List of the Top 21 Cloud GPU Providers for Amazon Web Services (AWS) in 2026

RunPod offers a robust cloud infrastructure designed for effortless deployment and scalability of AI workloads utilizing GPU-powered pods. By providing a diverse selection of NVIDIA GPUs, including options like the A100 and H100, RunPod ensures that machine learning models can be trained and deployed with high performance and minimal latency. The platform prioritizes user-friendliness, enabling users to create pods within seconds and adjust their scale dynamically to align with demand. Additionally, features such as autoscaling, real-time analytics, and serverless scaling contribute to making RunPod an excellent choice for startups, academic institutions, and large enterprises that require a flexible, powerful, and cost-effective environment for AI development and inference. Furthermore, this adaptability allows users to focus on innovation rather than infrastructure management.

Compute with Hivenet is an efficient and budget-friendly cloud computing service that provides instant access to RTX 4090 GPUs. Tailored for tasks involving AI model training and other computation-heavy operations, Compute ensures secure, scalable, and dependable GPU resources at a significantly lower price than conventional providers. Equipped with real-time usage monitoring, an intuitive interface, and direct SSH access, Compute simplifies the process of launching and managing AI workloads, allowing developers and businesses to expedite their initiatives with advanced computing capabilities. Additionally, Compute is an integral part of the Hivenet ecosystem, which comprises a wide range of distributed cloud solutions focused on sustainability, security, and cost-effectiveness. By utilizing Hivenet, users can maximize the potential of their underused hardware to help build a robust and distributed cloud infrastructure that benefits all participants. This innovative approach not only enhances computational power but also fosters a collaborative environment for technology advancement.

The NVIDIA GPU-Optimized AMI is a specialized virtual machine image crafted to optimize performance for GPU-accelerated tasks in fields such as Machine Learning, Deep Learning, Data Science, and High-Performance Computing (HPC). With this AMI, users can swiftly set up a GPU-accelerated EC2 virtual machine instance, which comes equipped with a pre-configured Ubuntu operating system, GPU driver, Docker, and the NVIDIA container toolkit, making the setup process efficient and quick. This AMI also facilitates easy access to the NVIDIA NGC Catalog, a comprehensive resource for GPU-optimized software, which allows users to seamlessly pull and utilize performance-optimized, vetted, and NVIDIA-certified Docker containers. The NGC catalog provides free access to a wide array of containerized applications tailored for AI, Data Science, and HPC, in addition to pre-trained models, AI SDKs, and numerous other tools, empowering data scientists, developers, and researchers to focus on developing and deploying cutting-edge solutions. Furthermore, the GPU-optimized AMI is offered at no cost, with an additional option for users to acquire enterprise support through NVIDIA AI Enterprise services. For more information regarding support options associated with this AMI, please consult the 'Support Information' section below. Ultimately, using this AMI not only simplifies the setup of computational resources but also enhances overall productivity for projects demanding substantial processing power, thereby significantly accelerating the innovation cycle in these domains.

NVIDIA Brev provides developers with instant access to fully optimized GPU environments in the cloud, eliminating the typical setup challenges of AI and machine learning projects. Its flagship feature, Launchables, allows users to create and deploy preconfigured compute environments by selecting the necessary GPU resources, Docker container images, and uploading relevant project files like notebooks or repositories. This process requires minimal effort and can be completed within minutes, after which the Launchable can be shared publicly or privately via a simple link. NVIDIA offers a rich library of prebuilt Launchables equipped with the latest AI frameworks, microservices, and NVIDIA Blueprints, enabling users to jumpstart their projects with proven, scalable tools. The platform’s GPU sandbox provides a full virtual machine with support for CUDA, Python, and Jupyter Lab, accessible directly in the browser or through command-line interfaces. This seamless integration lets developers train, fine-tune, and deploy models efficiently, while also monitoring performance and usage in real time. NVIDIA Brev’s flexibility extends to port exposure and customization, accommodating diverse AI workflows. It supports collaboration by allowing easy sharing and visibility into resource consumption. By simplifying infrastructure management and accelerating development timelines, NVIDIA Brev helps startups and enterprises innovate faster in the AI space. Its robust environment is ideal for researchers, data scientists, and AI engineers seeking hassle-free GPU compute resources.

Moonglow enables you to seamlessly run your local notebooks on a remote GPU, making it as easy as changing your Python runtime. You can wave farewell to the complexities of managing SSH keys, installing various packages, and navigating the challenges of DevOps. With a diverse selection of GPUs available, including A40s, A100s, H100s, and more, there's a perfect match for every application. Managing your GPU resources directly from your IDE streamlines your workflow, leading to improved productivity. This integration not only simplifies the initial setup but also significantly boosts your computational power, allowing for more efficient processing of tasks. Embrace the future of remote computing with Moonglow and unlock new possibilities in your projects.

Amazon EC2 has introduced its latest G5 instances powered by NVIDIA GPUs, specifically engineered for demanding graphics and machine-learning applications. These instances significantly enhance performance, offering up to three times the speed for graphics-intensive operations and machine learning inference, with a remarkable 3.3 times increase in training efficiency compared to the earlier G4dn models. They are perfectly suited for environments that depend on high-quality real-time graphics, making them ideal for remote workstations, video rendering, and gaming experiences. In addition, G5 instances provide a robust and cost-efficient platform for machine learning practitioners, facilitating the training and deployment of larger and more intricate models in fields like natural language processing, computer vision, and recommendation systems. They not only achieve graphics performance that is three times higher than G4dn instances but also feature a 40% enhancement in price performance, making them an attractive option for users. Moreover, G5 instances are equipped with the highest number of ray tracing cores among all GPU-based EC2 offerings, significantly improving their ability to manage sophisticated graphic rendering tasks. This combination of features establishes G5 instances as a highly appealing option for developers and enterprises eager to utilize advanced technology in their endeavors, ultimately driving innovation and efficiency in various industries.

Amazon's EC2 P4d instances are designed to deliver outstanding performance for machine learning training and high-performance computing applications within the cloud. Featuring NVIDIA A100 Tensor Core GPUs, these instances are capable of achieving impressive throughput while offering low-latency networking that supports a remarkable 400 Gbps instance networking speed. P4d instances serve as a budget-friendly option, allowing businesses to realize savings of up to 60% during the training of machine learning models and providing an average performance boost of 2.5 times for deep learning tasks when compared to previous P3 and P3dn versions. They are often utilized in large configurations known as Amazon EC2 UltraClusters, which effectively combine high-performance computing, networking, and storage capabilities. This architecture enables users to scale their operations from just a few to thousands of NVIDIA A100 GPUs, tailored to their particular project needs. A diverse group of users, such as researchers, data scientists, and software developers, can take advantage of P4d instances for a variety of machine learning tasks including natural language processing, object detection and classification, as well as recommendation systems. Additionally, these instances are well-suited for high-performance computing endeavors like drug discovery and intricate data analyses. The blend of remarkable performance and the ability to scale effectively makes P4d instances an exceptional option for addressing a wide range of computational challenges, ensuring that users can meet their evolving needs efficiently.

AceCloud functions as a comprehensive solution for public cloud and cybersecurity, designed to equip businesses with a versatile, secure, and efficient infrastructure. Its public cloud services encompass a variety of computing alternatives tailored to meet diverse requirements, including options for RAM-intensive and CPU-intensive tasks, as well as spot instances, and advanced GPU functionalities featuring NVIDIA models like A2, A30, A100, L4, L40S, RTX A6000, RTX 8000, and H100. By offering Infrastructure as a Service (IaaS), users can easily implement virtual machines, storage options, and networking resources according to their needs. The storage capabilities comprise both object and block storage, in addition to volume snapshots and instance backups, all meticulously designed to uphold data integrity while ensuring seamless access. Furthermore, AceCloud offers managed Kubernetes services for streamlined container orchestration and supports private cloud configurations, providing choices such as fully managed cloud solutions, one-time deployments, hosted private clouds, and virtual private servers. This all-encompassing strategy allows organizations to enhance their cloud experience significantly while improving security measures and performance levels. Ultimately, AceCloud aims to empower businesses with the tools they need to thrive in a digital-first world.

Hathora is a cutting-edge platform designed for orchestrating real-time computing, specifically aimed at enhancing the performance and reducing latency for applications by integrating CPUs and GPUs across diverse environments, such as cloud, edge, and on-site infrastructure. It provides comprehensive orchestration features that allow teams to effectively oversee workloads not just in their own data centers, but also across Hathora’s vast worldwide network, which includes intelligent load balancing, automatic spill-over, and a remarkable built-in uptime guarantee of 99.9%. The platform’s edge-compute capabilities maintain latency below 50 milliseconds globally by routing workloads to the closest geographical locations, and its support for containers enables effortless deployment of Docker-based applications—be it for GPU-accelerated inference, gaming servers, or batch processing—without requiring any architectural changes. Additionally, the platform includes data-sovereignty features that enable organizations to impose regional deployment restrictions and meet compliance mandates. With a wide range of applications, such as real-time inference and global game server management, build farms, and elastic “metal” availability, all can be accessed via a unified API and thorough global observability dashboards. Moreover, Hathora is engineered for rapid scaling, thus allowing it to handle a growing number of workloads in response to increasing demand, making it an indispensable tool for modern computing needs. This scalability is crucial for organizations looking to adapt swiftly to changing market conditions and expanding operational requirements.

Enable both development and operations teams to harness the self-service tools and automation they desire while achieving a careful equilibrium of standardization and governance required by the organization. Utilize Git for centralized management and definition of configurations across clusters, incorporating essential elements such as security policies and software upgrades, which include service mesh, ingress controllers, monitoring, logging, and solutions for backup and recovery. The lifecycle management of blueprints and add-ons can be effortlessly executed for both new and existing clusters from a unified location. Furthermore, these blueprints can be distributed among different teams, promoting centralized control over the add-ons deployed throughout the organization. In fast-paced environments that necessitate swift development cycles, users can swiftly move from a Git push to an updated application on managed clusters within seconds, with the capability to execute this process more than 100 times a day. This method is particularly beneficial in development settings characterized by frequent changes, thereby promoting a more agile operational workflow. By optimizing these processes, organizations can greatly improve their efficiency and adaptability, resulting in a more responsive operational structure that can meet evolving demands. Ultimately, this enhances collaboration and fosters innovation across all teams within the organization.

The NVIDIA DGX Cloud offers a robust AI infrastructure as a service, streamlining the process of deploying extensive AI models and fostering rapid innovation. This platform presents a wide array of tools tailored for machine learning, deep learning, and high-performance computing, allowing enterprises to execute their AI tasks effectively in the cloud. Additionally, its effortless integration with leading cloud services provides the scalability, performance, and adaptability required to address intricate AI challenges, while also removing the burdens associated with on-site hardware management. This makes it an invaluable resource for organizations looking to harness the power of AI without the typical constraints of physical infrastructure.

Fluidstack is an advanced AI infrastructure platform designed to deliver high-performance compute resources for large-scale machine learning and AI workloads. It provides dedicated GPU clusters that are fully isolated, ensuring consistent performance and security for enterprise-grade applications. The platform is built for speed, allowing users to deploy and scale infrastructure rapidly to meet demanding workloads. Fluidstack includes Atlas OS, a bare-metal operating system that enables efficient provisioning, orchestration, and control of compute resources. It also features Lighthouse, a monitoring and optimization system that detects issues early and maintains workload performance. The platform is designed to support a wide range of use cases, including AI training, inference, and data processing. Fluidstack emphasizes security with single-tenant environments and compliance with industry standards such as GDPR, SOC 2, and ISO certifications. It provides direct human support from engineers, ensuring fast response times and reliable operations. The infrastructure is built to scale, allowing organizations to handle increasing computational demands. Fluidstack is used by leading AI companies, research institutions, and government organizations. It offers flexibility in deployment, supporting global infrastructure needs. The platform reduces the complexity of managing large-scale compute environments. Overall, Fluidstack delivers a powerful, secure, and scalable solution for AI infrastructure and high-performance computing.

Amazon's EC2 P5 instances, equipped with NVIDIA H100 Tensor Core GPUs, alongside the P5e and P5en variants utilizing NVIDIA H200 Tensor Core GPUs, deliver exceptional capabilities for deep learning and high-performance computing endeavors. These instances can boost your solution development speed by up to four times compared to earlier GPU-based EC2 offerings, while also reducing the costs linked to machine learning model training by as much as 40%. This remarkable efficiency accelerates solution iterations, leading to a quicker time-to-market. Specifically designed for training and deploying cutting-edge large language models and diffusion models, the P5 series is indispensable for tackling the most complex generative AI challenges. Such applications span a diverse array of functionalities, including question-answering, code generation, image and video synthesis, and speech recognition. In addition, these instances are adept at scaling to accommodate demanding high-performance computing tasks, such as those found in pharmaceutical research and discovery, thereby broadening their applicability across numerous industries. Ultimately, Amazon EC2's P5 series not only amplifies computational capabilities but also fosters innovation across a variety of sectors, enabling businesses to stay ahead of the curve in technological advancements. The integration of these advanced instances can transform how organizations approach their most critical computational challenges.

Amazon EC2 Capacity Blocks are designed for machine learning, allowing users to secure accelerated compute instances within Amazon EC2 UltraClusters that are specifically optimized for their ML tasks. This service encompasses a variety of instance types, including P5en, P5e, P5, and P4d, which leverage NVIDIA's H200, H100, and A100 Tensor Core GPUs, along with Trn2 and Trn1 instances that utilize AWS Trainium. Users can reserve these instances for periods of up to six months, with flexible cluster sizes ranging from a single instance to as many as 64 instances, accommodating a maximum of 512 GPUs or 1,024 Trainium chips to meet a wide array of machine learning needs. Reservations can be conveniently made as much as eight weeks in advance. By employing Amazon EC2 UltraClusters, Capacity Blocks deliver a low-latency and high-throughput network, significantly improving the efficiency of distributed training processes. This setup ensures dependable access to superior computing resources, empowering you to plan your machine learning projects strategically, run experiments, develop prototypes, and manage anticipated surges in demand for machine learning applications. Ultimately, this service is crafted to enhance the machine learning workflow while promoting both scalability and performance, thereby allowing users to focus more on innovation and less on infrastructure. It stands as a pivotal tool for organizations looking to advance their machine learning initiatives effectively.

Amazon EC2 UltraClusters provide the ability to scale up to thousands of GPUs or specialized machine learning accelerators such as AWS Trainium, offering immediate access to performance comparable to supercomputing. They democratize advanced computing for developers working in machine learning, generative AI, and high-performance computing through a straightforward pay-as-you-go model, which removes the burden of setup and maintenance costs. These UltraClusters consist of numerous accelerated EC2 instances that are optimally organized within a particular AWS Availability Zone and interconnected through Elastic Fabric Adapter (EFA) networking over a petabit-scale nonblocking network. This cutting-edge arrangement ensures enhanced networking performance and includes access to Amazon FSx for Lustre, a fully managed shared storage system that is based on a high-performance parallel file system, enabling the efficient processing of large datasets with latencies in the sub-millisecond range. Additionally, EC2 UltraClusters support greater scalability for distributed machine learning training and seamlessly integrated high-performance computing tasks, thereby significantly reducing the time required for training. This infrastructure not only meets but exceeds the requirements for the most demanding computational applications, making it an essential tool for modern developers. With such capabilities, organizations can tackle complex challenges with confidence and efficiency.

The Elastic Fabric Adapter (EFA) is a dedicated network interface tailored for Amazon EC2 instances, aimed at facilitating applications that require extensive communication between nodes when operating at large scales on AWS. By employing a unique operating system (OS), EFA bypasses conventional hardware interfaces, greatly enhancing communication efficiency among instances, which is vital for the scalability of these applications. This technology empowers High-Performance Computing (HPC) applications that utilize the Message Passing Interface (MPI) and Machine Learning (ML) applications that depend on the NVIDIA Collective Communications Library (NCCL), enabling them to seamlessly scale to thousands of CPUs or GPUs. As a result, users can achieve performance benchmarks comparable to those of traditional on-premises HPC clusters while enjoying the flexible, on-demand capabilities offered by the AWS cloud environment. This feature serves as an optional enhancement for EC2 networking and can be enabled on any compatible EC2 instance without additional costs. Furthermore, EFA integrates smoothly with a majority of commonly used interfaces, APIs, and libraries designed for inter-node communications, making it a flexible option for developers in various fields. The ability to scale applications while preserving high performance is increasingly essential in today’s data-driven world, as organizations strive to meet ever-growing computational demands. Such advancements not only enhance operational efficiency but also drive innovation across numerous industries.

SQream is a cutting-edge data analytics solution that harnesses the power of GPU technology, enabling organizations to swiftly analyze vast and complex datasets with exceptional efficiency. By leveraging the robust capabilities of NVIDIA's GPUs, SQream executes intricate SQL queries on large datasets in a fraction of the time that traditional methods require, transforming lengthy processes into mere minutes. The platform offers dynamic scalability, allowing businesses to effortlessly expand their data operations as they evolve, all while maintaining uninterrupted analytics workflows. With its adaptable architecture, SQream meets various deployment requirements, ensuring it can fit into different infrastructure setups. It serves multiple industries, including telecommunications, manufacturing, finance, advertising, and retail, providing data teams with the necessary tools to derive meaningful insights, enhance data accessibility, and foster innovation, thus achieving significant cost reductions. This enhancement of operational efficiency not only facilitates better decision-making but also strengthens an organization’s competitive stance in the increasingly data-centric landscape. By empowering teams to focus on strategic initiatives, SQream ultimately helps drive growth and success in a rapidly changing market.

Cake AI functions as a comprehensive infrastructure platform that enables teams to effortlessly develop and deploy AI applications by leveraging a wide array of pre-integrated open source components, promoting transparency and governance throughout the process. It provides a meticulously assembled suite of high-quality commercial and open-source AI tools, complete with ready-to-use integrations that streamline the deployment of AI applications into production without hassle. The platform features dynamic autoscaling, robust security measures including role-based access controls and encryption, and sophisticated monitoring capabilities, all while maintaining an adaptable infrastructure compatible with diverse environments, from Kubernetes clusters to cloud services like AWS. Furthermore, its data layer includes vital tools for data ingestion, transformation, and analytics, utilizing technologies such as Airflow, DBT, Prefect, Metabase, and Superset to optimize data management practices. To facilitate effective AI operations, Cake AI integrates seamlessly with model catalogs such as Hugging Face and supports a variety of workflows through tools like LangChain and LlamaIndex, enabling teams to tailor their processes with ease. This extensive ecosystem not only enhances organizational capabilities but also fosters innovation, allowing for the rapid deployment of AI solutions with increased efficiency and accuracy. Ultimately, Cake AI equips teams with the resources they need to navigate the complexities of AI development successfully.

Amazon EC2 G4 instances are meticulously engineered to boost the efficiency of machine learning inference and applications that demand superior graphics performance. Users have the option to choose between NVIDIA T4 GPUs (G4dn) and AMD Radeon Pro V520 GPUs (G4ad) based on their specific needs. The G4dn instances merge NVIDIA T4 GPUs with custom Intel Cascade Lake CPUs, providing an ideal combination of processing power, memory, and networking capacity. These instances excel in various applications, including the deployment of machine learning models, video transcoding, game streaming, and graphic rendering. Conversely, the G4ad instances, which feature AMD Radeon Pro V520 GPUs and 2nd-generation AMD EPYC processors, present a cost-effective solution for managing graphics-heavy tasks. Both types of instances take advantage of Amazon Elastic Inference, enabling users to incorporate affordable GPU-enhanced inference acceleration to Amazon EC2, which helps reduce expenses tied to deep learning inference. Available in multiple sizes, these instances are tailored to accommodate varying performance needs and they integrate smoothly with a multitude of AWS services, such as Amazon SageMaker, Amazon ECS, and Amazon EKS. Furthermore, this adaptability positions G4 instances as a highly appealing option for businesses aiming to harness the power of cloud-based machine learning and graphics processing workflows, thereby facilitating innovation and efficiency.

Shadeform functions as an all-encompassing GPU cloud marketplace that simplifies the tasks of discovering, comparing, launching, and managing on-demand GPU instances from multiple cloud providers through one cohesive platform, consolidated console, and API. This integration supports the development, training, and deployment of AI models while alleviating the complications associated with handling numerous accounts or maneuvering through different provider interfaces. Users benefit from the ability to access current pricing and availability for GPUs across various clouds, launch instances either within their own cloud accounts or via Shadeform's managed accounts, and efficiently manage a multi-cloud ecosystem from a single, centralized location using standardized tools such as curl, Python, or Terraform. By consolidating information on GPU capacity and pricing, teams can optimize their computing costs effectively, deploy containerized workloads with consistent interfaces, centralize billing and account management, and reduce vendor-specific challenges through a unified API that supports a range of providers. Furthermore, Shadeform improves the user experience with additional features such as scheduling and automated resource provisioning, which guarantee that users can obtain essential resources as they become available while ensuring operational flexibility. This approach not only streamlines processes but also enhances collaboration among teams working on AI projects, allowing them to focus more on innovation rather than logistical hurdles.

OpenGPU Network is a groundbreaking decentralized platform designed for GPU computing that connects users requiring substantial processing capabilities with a wide range of independent GPU providers worldwide. This cutting-edge system supports various intensive operations, including AI inference, machine learning training, and rendering tasks, by utilizing distributed resources instead of depending on conventional centralized cloud services. It serves as an advanced routing solution that intelligently matches workloads with available GPU resources across the globe, allowing for instant task execution without the complexities of managing infrastructure or facing regional constraints, queue issues, or provisioning delays. By aggregating resources from data centers, cloud services, and personal devices, OpenGPU addresses the growing gap between the escalating demand for GPUs and the fragmented, underutilized supply available in the market. The platform runs on a blockchain framework that not only oversees task coordination and verifies results but also guarantees fair distribution of rewards, creating a trustless ecosystem for its users. Consequently, OpenGPU significantly improves access to GPU computing while also encouraging the efficient use of computational resources on a global scale, ultimately leading to a more connected and responsive computing environment. This innovative approach enables individuals and businesses alike to tap into the vast potential of GPU technology without the burden of traditional limitations.