How to Choose a Cloud GPU Provider

The pricing and product information in this article is accurate as of October 1, 2024.

GPUs (Graphics Processing Units) are increasingly being used for artificial intelligence (AI) and machine learning (ML) workloads due to their ability to process vast amounts of data quickly. Unlike CPUs, which handle tasks sequentially, GPUs excel at parallel processing, making them ideal for compute-intensive applications

As computing demands have grown, especially for applications requiring high-definition visuals and complex operations such as deep learning and graphics rendering, the need for more powerful resources has driven advancements in GPU technology. While CPUs provide the foundation for faster computing, GPUs offer the efficiency needed for dense, high-speed workloads.

Historically, many organizations relied on on-premise GPUs, but managing this hardware in-house can be costly and complex. With rapid advancements in GPU technology, cloud-based GPUs have become an attractive alternative, offering access to the latest hardware without the challenges of maintenance or high upfront costs.

In this article, we’ll explore cloud-based GPUs’ benefits and use cases and how to select the right cloud GPU provider.

What are cloud GPUs?

GPUs are microprocessors that use parallel processing capabilities and higher memory bandwidth to perform specialized tasks such as accelerating graphics creation and simultaneous computations. Unlike CPUs optimized for sequential processing, GPUs excel in running multiple computations simultaneously. They have become essential for the dense computing required in gaming, 3D imaging, video editing, and machine learning applications. It’s no secret that GPUs are much faster and more efficient in running dense computations for which CPUs are extremely slow.

GPUs are much faster than CPUs for deep learning operations because the training phase is quite resource-intensive, and the hundreds or thousands of cores in a GPU make these processes much easier to run in parallel. Such operations require extensive data-point processing due to the numerous convolutional and dense operations. These involve several matrix operations between tensors, weights, and layers for large-scale input data and deep networks that characterize deep learning projects.

GPUs’ ability to run these multiple tensor operations faster due to their numerous cores and accommodate more data due to their higher memory bandwidth makes them much more efficient for running deep learning processes than CPUs.

Why use cloud GPU?

While some users opt to have on-premise GPUs, the popularity of cloud GPUs has continued to grow. An on-premise GPU often requires upfront expenses and time on custom installations, management, maintenance, and eventual upgrades. In contrast, GPU instances provided by cloud platforms simply require the users to use the service without needing any of those technical operations at an affordable rate. These platforms provide all the services required to use GPUs for computing and are responsible for managing the GPU infrastructure overall. Furthermore, the onus to do expensive upgrades is not left to the customer, and they can switch between machine types as new machines become available without any additional cost.

Eliminating the technical processes required to self-manage on-premise GPUs allows users to focus on their business specialty, simplifying business operations and improving productivity.

Besides erasing the complexities of managing on-premise GPUs, using cloud GPUs saves time and is often more cost-effective than investing in and maintaining on-site infrastructures. This can benefit startups by turning the capital expenses required to mount and manage such computing resources into the operational cost for using the cloud GPU services, lowering their barrier to building deep learning infrastructures.

Cloud platforms also provide other perks such as data migration, accessibility, integration with ML frameworks, databases, languages such as Python, R, or Java, storage, security, upgrade, scalability, collaboration, control, and support for stress-free and efficient computing.

Use cases for cloud GPUs

Cloud GPUs are suitable for various specialized tasks, such as:

• Deep learning: Training neural networks, image recognition, and natural language processing.

• Scientific simulations: Running complex simulations for physics, chemistry, and biology to accelerate research and analyze complex systems.

• Video rendering & image processing: Speeding up workflows in video editing, VFX, and digital imaging workflows for efficient graphics rendering.

• Data analytics: Handling large datasets for real-time analytics or batch processing.

• AI/ML experimentation: Running small model training, inference tasks, and AI experimentation environments, such as Jupyter Notebooks.

Factors to consider when choosing a cloud GPU provider

Selecting the right cloud GPU provider depends on your specific needs. Here are some key factors to evaluate:

• GPU instance types and specifications: Providers offer GPU models with varying performance characteristics. Compare options and assess their core computing strength, memory, bandwidth, and clock speed.

• Pricing models: Most cloud providers offer flexible pricing, including pay-as-you-go, per-second billing, and discounted spot instances. Align your budget accordingly to avoid overpaying for underutilized resources and efficient cloud cost optimization.

• Scalability and flexibility: Ensure your provider can accommodate your current and future needs. Auto-scaling features allow you to increase or decrease resources based on demand, saving costs and maintaining performance.

• Regional availability: Consider where the provider’s data centers are located. Geographically close servers reduce network latency and improve performance, critical for real-time applications, including those in industries such as finance and healthcare.

• Support and integration: Look for providers offering comprehensive integration with other cloud services and strong customer support. Smaller, specialized providers often excel in providing dedicated, personalized services for specific industries.

How do I choose a suitable platform and plan?

Modern GPU cloud providers, including hyperscalers like AWS, Google Cloud, and Azure, offer scalable, high-performance GPU solutions for applications involving machine learning, AI, and data analytics.

In contrast, providers like DigitalOcean, Linode, and OVHcloud focus on personalized solutions, dedicated support, and often cost-effective pricing, specifically for developers, data scientists, and fast-growing businesses. This section highlights the best cloud GPU platforms and the key differences between them to help make informed decisions.

1. DigitalOcean GPU Droplets

DigitalOcean offers high-performance GPU Droplets, virtual GPU machines that are available on demand and provide simplicity, affordability, and accessibility for developers. Unlike traditional cloud GPU platforms that require extensive configuration, DigitalOcean offers an easy-to-use experience with quick deployment times. Its GPU resources are designed for AI and machine learning tasks, particularly on use cases such as experimentation, single model inference, and image generation. DigitalOcean’s GPU Droplets integrate seamlessly with its broader ecosystem, offering services such as GPU Worker Nodes for DigitalOcean Kubernetes, Storage, Managed Databases, and App Platform, a Platform as a Service offiering, facilitating a holistic cloud experience.

Unlike hyperscalers like AWS, GCP, and Azure, which often have more complex billing structures, DigitalOcean offers straightforward, transparent options, making it an attractive choice for small- to medium-sized businesses or individual developers. DigitalOcean’s 1-Click Models powered by Hugging Face also makes it even simpler for anyone to deploy popular AI models with just one click.

2. Amazon Elastic Computing (EC2)

Amazon EC2 provides pre-configured templates for virtual machines with GPU-enabled instances for accelerated deep-learning computing. The Amazon EC2 instances also allow easy access to other Amazon web services, such as Elastic Graphics for attaching low-cost GPU options to instances, SageMaker for building, training, deploying, and enterprise scaling of ML models, the Virtual Private Cloud (VPC) for training and hosting workflows, and the Simple Storage Service (Amazon S3) for storing training data.

While AWS is comprehensive, its complexity is often cited as a barrier for new users. GPU configuration on EC2 can be time-consuming, and setup involves a learning curve due to the platform’s breadth. Hence, AWS is often more suitable for enterprises handling large-scale GPU workloads, particularly those committed to longer-term projects through reserved instances.

3. Google Compute Engine (GCE)

Google Compute Engine (GCE) offers high-performing GPU servers for computing-intensive workloads. GCE enables users to attach GPU instances to new and existing virtual machines. It is well-suited for workloads that demand high-performance resources, such as machine learning, 3D rendering, and AI model inference. Like AWS, GCP has a large global network.

GCP’s approach differs because GPU instances are available as an “add-on” to virtual machines (VMs). While this offers flexibility in pairing GPU resources with any VM, it also complicates the pricing structure, as VM and GPU costs must be combined for accurate calculations. This structure may appeal to users looking for fine-tuned configuration options.

4. Vast AI

Vast AI is a global marketplace for renting affordable GPUs, enabling businesses and individuals to perform high-performance computing tasks at lower costs. The platform’s unique model allows hosts to rent out their GPU hardware, giving clients access to various computing resources. Using Vast AI’s user-friendly web search interface, customers can browse for the best available deals based on their specific computing needs, which is also suitable for fluctuating workloads. Additionally, Vast AI offers simple interfaces for launching SSH sessions or using Jupyter instances, focusing on deep learning tasks.

One of Vast AI’s key features is its DLPerf function, which estimates deep learning tasks’ performance based on the chosen hardware configuration. This enables users to select the best-suited instances for their workload confidently. However, unlike many traditional cloud platforms, Vast AI does not offer remote desktop support, and its systems operate exclusively on Ubuntu.

5. Azure N Series

The Azure N-Series are GPU-enabled virtual machines for demanding workloads, including simulation, deep learning, graphics rendering, video editing, gaming, and remote visualization.

Azure’s integration with the broader Microsoft ecosystem, including Office 365 and Power BI services, simplifies data management and improves platform workflow consistency. Pricing models include pay-as-you-go, reserved, and spot instances and vary widely based on service type, usage, and selected pricing model. Costs can accumulate quickly depending on usage levels across Azure’s various services, leading to unexpected costs, especially if users are not fully aware of the pricing details of each service.

6. Oracle Cloud Infrastructure (OCI)

Oracle offers bare-metal and virtual machine GPU instances for fast, inexpensive, high-performance computing. Their GPU instances utilize low-latency networking, allowing users to host 500+ GPU clusters at scale and on demand. OCI emphasizes robust security features, including encryption and detailed access controls, ensuring that sensitive data is protected throughout the computational processes. Like IBM cloud, Oracle’s Bare-Metal instances allow customers to run workloads that need to run on non-virtualized environments. These instances can be used in the US, Germany, and UK regions.

7. IBM Cloud GPU

The IBM Cloud GPU provides flexible server-selection processes and seamless integration with the IBM Cloud architecture, APIs, and applications through a globally distributed network of data centers. IBM Cloud is ideal for hybrid cloud deployments and businesses that leverage IBM’s suite of software and services.

Unlike other providers like AWS, Azure, and GCP, IBM Cloud focuses on customized solutions for industries with specific regulatory needs, such as finance and healthcare. This makes it a solid choice for businesses that require computational power and rigorous data management and governance.

8. Lambda Labs Cloud

Lambda Labs offers cloud GPU instances for training and scaling deep learning models from a single machine to numerous virtual machines. Their virtual machines come pre-installed with major deep learning frameworks, CUDA drivers, and access to a dedicated Jupyter notebook. Connections to the instances are made via the web terminal in the cloud dashboard or directly via provided SSH keys. The instances support up to 10Gbps of inter-node bandwidth for distributed training and scalability across numerous GPUs, thereby reducing the time for model optimization.

9. Genesis Cloud

Genesis Cloud provides affordable, high-performance cloud GPUs for machine learning, visual processing, and other high-performance computing workloads. Its compute dashboard interface is simple, and its prices are comparatively cheaper than most platforms for similar resources. It also offers free credits on sign-up, discounts on long-term plans, a public API, and support for the PyTorch and TensorFlow frameworks.

10. Tencent Cloud

Tencent Cloud offers fast, stable, and elastic cloud GPU computing via various rendering instances that utilize GPUs to facilitate processes, including deep learning inference and training, video encoding and decoding, and scientific computing. Their services are available in Guangzhou, Shanghai, Beijing, and Singapore regions of Asia.

11. CoreWeave

CoreWeave provides configurable GPU instances for users with specific, resource-heavy workloads, such as machine learning, rendering, and simulations. However, potential downsides include hidden storage and networking costs and a lack of starter templates or images that might make the initial setup more complex for some users.

12. Linode

Linode offers a simplified GPU service for users who prioritize price-performance balance. Acquired by Akamai in 2022, Linode focuses on providing a straightforward cloud experience with GPU resources for machine learning, data analytics, and gaming. Unlike other providers with a broader GPU catalog, Linode offers a single GPU instance. However, the availability of GPU instances is restricted to certain compute regions.

13. OVH cloud

OVHcloud, which initially offered web hosting solutions, has recently expanded its offerings to include GPU-accelerated cloud services. OVHcloud’s GPU services are suitable for image recognition, situational analysis, and human interaction models. However, with no single GPU configuration and limited customization options for instances, it may be challenging for users who need flexibility and scalability in their cloud GPU environment compared to other providers.

Build with AI on DigitalOcean

AI is transforming how we work, and it’s worth experimenting with—whether exploring an AI side project or building a full-fledged AI business. DigitalOcean can help with our AI tools and support your AI endeavors.

Sign up for the early availability of GPU Droplets to supercharge your AI/ML workloads, and contact our team if you’re interested in dedicated H100s that are even more customizable. DigitalOcean GPU Droplets offer a simple, flexible, and affordable solution for your cutting-edge projects.

With GPU Droplets, you can:

• Reliably run training and inference on AI/ML models

• Process large data sets and complex neural networks for deep learning use cases

• Tackle high-performance computing (HPC) tasks with ease

Don’t miss out on this opportunity to scale your AI capabilities.

Spin up a GPU Droplet now and be among the first to experience the power of DigitalOcean GPU Droplets!