Top 10 Cloud GPU Providers in 2023
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 offered** | # of models offered*** | # of multi-GPU combinations**** | Comments |
|---|---|---|---|---|
| AWS | AWS chips like Trainium | 7 | 16 | |
| Azure | Working on own chips | 9 | 20 | |
| GCP | Google Cloud tensor processing units (TPUs) | 6 | 22 | |
| Nvidia DGX | 2 | 2 | Sole focus: High-scale enterprise workloads | |
| OCI | 5 | 12 | Bare metal available | |
| IBM Cloud | 3 | 6 | ||
| CoreWeave | 9 | 63 | ||
| Jarvis Labs | 5 | 5 | Sole focus: Cloud GPUs | |
| Lambda Labs | 3 | 7 | Sole focus: Cloud GPUs | |
| Paperspace CORE | Graphcore | 10 | 28 | Sole focus: Cloud GPUs |
| Alibaba Cloud | Alibaba chips like Hanguang 800 | 11 | 12 | |
| Cirrascale | Cerebras, Graphcore, SambaNova | 16 | 29 | Focus: Research workloads |
| Datacrunch.io | 4 | 16 | Sole focus: Cloud GPUs | |
| Latitude.sh | 1 | 1 | Bare metal available | |
| OVHcloud | 3 | 3 | ||
| Scaleway | 1 | 2 | ||
| Serverless GPU providers | Depends on provider | Not relevant |
* 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.
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.
Pros
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
Cons
- AWS offers fewer GPU options than some other players like Azure.
- UI is found to be complex by users
- 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
Pros
- 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
Cons
- Some users find that certain advanced features within Azure require a high level of technical expertise to configure and manage effectively
- 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.
Pros
- 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
Cons
- GPUs must be attached to standard VMs, making pricing confusing
- Like AWS, GCP offers fewer GPU options than some players like Azure
NVIDIA DGX Cloud
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.
Pros
- Support from NVIDIA engineers
Cons
- 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.
Pros
- Powerful integration with IBM Cloud architecture and applications
- Worldwide distributed data centers increases data protection
Cons
- 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.
Customers
Oracle serves some of the leading LLM providers like Cohere, a company that Oracle also invested in.11
Pros
- 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
Cons
- User interface perceived as clunky and slow by users
- Some users find the documentation difficult to understand
- The process of starting to use Oracle Cloud compute services was viewed as bureaucratic, complicated, and time-consuming by some users
CoreWeave
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
Pros
- No credit card required to register
- A simple interface for beginners
Cons
- 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
Pros
- 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
Cons
- Some users complain about machine availability, both in terms of the free virtual machines and specific machine types not being available in all regions
- The integrated Jupyter interface is criticized and lacks some keyboard shortcuts, although a native Jupyter Notebook interface is offered
- 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 leading serverless GPU providers?
Serverless is a new approach that facilitates cloud management. Many cloud providers are starting to offer serverless gpu offerings.
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. This is possible with some of the global cloud providers but there are also European based cloud GPU providers.
Datacrunch.io
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
OVHcloud a public cloud provider headquartered in France. It started offering Nvidia GPUs in 2023 and plans to expand its offering.22
Scaleway
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
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
FAQ
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.
External links
- Big Three Dominate the Global Cloud Market, Statista, Retrieved July 19, 2023
- https://www.g2.com/products/amazon-ec2/reviews/amazon-ec2-review-8154729
- https://www.g2.com/products/aws-cloud/reviews/aws-cloud-review-8271023
- Same Statista source as above
- “Microsoft to Debut AI Chip Next Month That Could Cut Nvidia GPU Costs“. The Information. October 6, 2023. October 8, 2023
- https://www.g2.com/products/azure-virtual-machines/reviews/azure-virtual-machines-review-8145738
- Same Statista source as above
- “NVIDIA Launches DGX Cloud, Giving Every Enterprise Instant Access to AI Supercomputer From a Browser“. NVIDIA. March 21, 2023. Retrieved September 26, 2023.
- Same Statista source as above
- “Oracle and NVIDIA Partner to Speed AI Adoption for Enterprises“. Oracle. October 18, 2022. Retrieved September 26, 2023
- “Oracle to Deliver Powerful and Secure Generative AI Services for Business“. Oracle. June 13, 2023. Retrieved October 10, 2023
- GPU instances, Oracle, Retrieved July 19, 2023
- Oracle Cloud Infrastructure Blog, Oracle, Retrieved July 19, 2023
- https://www.g2.com/products/oracle-cloud-infrastructure-compute/reviews/oracle-cloud-infrastructure-compute-review-7509159
- https://www.g2.com/products/oracle-cloud-infrastructure-compute/reviews/oracle-cloud-infrastructure-compute-review-8144154
- “CoreWeave Becomes NVIDIA’s First Elite Cloud Services Provider for Compute“. CoreWeave. Retrieved September 26, 2023.
- “Rent GPU“. Jarvislabs.ai. Retrieved October 3, 2023
- “NVIDIA DGX™ Systems with Lambda“. Lambda Labs. Retrieved October 3, 2023
- “Customers“. Paperspace. Retrieved October 3, 2023
- https://www.g2.com/products/paperspace-core/reviews/paperspace-core-review-5092833
- https://www.g2.com/products/paperspace-core/reviews/paperspace-core-review-6974232
- “GPU“. OVHcloud. Retrieved October 8, 2023
- “Choosing the Best Hosting Region for You“. Alibaba Cloud. Retrieved October 3, 2023.
- “Cirrascale Cloud Services“. Linkedin. Retrieved October 11, 2023.
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 offered** | # of models offered*** | # of multi-GPU combinations**** | Comments |
|---|---|---|---|---|
| AWS | AWS chips like Trainium | 7 | 16 | |
| Azure | Working on own chips | 9 | 20 | |
| GCP | Google Cloud tensor processing units (TPUs) | 6 | 22 | |
| Nvidia DGX | 2 | 2 | Sole focus: High-scale enterprise workloads | |
| OCI | 5 | 12 | Bare metal available | |
| IBM Cloud | 3 | 6 | ||
| CoreWeave | 9 | 63 | ||
| Jarvis Labs | 5 | 5 | Sole focus: Cloud GPUs | |
| Lambda Labs | 3 | 7 | Sole focus: Cloud GPUs | |
| Paperspace CORE | Graphcore | 10 | 28 | Sole focus: Cloud GPUs |
| Alibaba Cloud | Alibaba chips like Hanguang 800 | 11 | 12 | |
| Cirrascale | Cerebras, Graphcore, SambaNova | 16 | 29 | Focus: Research workloads |
| Datacrunch.io | 4 | 16 | Sole focus: Cloud GPUs | |
| Latitude.sh | 1 | 1 | Bare metal available | |
| OVHcloud | 3 | 3 | ||
| Scaleway | 1 | 2 | ||
| Serverless GPU providers | Depends on provider | Not relevant |
* 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.
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.
Pros
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
Cons
- AWS offers fewer GPU options than some other players like Azure.
- UI is found to be complex by users
- 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
Pros
- 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
Cons
- Some users find that certain advanced features within Azure require a high level of technical expertise to configure and manage effectively
- 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.
Pros
- 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
Cons
- GPUs must be attached to standard VMs, making pricing confusing
- Like AWS, GCP offers fewer GPU options than some players like Azure
NVIDIA DGX Cloud
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.
Pros
- Support from NVIDIA engineers
Cons
- 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.
Pros
- Powerful integration with IBM Cloud architecture and applications
- Worldwide distributed data centers increases data protection
Cons
- 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.
Customers
Oracle serves some of the leading LLM providers like Cohere, a company that Oracle also invested in.11
Pros
- 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
Cons
- User interface perceived as clunky and slow by users
- Some users find the documentation difficult to understand
- The process of starting to use Oracle Cloud compute services was viewed as bureaucratic, complicated, and time-consuming by some users
CoreWeave
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
Pros
- No credit card required to register
- A simple interface for beginners
Cons
- 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
Pros
- 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
Cons
- Some users complain about machine availability, both in terms of the free virtual machines and specific machine types not being available in all regions
- The integrated Jupyter interface is criticized and lacks some keyboard shortcuts, although a native Jupyter Notebook interface is offered
- 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 leading serverless GPU providers?
Serverless is a new approach that facilitates cloud management. Many cloud providers are starting to offer serverless gpu offerings.
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. This is possible with some of the global cloud providers but there are also European based cloud GPU providers.
Datacrunch.io
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
OVHcloud a public cloud provider headquartered in France. It started offering Nvidia GPUs in 2023 and plans to expand its offering.22
Scaleway
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
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
FAQ
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.
External links
- Big Three Dominate the Global Cloud Market, Statista, Retrieved July 19, 2023
- https://www.g2.com/products/amazon-ec2/reviews/amazon-ec2-review-8154729
- https://www.g2.com/products/aws-cloud/reviews/aws-cloud-review-8271023
- Same Statista source as above
- “Microsoft to Debut AI Chip Next Month That Could Cut Nvidia GPU Costs“. The Information. October 6, 2023. October 8, 2023
- https://www.g2.com/products/azure-virtual-machines/reviews/azure-virtual-machines-review-8145738
- Same Statista source as above
- “NVIDIA Launches DGX Cloud, Giving Every Enterprise Instant Access to AI Supercomputer From a Browser“. NVIDIA. March 21, 2023. Retrieved September 26, 2023.
- Same Statista source as above
- “Oracle and NVIDIA Partner to Speed AI Adoption for Enterprises“. Oracle. October 18, 2022. Retrieved September 26, 2023
- “Oracle to Deliver Powerful and Secure Generative AI Services for Business“. Oracle. June 13, 2023. Retrieved October 10, 2023
- GPU instances, Oracle, Retrieved July 19, 2023
- Oracle Cloud Infrastructure Blog, Oracle, Retrieved July 19, 2023
- https://www.g2.com/products/oracle-cloud-infrastructure-compute/reviews/oracle-cloud-infrastructure-compute-review-7509159
- https://www.g2.com/products/oracle-cloud-infrastructure-compute/reviews/oracle-cloud-infrastructure-compute-review-8144154
- “CoreWeave Becomes NVIDIA’s First Elite Cloud Services Provider for Compute“. CoreWeave. Retrieved September 26, 2023.
- “Rent GPU“. Jarvislabs.ai. Retrieved October 3, 2023
- “NVIDIA DGX™ Systems with Lambda“. Lambda Labs. Retrieved October 3, 2023
- “Customers“. Paperspace. Retrieved October 3, 2023
- https://www.g2.com/products/paperspace-core/reviews/paperspace-core-review-5092833
- https://www.g2.com/products/paperspace-core/reviews/paperspace-core-review-6974232
- “GPU“. OVHcloud. Retrieved October 8, 2023
- “Choosing the Best Hosting Region for You“. Alibaba Cloud. Retrieved October 3, 2023.
- “Cirrascale Cloud Services“. Linkedin. Retrieved October 11, 2023.
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