5 Best GPU Cloud 2024 (Compared)

GPU Cloud Hosting refers to the practice of leveraging cloud computing services that offer access to Graphics Processing Units (GPUs) alongside traditional computing resources.

This innovative hosting solution is designed to cater to applications and processes that demand high levels of parallel computing power, making it an ideal choice for a wide range of computationally intensive tasks.

By providing on-demand access to GPUs, cloud hosting enables businesses and individual developers to accelerate their applications without the need for significant upfront investment in physical hardware.

What are the Best GPU Cloud Providers?

1. OVHCloud

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  • Specially designed for processing massively parallel tasks
  • Powerful NVIDIA GPUs that provide great performance
  • Flexible configurations to meet various workloads
  • Supports popular AI frameworks for machine learning and deep learning
  • Starting at $0.77/hour
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  • User-friendly and responsive AI cloud hosting
  • Scalability and customization to cater different project needs
  • Efficient for AI and Deep Learning tasks
  • No upfront costs for GPU servers


  • Outdated hardware
  • Lack of specific CPU information
  • Mixed customer reviews
  • Limited geographical reach

OVHCloud is an ideal platform for tasks requiring massively parallel processing, like machine learning and deep learning. They have integrated NVIDIA GPUs, known for their impressive performance and functionality. This makes the platform highly suited for data-intensive operations.

Being an OVHcloud’s solution, you get the flexibility of on-demand resources and hourly billing. This can be quite cost-effective for businesses of all sizes.

In terms of hardware, OVHcloud offers a range of configurations. You have choices for different memory sizes from 45GB to 1.52TB and storage capacities reaching up to 2TB NVMe drives, entirely adaptable to your project needs.

The platform ensures seamless user experience, supported by an easy-to-use interface and straightforward setup process. Additionally, it supports a wide range of popular AI frameworks, enhancing its appeal to AI and machine learning professionals.

Despite certain limitations such as outdated hardware, lack of specific CPU information, and limited geographical reach, OVHCloud remains an excellent GPU server choice. With its distinct features and compliance with various ISO/IEC standards for data management, it offers a reliable and secure cloud hosting solution for companies and individuals involved in AI and deep learning tasks.

2. Paperspace

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  • Instant access to high-performance GPU and IPUs
  • Pre-configured templates for swift set-up
  • Infrastructure abstraction with easy scalability
  • Latest NVIDIA GPUs and diverse ML frameworks
  • Starting at $0.0045/hour
  • Save up to 70% on compute costs
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  • User-friendly interface
  • Quick setup process
  • Fully scalable platform with automatic versioning
  • Auto-shutdown feature assists in cost management


  • Difficulties with Conda environments in JupyterLab
  • Lagging performance in lower-tier machines
  • Limited auto-shutdown time
  • Issues with billing errors

Paperspace stands out as a leading brand in the field of GPU cloud hosting. By providing immediate access to high-performance GPU and IPUs, they offer a robust platform that caters primarily to AI developers and other computational environments.

The platform leverages the power of NVIDIA’s expansive GPU catalog and incorporates preinstalled ML frameworks. This not only enhances the performance but also provides the users with a range of options to select the resources that fulfill their specific needs.

The pricing model of Paperspace is highly economical, with rates starting as low as $0.0045 per hour. By implementing per-second billing, the platform allows users to save up to 70% on compute costs. Also, the on-demand pricing structure means the users only pay for what they use, ensuring no unnecessary costs are incurred.

The process of setting up and managing virtual machines on Paperspace is streamlined and user-friendly. Pre-configured templates enable the users to become operational in seconds.

On the flip side, users have reported difficulties in activating Conda environments in JupyterLab, which suggests that more streamlined processes are needed. There are also some instances of billing errors, which can be off-putting for users.

Nevertheless, with a comprehensive offering of high-performing GPUs, practical features, and an emphasis on cost-effectiveness, Paperspace is lauded as a reliable solution for individuals and organizations seeking quality GPU cloud hosting.

3. GPU Mart

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  • Supports 19+ models of GPU cards
  • Cost-effective and flexible hosting solutions
  • 24/7/365 Technical support
  • Customizable hardware configurations
  • Starting at $21.00/month
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  • Dedicated GPU Server Hosting
  • Support for 8+ GPU use scenarios
  • 99.9% uptime guarantee
  • Multi-data center locations in the USA


  • Maintenance of GPU hardware can be challenging
  • Potential data security concerns in remote GPU servers
  • Affordable pricing might still be high for some due to the use of specialized GPU servers
  • High network latency can affect real-time processing and communication

GPU Mart is a high-value player in the industry of GPU hosting, offering cutting-edge infrastructure and support for over 19 different models of GPU cards. With exceedingly powerful servers optimized for maximum speed and performance, this provider is a great solution for complex high-performance computing projects.

Whether you’re engaged in deep learning, gaming, running Andriod simulations or dealing with other heavy-lifting applications, GPU Mart provides robust, reliable, and swift processing power. A selection of 20+ supported GPU Computing Applications is available to cater to your specific needs.

Notably, the robust technical support service available 24/7 ensures all technical hitches are promptly resolved. For users with hardware-specific needs, GPU Mart offers the flexibility of customizable hardware configurations.

The pricing plans of GPU Mart are designed to be cost-effective, starting at an affordable $21.00 per month. They also offer a dedicated GPU server rental service for high-performance tasks, providing various high-performance GPUs for rent. This is a budget-friendly alternative, especially for short-term or specific project needs.

With a solid reputation of 99.9% uptime, easy startup process, round-the-clock technical services and a multiple-location data center based in the USA for optimum speed, GPU Mart lives up to its promise of delivering high-quality, professional GPU hosting.

4. Lambda

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  • Trusted by leading companies and research labs
  • NVIDIA powered-AI optimized GPUs
  • Multi-GPU instances
  • Lambda Stack with PyTorch®, TensorFlow, and NVIDIA support
  • Starting at $2.49/hour
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  • High-performance GPUs for Machine Learning
  • Customized solutions for different workloads & budgets
  • Reliable performance and convenient setup
  • One of the first to offer NVIDIA H100 and H200 on-demand


  • Higher prices compared to some competitors
  • Some reports of hardware reliability issues
  • Lack of NVlink in some GPU configurations
  • Concerns about the quality of technical support

Lambda is a leading provider of high-performance cloud services optimized for artificial intelligence and machine learning workloads. Trusted by companies like Intel, Microsoft, Amazon Research, and top universities, Lambda ensures optimal performance for your intense computational workloads.

Lambda offers NVIDIA-powered cloud GPUs on-demand, with pricing starting at $2.49 an hour. It not only provides high performance but also the flexibility to scale resources according to the workload and budget. The inclusion of NVIDIA H100 and H200 Tensor Core GPUs makes Lambda one of the pioneering providers to offer these powerful GPUs on-demand.

The Lambda Stack, adopted by over 50,000 machine learning teams, supports key libraries such as PyTorch and TensorFlow, ensuring seamless machine learning operations right out of the box.

Furthermore, Lambda’s reserved cloud services come with Quantum-2 InfiniBand Networking, enabling high-speed communications for multi-GPU instances. This makes it the go-to option for companies aiming to leverage large-scale GPU deployments.

Despite some concerns about high prices, hardware reliability, the lack of NVlink in some configurations, and the quality of technical support, Lambda remains a top choice for organizations looking to accelerate their machine learning workloads in the cloud at scale.

With its wide range of GPU instances to choose from, Lambda proves to be a reliable and efficient choice for both individuals and companies looking to power their computing needs.

5. Google Cloud

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  • High-performance GPUs for machine learning, scientific computing, and generative AI
  • Balance processor, memory, high performance disk, and up to 8 GPUs
  • Access to industry-leading storage, networking, and data analytics technologies
  • Offers NVIDIA T4, P4, V100, P100, K80 – GPUs for a range of compute options
  • Starting at $0.14/month for NVIDIA T4
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  • Offers a wide range of GPU options
  • Flexible performance and pricing for workload optimization
  • Easy to setup and use
  • Supports advanced tasks like deep learning and AI


  • Scarcity of high-demand GPUs
  • Geographical limitations on availability
  • Potential quota management issues
  • Lack of transparency in availability
  • Possible accessibility issues for smaller groups or independent developers

Google Cloud is lauded for its high-performance GPUs, designed for machine learning, scientific computing, and generative AI. It uses optimal balance processor, memory, high performance disk, and up to 8 GPUs per instance, ensuring tailored performance for individual workloads.

Recognized for its flexibility and customizability, Google Cloud offers a myriad of high-performance processors, including NVIDIA T4, P4, V100, P100, and K80, allowing users to choose the best fit for their budget and performance needs.

In terms of pricing, Google Cloud has structured its costs to be highly flexible, providing solutions that start at just $0.14 per month for a NVIDIA T4 GPU. Moreover, this pricing model, combined with per-second billing, ensures you only pay for what you need.

Despite its many advantages, Google Cloud does face potential challenges. Scarcity of certain high-demand GPUs and geographical limitations hinder its delivery. Additionally, some users reported quota management issues and lack of transparency in GPU availability.

Nonetheless, Google Cloud presents an efficient choice for users requiring GPU cloud hosting. Its flexible pricing, wide range of GPU options, and industry-leading storage, networking, and data analytics technologies make it a favoured choice amongst AI and machine learning practitioners.

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