’s platform is particularly favored by developers, data scientists, and AI enthusiasts for its straightforward setup and deployment of GPU-powered virtual machines, optimized for machine learning tasks.

Optimized for Machine Learning

Their services are tailored specifically for machine learning and AI development, featuring pre-installed and configured environments for various ML frameworks.

Tailored for Creative Professionals

Paperspace also caters to creative professionals, such as graphic designers and video editors, offering high-performance GPUs and rendering capabilities. The platform’s diverse pricing structures, including per-minute billing, appeal to both small-scale customers and large organizations.

Pros

• Simple and user-friendly installation
• Popular among developers, data scientists, and AI enthusiasts
• Pre-installed and configured environments for machine learning frameworks
• Ideal for creative professionals utilizing high-performance GPUs
• Flexible price models, including per-minute billing

Cons

• May not offer the same level of customization as other providers

Vultr

Vultr stands out in the cloud computing market with its focus on simplicity and performance, offering a range of cloud services, including high-performance GPU instances.

Simple and Rapid Deployment

Vultr’s services are characterized by simple and rapid deployment, making them particularly attractive to small and medium-sized organizations. Their competitive pricing further enhances their appeal.

GPU Solutions for Various Applications

Vultr’s GPU solutions cater to a wide range of applications, including AI and machine learning, video processing, and gaming servers.

Global Network of Data Centers

With a global network of data centers, Vultr ensures low-latency and reliable services across multiple geographies. Their transparent pricing methodology enables organizations to accurately estimate and manage their cloud spending.

Pros

• Simple and quick deployment
• Competitive pricing
• Ideal for small and medium-sized businesses
• Suitable for AI, machine learning, video processing, and gaming
• Global network of data centers for low-latency services

Cons

• Larger competitors may offer more advanced features

G Core

Gcore specializes in cloud and edge computing services, with a focus on solutions tailored for the gaming and streaming sectors.

High Performance Computing

Gcore’s GPU cloud services are designed to tackle high-performance computing tasks, offering ample computational capacity for graphically intensive applications. Their scalable and reliable infrastructure is particularly well-suited for MMO gaming, VR applications, and real-time video processing.

Global Content Delivery Network

In addition to cloud services, Gcore provides global content delivery network (CDN) services, enhancing their offerings with high-speed data transmission and low latency for end customers worldwide.

Pros

• High-performance computing for graphically intensive applications
• Scalable and durable infrastructure
• Global content delivery network (CDN) services
• Suitable for MMO gaming, VR applications, and real-time video processing

Cons

• May be less suitable for non-gaming or non-streaming workloads

Lambda Labs

Lambda Labs is a dedicated firm focusing on AI and machine learning, offering specialized GPU cloud instances tailored for these purposes.

Pre-configured Environments

Lambda Labs is renowned in the AI research field for providing pre-configured settings with major AI frameworks, streamlining setup processes for data scientists and researchers. Their solutions are optimized for deep learning, featuring high-end GPUs and extensive memory capacities.

Clients Include Academic Institutions

With a client base including academic institutions, AI startups, and large corporations working on complex AI models and datasets, Lambda Labs demonstrates its commitment to the AI research field. In addition to cloud services, they also offer specialized equipment for AI research.

Pros

• Pre-configured settings featuring major AI frameworks
• High-end GPUs and extensive memory capacities for optimized deep learning performance
• Ideal for AI research, academic institutions, and startups

Cons

• Specialized focus and pricing tailored to AI research may limit applicability for other use cases

Comprehensive Buyer’s Guide to Cloud GPU Services

In today’s data-driven world, businesses and researchers rely heavily on high-performance computing to tackle complex tasks such as AI training, machine learning algorithms, data processing, and graphics-intensive applications. Cloud GPU services have emerged as a vital resource for accessing the computational power needed to accomplish these tasks efficiently. However, with numerous providers offering a variety of services, choosing the right cloud GPU service can be a daunting task. This comprehensive buyer’s guide aims to simplify the process by outlining key factors to consider when evaluating cloud GPU services.

Understanding Your Requirements

Before diving into the selection process, it’s crucial to understand your specific requirements and objectives. Consider the following questions:

• What type of tasks will you be performing with the GPU instances? (e.g., AI training, machine learning, graphics rendering)
• How much computational power do you need? (e.g., number of GPUs, memory capacity, processing speed)
• Are there any specific software frameworks or tools you require? (e.g., TensorFlow, PyTorch, CUDA)
• What level of scalability and flexibility do you need? (e.g., on-demand scaling, customizable configurations)
• What is your budget for cloud GPU services?

Key Factors to Consider

Once you have a clear understanding of your requirements, you can evaluate cloud GPU services based on the following key factors:

1. Performance and Hardware

• Look for providers that offer high-performance GPUs with the latest hardware specifications, such as NVIDIA Tesla or AMD Radeon.
• Consider factors like GPU memory capacity, processing speed, and the number of cores to ensure they meet your workload requirements.
• Check if the provider offers customizable configurations to accommodate varying performance needs.

2. Software Support and Pre-configured Environments

• Ensure that the cloud GPU service supports the software frameworks and tools you need for your projects, such as TensorFlow, PyTorch, or CUDA.
• Look for providers that offer pre-configured environments with major AI frameworks, saving you time on setup and configuration.
• Check for compatibility with popular development environments and integrated development tools (IDEs).

3. Scalability and Flexibility

• Evaluate the scalability options offered by the provider, including on-demand scaling, auto-scaling, and customizable instance sizes.
• Consider your future growth needs and choose a provider that can accommodate increasing workloads without significant downtime or performance degradation.
• Look for flexible pricing models that allow you to pay only for the resources you use and adjust your capacity as needed.

4. Reliability and Uptime

• Assess the provider’s track record for reliability and uptime, including their data center infrastructure, network reliability, and disaster recovery capabilities.
• Look for providers that offer SLA-backed guarantees for uptime and performance, ensuring minimal downtime and consistent service levels.

5. Security and Compliance

• Prioritize security features such as data encryption, network isolation, and access controls to protect your sensitive workloads and data.
• Check if the provider complies with industry standards and regulations relevant to your business, such as GDPR, HIPAA, or SOC 2.
• Evaluate their data protection measures, backup and recovery options, and incident response protocols.

6. Pricing and Cost Management

• Compare pricing models, including pay-as-you-go, subscription-based, and reserved instances, to find the most cost-effective option for your budget and usage patterns.
• Look for transparent pricing structures with no hidden fees or unexpected charges, and consider factors like data transfer costs, storage fees, and instance types.
• Utilize cost management tools and monitoring dashboards provided by the provider to track your usage and optimize costs over time.

7. Support and Customer Service

• Evaluate the provider’s customer support options, including documentation, tutorials, forums, and live chat support, to ensure timely assistance when needed.
• Look for providers that offer 24/7 technical support and dedicated account managers for personalized assistance with complex issues or customization requests.
• Consider the provider’s reputation for customer service and responsiveness based on reviews, testimonials, and industry benchmarks.

Top Cloud GPU Service Providers

Finally, consider researching and comparing the offerings of leading cloud GPU service providers in the market, such as:

1. Amazon Web Services (AWS) — Offers a range of GPU instances with support for popular AI frameworks and scalable infrastructure.
2. Microsoft Azure — Provides GPU-accelerated virtual machines for AI, machine learning, and graphics-intensive workloads.
3. Google Cloud Platform (GCP) — Offers high-performance GPUs with pre-configured environments for deep learning and data analytics.
4. IBM Cloud — Provides GPU instances for AI and machine learning, along with tools for data science and analytics.
5. NVIDIA GPU Cloud (NGC) — Offers a comprehensive ecosystem of GPU-optimized software containers and frameworks for AI and HPC.

Conclusion

Choosing the right cloud GPU service provider requires careful consideration of your specific requirements, performance needs, scalability options, security features, and pricing models. By understanding these key factors and conducting thorough research, you can select a provider that meets your needs and empowers your organization to succeed in today’s competitive landscape of high-performance computing and AI-driven innovation.