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Top 10 Cloud GPU Providers in 2024

Updated on Mar 7
9 min read
Written by
Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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Top 10 Cloud GPU Providers in 2024Top 10 Cloud GPU Providers in 2024

AIMultiple team adheres to the ethical standards summarized in our research commitments.

GPU procurement complexity has been increasing with more providers offering GPU cloud options. AIMultiple analyzed GPU cloud providers across most relevant dimensions to facilitate cloud GPU procurement.

While listing pros and cons for each provider, we relied on user reviews on G2, other online reviews as well as our assessment. Here is a summary of major providers:

GPU / AI CSP*Brands**Models***Combinations****Comments
Latitude.sh24Bare metal available
US & EU data centers
Seeweb425Focus: Serving EU customers from EU data center
AWSAWS chips like Trainium716
AzureWorking on own chips920
GCPGoogle Cloud tensor processing units (TPUs)622
Nvidia DGX22Sole focus: High-scale enterprise workloads
OCI512Bare metal available
IBM Cloud36
Jarvis Labs55Sole focus: Cloud GPUs
Lambda Labs37Sole focus: Cloud GPUs
Paperspace COREGraphcore1028Sole focus: Cloud GPUs
ACE Cloud311
Alibaba CloudAlibaba chips like Hanguang 8001112
CirrascaleCerebras, Graphcore, SambaNova1629Focus: Research workloads
Crusoe Cloud418
Datacrunch.io416Sole focus: Cloud GPUs
Serverless GPU providersDepends on providerNot relevant

Transparency statement: Vendors with links are sponsors of this article and are highlighted at the top of relevant lists.

* Cloud service provider (CSP)

** All providers offer Nvidia GPUs. In addition, some CSPs provide hardware from other AI chip makers as indicated in this column.

*** Distinct Nvidia GPU models offered. For example, “A100 40 GB” and “A100 80 GB” are counted as separate models.

**** Distinct multi-GPU combinations offered. For example, “1 x A100 40 GB” and “2 x A100 40 GB” are counted as separate multi-GPU combinations.

GPUs can be delivered in a serverless manner, as virtual GPUs or as bare metal. While serverless offers the easiest way to manage workloads, bare metal offers the highest level of control of the hardware. If you are specifically looking for these, please visit relevant sections:

What are Virtual GPU providers?

Virtual GPUs (vGPUs) are virtual machines that allow multiple users share GPUs over the cloud. They are the most commonly offered form of cloud GPUs. Leading providers include:

Amazon Web Services (AWS)

AWS is the largest cloud platform provider and a leading cloud GPU provider.1Amazon EC2 (Elastic Compute Cloud) offers GPU-powered virtual machine instances facilitating accelerated computations for deep learning tasks. 


Offers seamless integration with other popular AWS solutions like:

  • SageMaker, used for creating, training, deploying, and large-scale application of ML models
  • Simple Storage Service (Amazon S3), Amazon RDS (Relational Database Services) or other AWS storage services, which can serve as a storage solution for training data


  • AWS offers fewer GPU options than some other players like Azure.
  • UI is found to be complex by users
A review about AWS EC2 2
A review about AWS EC2 3
  • On-demand pricing per hour is higher than other big cloud providers. Like other cloud providers, AWS offers volume discounts.

Microsoft Azure

Microsoft Azure, the second largest cloud provider, provides a cloud-based GPU service known as Azure N-Series Virtual Machines, which leverages NVIDIA GPUs like other providers to deliver high-performance computing capabilities.4 This service is particularly suited for demanding applications such as deep learning, simulations, rendering and the training of AI models.

Microsoft is also rumored to have started producing its own chips.5


  • Microsoft Azure is offering a larger set of GPU options than most other providers
  • Free plan offers 12 months of access to some services
  • Azure’s intuitive user interface is praised for its ease of use


  • Some users find that certain advanced features within Azure require a high level of technical expertise to configure and manage effectively
A review about Azure Virtual Machines 6
  • Some users find Azure’s pricing structure complex to navigate and stress the importance of careful planning to avoid unexpected costs

Google Cloud Platform (GCP)

Google Cloud Platform (GCP) is the third biggest cloud platform.7 GCP offers GPU instances that can be attached to existing virtual machines (VMs) or can be part of a new VM setup.


  • UI is easier than other common platforms such as AWS
  • Offers limited free GPU options for Kaggle and Colab users
  • Customers can use 20+ products for free, up to monthly usage limits


  • GPUs must be attached to standard VMs, making pricing confusing
  • Like AWS, GCP offers fewer GPU options than some players like Azure


NVIDIA is the leader in the GPU hardware market. NVIDIA launched its GPU cloud offering, DGX Cloud, by leasing space in leading cloud providers’ (e.g. OCI, Azure and GCP) data centers.

DGX cloud offers NVIDIA Base Command™, NVIDIA AI Enterprise and NVIDIA networking platforms. DGX Cloud instances featured 8 NVIDIA H100 or A100 80GB Tensor Core GPUs at launch.

An initial customer’s, Amgen’s, research team claims 3x faster training of protein LLMs with BioNeMo and up to 100x faster post-training analysis with NVIDIA RAPIDS.8

The offering is enterprise focused with the list price of DGX Cloud instances starting at $36,999 per instance per month at launch.


  • Support from NVIDIA engineers


  • Offering is not suitable for firms with limited GPU needs
  • The service is provided on top of cloud providers’ physical infrastructure. Therefore buyer needs to pay for the margins of both the cloud provider and NVIDIA.

IBM Cloud

The GPU offered by IBM Cloud allows for a flexible process of selecting servers, and it has a seamless integration with the architecture, applications, and APIs of IBM Cloud. This is accomplished via a globally distributed network of data centers that are interconnected.


  • Powerful integration with IBM Cloud architecture and applications
  • Worldwide distributed data centers increases data protection


  • Limited adoption compared to the top 3 providers.9

Oracle Cloud Infrastructure (OCI)

Oracle ramped up its GPU offering after formalizing its partnership with NVIDIA.10

Oracle provides GPU instances in both bare-metal and virtual machine formats for quick, cost-effective, and high-efficiency computing. Oracle’s Bare-Metal instances offer customers the capability to execute tasks in non-virtualized settings. These instances are accessible in regions such as the United States, Germany, and the United Kingdom, with availability under both on-demand and interruptible pricing models.


Oracle serves some of the leading LLM providers like Cohere, a company that Oracle also invested in.11


  • Wide range of cloud products and services. Among the tech giants’ cloud services, only OCI offers bare metal GPUs.12 For GPU cluster users, only OCI offers RoCE v2 for its cluster technology among the tech giants’ cloud services.13
  • Cost-effective compared to other major cloud providers
  • Offers provision for free trial period and some free-forever products


  • User interface perceived as clunky and slow by users
A review about the OIC Compute 14
  • Some users find the documentation difficult to understand
A review about the OIC Compute 15
  • The process of starting to use Oracle Cloud compute services was viewed as bureaucratic, complicated, and time-consuming by some users


CoreWeave is a specialized GPU cloud provider. NVIDIA is one of CoreWeave’s investors. CoreWeave claims to have 45,000 GPUs and to be selected as the first first Elite level cloud services provider by NVIDIA.16

Jarvis Labs

Jarvis Labs, established in 2019 and based in India, specializes in facilitating swift and straightforward training of deep learning models on GPU compute instances. With its data centers located in India, Jarvis Labs is recognized for its user-friendly setup that enables users to start operations promptly.

Jarvis Labs claims to serve 10,000+ AI practitioners.17


  • No credit card required to register
  • A simple interface for beginners


  • Although Jarvis Labs is gaining momentum, its suitability for your business’ enterprise-level tasks would need to be validated. It seems to be catering to small workloads since it is not offering multi-GPU instances.

Lambda Labs

Originally, Lambda Labs was a hardware company offering GPU desktop assembly and server hardware solutions. Since 2018, Lambda Labs offers Lambda Cloud as a GPU platform. The virtual machines they offer are pre-equipped with predominant deep learning frameworks, CUDA drivers, and a dedicated Jupyter notebook. Users can connect to these instances through the web terminal in the cloud dashboard or directly using the given SSH keys.

Lambda Labs claims to be used by 10,000+ research teams and has a purely GPU focused offering.18

Paperspace CORE

Paperspace is a cloud computing platform that offers GPU-accelerated virtual machines, among other services. The company is well-regarded for its focus on GPU-intensive workloads and provides a cloud platform for developing, training, and deploying machine learning models.

Paperspace claims to have served 650,000 users.19


  • Offers a wide range of GPUs compared to other providers
  • Users find the prices fair for the computing power provided
  • Users find the customer service to be friendly and responsive


  • Some users complain about machine availability, both in terms of the free virtual machines and specific machine types not being available in all regions
A review about Paperspace Core 20
  • The integrated Jupyter interface is criticized and lacks some keyboard shortcuts, although a native Jupyter Notebook interface is offered
A review about Paperspace Core 21
  • Longer loading or creation times for machines
  • Monthly subscription fee on top of machine costs can be a downside, and multi-GPU training can be expensive

What are serverless GPU providers?

Serverless is a new cloud computing approach that facilitates cloud management. Many cloud providers are starting to offer serverless GPU offerings. We will be sharing a list here soon.

Explore more on Serverless GPUs.

What are bare-metal GPU providers?

Bare metal is not as commonly provided as GPU VMs. The providers include:

For more, see AIMultiple’s bare-metal GPU provider list.

What are cloud GPU cloud providers based in Europe?

European businesses may prefer to keep their data in Europe for

  • GDPR compliance and data security
  • Offering faster AI inference services to European users

This is possible with some of the global cloud providers but there are also European based cloud GPU providers.


Seeweb is a public cloud provider headquartered in Italy that runs 100% on renewable energy. Seeweb supports IaC via Terraform and offers 5 different GPU models.

Datacrunch provides Nvidia’s A100, H100 RTX6000, V100 models in groups of 1, 2, 4 or 8. The company is based in Helsinki, Finland and relies on 100% renewable energy.


OVHcloud is a public cloud provider headquartered in France. It started offering Nvidia GPUs in 2023 and plans to expand its offering.22


Scaleway offers H100 instances, provides 3 European regions (Paris, Amsterdam, Warsaw) and relies 100% of renewable energy. For high scale users, Nabu 2023 supercomputer with its 1,016 Nvidia H100 Tensor Core GPUs is available.

What are upcoming GPU cloud providers?

These providers have limited reach or scope or recently launched their offerings. Therefore they were not included in the top 10:

Alibaba Cloud

Alibaba’s offering may be attractive for businesses operating in China. It is also available across 20 regions including those in Australia, Dubai, Germany, India, Japan, Singapore, the USA and the UK.23

However, a US or EU organization with access to top secret data in domains such as state, defense or telecom may not prefer to work with a cloud service provider headquartered in China.


Cirrascale is specialized in providing different AI hardware to research teams. Though they are one of the smallest teams in this domain with about ~20 employees, they offer AI hardware from 4 different AI hardware producers.24

Voltage Park

Voltage Park is a non-profit that spent funds including ~$500 million with NVIDIA to set up 24,000 cloud H100 GPUs. 25 26 It offers low-price GPU rental to AI focused companies like Character AI.


What is a cloud GPU platform?

A cloud GPU platform is a service offered by cloud gpu providers that allows users to access and utilize GPU technology remotely. Instead of having physical GPUs installed in local machines, users can use the power of cloud GPUs hosted on efficient cloud GPU platforms. These platforms, like Google Cloud GPUs and NVIDIA GPU instances, harness the high-performance capabilities of GPUs such as the NVIDIA Tesla series, making them accessible to users through the cloud.

Why do you need cloud GPU services?

Cloud GPU services are essential for individuals and businesses that require immense computational power without the capital expense of buying and maintaining physical GPUs. As the demand for high-performance computing increases in areas like artificial intelligence, deep learning, and graphics rendering, an efficient cloud GPU platform can offer scalable and cost-effective solutions. 

Moreover, with the emergence of best cloud GPU platforms, users can now rent GPU power on-demand, suitable for short-term intensive tasks or projects. This way, users can leverage the cutting-edge capabilities of services like Google Cloud GPUs or NVIDIA GPU instances without committing to a significant hardware investment.

How secure are cloud GPU services?

Security is a top priority for any cloud GPU provider. The best cloud GPU platforms implement stringent security measures, ensuring that users’ data and applications remain protected. This includes data encryption during transit and at rest, secure access controls, regular security audits, and more. Providers of services like NVIDIA GPU instances and Google Cloud GPUs invest heavily in maintaining the integrity and confidentiality of user data. 

As with any cloud service, while the provider takes measures to secure the infrastructure, users should also follow best practices in data management and access control to ensure optimal security.

Cem Dilmegani
Principal Analyst

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

Sources: Traffic Analytics, Ranking & Audience, Similarweb.
Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
Data management barriers to AI success, Deloitte.
Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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