Alternatives to Amazon EC2 UltraClusters

CoreWeave stands out as a cloud infrastructure service that focuses on GPU-centric computing solutions specifically designed for artificial intelligence applications. Their platform delivers scalable, high-performance GPU clusters that enhance both training and inference processes for AI models, catering to sectors such as machine learning, visual effects, and high-performance computing. In addition to robust GPU capabilities, CoreWeave offers adaptable storage, networking, and managed services that empower AI-focused enterprises, emphasizing reliability, cost-effectiveness, and top-tier security measures. This versatile platform is widely adopted by AI research facilities, labs, and commercial entities aiming to expedite their advancements in artificial intelligence technology. By providing an infrastructure that meets the specific demands of AI workloads, CoreWeave plays a crucial role in driving innovation across various industries.

Amazon Elastic Compute Cloud (Amazon EC2) is a cloud service that offers flexible and secure computing capabilities. Its primary aim is to simplify large-scale cloud computing for developers. With an easy-to-use web service interface, Amazon EC2 allows users to quickly obtain and configure computing resources with ease. Users gain full control over their computing power while utilizing Amazon’s established computing framework. The service offers an extensive range of compute options, networking capabilities (up to 400 Gbps), and tailored storage solutions that enhance price and performance specifically for machine learning initiatives. Developers can create, test, and deploy macOS workloads on demand. Furthermore, users can scale their capacity dynamically as requirements change, all while benefiting from AWS's pay-as-you-go pricing model. This infrastructure enables rapid access to the necessary resources for high-performance computing (HPC) applications, resulting in enhanced speed and cost efficiency. In essence, Amazon EC2 ensures a secure, dependable, and high-performance computing environment that caters to the diverse demands of modern businesses. Overall, it stands out as a versatile solution for various computing needs across different industries.

Amazon EC2 Capacity Blocks for Machine Learning allow users to secure accelerated computing instances within Amazon EC2 UltraClusters specifically for their machine learning tasks. This service encompasses a variety of instance types, including Amazon EC2 P5en, P5e, P5, and P4d, which utilize NVIDIA H200, H100, and A100 Tensor Core GPUs, along with Trn2 and Trn1 instances that leverage AWS Trainium. Users can reserve these instances for periods of up to six months, with cluster sizes ranging from a single instance to 64 instances, translating to a maximum of 512 GPUs or 1,024 Trainium chips, thus providing ample flexibility to accommodate diverse machine learning workloads. Additionally, reservations can be arranged as much as eight weeks ahead of time. By operating within Amazon EC2 UltraClusters, Capacity Blocks facilitate low-latency and high-throughput network connectivity, which is essential for efficient distributed training processes. This configuration guarantees reliable access to high-performance computing resources, empowering you to confidently plan your machine learning projects, conduct experiments, develop prototypes, and effectively handle anticipated increases in demand for machine learning applications. Furthermore, this strategic approach not only enhances productivity but also optimizes resource utilization for varying project scales.

Amazon EC2 P4d instances are designed for optimal performance in machine learning training and high-performance computing (HPC) applications within the cloud environment. Equipped with NVIDIA A100 Tensor Core GPUs, these instances provide exceptional throughput and low-latency networking capabilities, boasting 400 Gbps instance networking. P4d instances are remarkably cost-effective, offering up to a 60% reduction in expenses for training machine learning models, while also delivering an impressive 2.5 times better performance for deep learning tasks compared to the older P3 and P3dn models. They are deployed within expansive clusters known as Amazon EC2 UltraClusters, which allow for the seamless integration of high-performance computing, networking, and storage resources. This flexibility enables users to scale their operations from a handful to thousands of NVIDIA A100 GPUs depending on their specific project requirements. Researchers, data scientists, and developers can leverage P4d instances to train machine learning models for diverse applications, including natural language processing, object detection and classification, and recommendation systems, in addition to executing HPC tasks such as pharmaceutical discovery and other complex computations. These capabilities collectively empower teams to innovate and accelerate their projects with greater efficiency and effectiveness.

The Elastic Fabric Adapter (EFA) serves as a specialized network interface for Amazon EC2 instances, allowing users to efficiently run applications that demand high inter-node communication at scale within the AWS environment. By utilizing a custom-designed operating system (OS) that circumvents traditional hardware interfaces, EFA significantly boosts the performance of communications between instances, which is essential for effectively scaling such applications. This technology facilitates the scaling of High-Performance Computing (HPC) applications that utilize the Message Passing Interface (MPI) and Machine Learning (ML) applications that rely on the NVIDIA Collective Communications Library (NCCL) to thousands of CPUs or GPUs. Consequently, users can achieve the same high application performance found in on-premises HPC clusters while benefiting from the flexible and on-demand nature of the AWS cloud infrastructure. EFA can be activated as an optional feature for EC2 networking without incurring any extra charges, making it accessible for a wide range of use cases. Additionally, it seamlessly integrates with the most popular interfaces, APIs, and libraries for inter-node communication needs, enhancing its utility for diverse applications.

Amazon EC2 Trn2 instances, equipped with AWS Trainium2 chips, are specifically designed to deliver exceptional performance in the training of generative AI models, such as large language and diffusion models. Users can experience cost savings of up to 50% in training expenses compared to other Amazon EC2 instances. These Trn2 instances can accommodate as many as 16 Trainium2 accelerators, boasting an impressive compute power of up to 3 petaflops using FP16/BF16 and 512 GB of high-bandwidth memory. For enhanced data and model parallelism, they are built with NeuronLink, a high-speed, nonblocking interconnect, and offer a substantial network bandwidth of up to 1600 Gbps via the second-generation Elastic Fabric Adapter (EFAv2). Trn2 instances are part of EC2 UltraClusters, which allow for scaling up to 30,000 interconnected Trainium2 chips within a nonblocking petabit-scale network, achieving a remarkable 6 exaflops of compute capability. Additionally, the AWS Neuron SDK provides seamless integration with widely used machine learning frameworks, including PyTorch and TensorFlow, making these instances a powerful choice for developers and researchers alike. This combination of cutting-edge technology and cost efficiency positions Trn2 instances as a leading option in the realm of high-performance deep learning.

AWS High Performance Computing (HPC) services enable users to run extensive simulations and deep learning tasks in the cloud, offering nearly limitless computing power, advanced file systems, and high-speed networking capabilities. This comprehensive set of services fosters innovation by providing a diverse array of cloud-based resources, such as machine learning and analytics tools, which facilitate swift design and evaluation of new products. Users can achieve peak operational efficiency thanks to the on-demand nature of these computing resources, allowing them to concentrate on intricate problem-solving without the limitations of conventional infrastructure. AWS HPC offerings feature the Elastic Fabric Adapter (EFA) for optimized low-latency and high-bandwidth networking, AWS Batch for efficient scaling of computing tasks, AWS ParallelCluster for easy cluster setup, and Amazon FSx for delivering high-performance file systems. Collectively, these services create a flexible and scalable ecosystem that is well-suited for a variety of HPC workloads, empowering organizations to push the boundaries of what’s possible in their respective fields. As a result, users can experience greatly enhanced performance and productivity in their computational endeavors.

AWS Parallel Computing Service (AWS PCS) is a fully managed service designed to facilitate the execution and scaling of high-performance computing tasks while also aiding in the development of scientific and engineering models using Slurm on AWS. This service allows users to create comprehensive and adaptable environments that seamlessly combine computing, storage, networking, and visualization tools, enabling them to concentrate on their research and innovative projects without the hassle of managing the underlying infrastructure. With features like automated updates and integrated observability, AWS PCS significantly improves the operations and upkeep of computing clusters. Users can easily construct and launch scalable, dependable, and secure HPC clusters via the AWS Management Console, AWS Command Line Interface (AWS CLI), or AWS SDK. The versatility of the service supports a wide range of applications, including tightly coupled workloads such as computer-aided engineering, high-throughput computing for tasks like genomics analysis, GPU-accelerated computing, and specialized silicon solutions like AWS Trainium and AWS Inferentia. Overall, AWS PCS empowers researchers and engineers to harness advanced computing capabilities without needing to worry about the complexities of infrastructure setup and maintenance.

QumulusAI provides unparalleled supercomputing capabilities, merging scalable high-performance computing (HPC) with autonomous data centers to eliminate bottlenecks and propel the advancement of AI. By democratizing access to AI supercomputing, QumulusAI dismantles the limitations imposed by traditional HPC and offers the scalable, high-performance solutions that modern AI applications require now and in the future. With no virtualization latency and no disruptive neighbors, users gain dedicated, direct access to AI servers that are fine-tuned with the latest NVIDIA GPUs (H200) and cutting-edge Intel/AMD CPUs. Unlike legacy providers that utilize a generic approach, QumulusAI customizes HPC infrastructure to align specifically with your unique workloads. Our partnership extends through every phase—from design and deployment to continuous optimization—ensuring that your AI initiatives receive precisely what they need at every stage of development. We maintain ownership of the entire technology stack, which translates to superior performance, enhanced control, and more predictable expenses compared to other providers that rely on third-party collaborations. This comprehensive approach positions QumulusAI as a leader in the supercomputing space, ready to adapt to the evolving demands of your projects.

The Nimbix Supercomputing Suite offers a diverse and secure range of high-performance computing (HPC) solutions available as a service. This innovative model enables users to tap into a comprehensive array of HPC and supercomputing resources, spanning from hardware options to bare metal-as-a-service, facilitating the widespread availability of advanced computing capabilities across both public and private data centers. Through the Nimbix Supercomputing Suite, users gain access to the HyperHub Application Marketplace, which features an extensive selection of over 1,000 applications and workflows designed for high performance. By utilizing dedicated BullSequana HPC servers as bare metal-as-a-service, clients can enjoy superior infrastructure along with the flexibility of on-demand scalability, convenience, and agility. Additionally, the federated supercomputing-as-a-service provides a centralized service console, enabling efficient management of all computing zones and regions within a public or private HPC, AI, and supercomputing federation, thereby streamlining operations and enhancing productivity. This comprehensive suite empowers organizations to drive innovation and optimize performance across various computational tasks.

WhiteFiber operates as a comprehensive AI infrastructure platform that specializes in delivering high-performance GPU cloud services and HPC colocation solutions specifically designed for AI and machine learning applications. Their cloud services are meticulously engineered for tasks involving machine learning, expansive language models, and deep learning, equipped with advanced NVIDIA H200, B200, and GB200 GPUs alongside ultra-fast Ethernet and InfiniBand networking, achieving an impressive GPU fabric bandwidth of up to 3.2 Tb/s. Supporting a broad range of scaling capabilities from hundreds to tens of thousands of GPUs, WhiteFiber offers various deployment alternatives such as bare metal, containerized applications, and virtualized setups. The platform guarantees enterprise-level support and service level agreements (SLAs), incorporating unique cluster management, orchestration, and observability tools. Additionally, WhiteFiber’s data centers are strategically optimized for AI and HPC colocation, featuring high-density power, direct liquid cooling systems, and rapid deployment options, while also ensuring redundancy and scalability through cross-data center dark fiber connectivity. With a commitment to innovation and reliability, WhiteFiber stands out as a key player in the AI infrastructure ecosystem.

Amazon's Elastic Compute Cloud (EC2) offers P5 instances that utilize NVIDIA H100 Tensor Core GPUs, alongside P5e and P5en instances featuring NVIDIA H200 Tensor Core GPUs, ensuring unmatched performance for deep learning and high-performance computing tasks. With these advanced instances, you can reduce the time to achieve results by as much as four times compared to earlier GPU-based EC2 offerings, while also cutting ML model training costs by up to 40%. This capability enables faster iteration on solutions, allowing businesses to reach the market more efficiently. P5, P5e, and P5en instances are ideal for training and deploying sophisticated large language models and diffusion models that drive the most intensive generative AI applications, which encompass areas like question-answering, code generation, video and image creation, and speech recognition. Furthermore, these instances can also support large-scale deployment of high-performance computing applications, facilitating advancements in fields such as pharmaceutical discovery, ultimately transforming how research and development are conducted in the industry.

Bright Cluster Manager offers a variety of machine learning frameworks including Torch, Tensorflow and Tensorflow to simplify your deep-learning projects. Bright offers a selection the most popular Machine Learning libraries that can be used to access datasets. These include MLPython and NVIDIA CUDA Deep Neural Network Library (cuDNN), Deep Learning GPU Trainer System (DIGITS), CaffeOnSpark (a Spark package that allows deep learning), and MLPython. Bright makes it easy to find, configure, and deploy all the necessary components to run these deep learning libraries and frameworks. There are over 400MB of Python modules to support machine learning packages. We also include the NVIDIA hardware drivers and CUDA (parallel computer platform API) drivers, CUB(CUDA building blocks), NCCL (library standard collective communication routines).

HPE Performance Cluster Manager (HPCM) offers a cohesive system management solution tailored for Linux®-based high-performance computing (HPC) clusters. This software facilitates comprehensive provisioning, management, and monitoring capabilities for clusters that can extend to Exascale-sized supercomputers. HPCM streamlines the initial setup from bare-metal, provides extensive hardware monitoring and management options, oversees image management, handles software updates, manages power efficiently, and ensures overall cluster health. Moreover, it simplifies the scaling process for HPC clusters and integrates seamlessly with numerous third-party tools to enhance workload management. By employing HPE Performance Cluster Manager, organizations can significantly reduce the administrative burden associated with HPC systems, ultimately leading to lowered total ownership costs and enhanced productivity, all while maximizing the return on their hardware investments. As a result, HPCM not only fosters operational efficiency but also supports organizations in achieving their computational goals effectively.

Lambda is building the cloud designed for superintelligence by delivering integrated AI factories that combine dense power, liquid cooling, and next-generation NVIDIA compute into turnkey systems. Its platform supports everything from rapid prototyping on single GPU instances to running massive distributed training jobs across full GB300 NVL72 superclusters. With 1-Click Clusters™, teams can instantly deploy optimized B200 and H100 clusters prepared for production-grade AI workloads. Lambda’s shared-nothing, single-tenant security model ensures that sensitive data and models remain isolated at the hardware level. SOC 2 Type II certification and caged-cluster options make it suitable for mission-critical use cases in enterprise, government, and research. NVIDIA’s latest chips—including the GB300, HGX B300, HGX B200, and H200—give organizations unprecedented computational throughput. Lambda’s infrastructure is built to scale with ambition, capable of supporting workloads ranging from inference to full-scale training of foundation models. For AI teams racing toward the next frontier, Lambda provides the power, security, and reliability needed to push boundaries.

The Lustre file system is a parallel, open-source file system designed to cater to the demanding requirements of high-performance computing (HPC) simulation environments often found in leadership class facilities. Whether you are part of our vibrant development community or evaluating Lustre as a potential parallel file system option, you will find extensive resources and support available to aid you. Offering a POSIX-compliant interface, the Lustre file system can efficiently scale to accommodate thousands of clients, manage petabytes of data, and deliver impressive I/O bandwidths exceeding hundreds of gigabytes per second. Its architecture includes essential components such as Metadata Servers (MDS), Metadata Targets (MDT), Object Storage Servers (OSS), Object Server Targets (OST), and Lustre clients. Lustre is specifically engineered to establish a unified, global POSIX-compliant namespace suited for massive computing infrastructures, including some of the largest supercomputing platforms in existence. With its capability to handle hundreds of petabytes of data storage, Lustre stands out as a robust solution for organizations looking to manage extensive datasets effectively. Its versatility and scalability make it a preferable choice for a wide range of applications in scientific research and data-intensive computing.

Amazon FSx for Lustre is a fully managed service designed to deliver high-performance and scalable storage solutions tailored for compute-heavy tasks. Based on the open-source Lustre file system, it provides remarkably low latencies, exceptional throughput that can reach hundreds of gigabytes per second, and millions of input/output operations per second, making it particularly suited for use cases such as machine learning, high-performance computing, video processing, and financial analysis. This service conveniently integrates with Amazon S3, allowing users to connect their file systems directly to S3 buckets. Such integration facilitates seamless access and manipulation of S3 data through a high-performance file system, with the added capability to import and export data between FSx for Lustre and S3 efficiently. FSx for Lustre accommodates various deployment needs, offering options such as scratch file systems for temporary storage solutions and persistent file systems for long-term data retention. Additionally, it provides both SSD and HDD storage types, enabling users to tailor their storage choices to optimize performance and cost based on their specific workload demands. This flexibility makes it an attractive choice for a wide range of industries that require robust storage solutions.

TrinityX is a cluster management solution that is open source and developed by ClusterVision, aimed at ensuring continuous monitoring for environments focused on High-Performance Computing (HPC) and Artificial Intelligence (AI). It delivers a robust support system that adheres to service level agreements (SLAs), enabling researchers to concentrate on their work without the burden of managing intricate technologies such as Linux, SLURM, CUDA, InfiniBand, Lustre, and Open OnDemand. By providing an easy-to-use interface, TrinityX simplifies the process of cluster setup, guiding users through each phase to configure clusters for various applications including container orchestration, conventional HPC, and InfiniBand/RDMA configurations. Utilizing the BitTorrent protocol, it facilitates the swift deployment of AI and HPC nodes, allowing for configurations to be completed in mere minutes. Additionally, the platform boasts a detailed dashboard that presents real-time data on cluster performance metrics, resource usage, and workload distribution, which helps users quickly identify potential issues and optimize resource distribution effectively. This empowers teams to make informed decisions that enhance productivity and operational efficiency within their computational environments.

Amazon S3 Express One Zone is designed as a high-performance storage class that operates within a single Availability Zone, ensuring reliable access to frequently used data and meeting the demands of latency-sensitive applications with single-digit millisecond response times. It boasts data retrieval speeds that can be up to 10 times quicker, alongside request costs that can be reduced by as much as 50% compared to the S3 Standard class. Users have the flexibility to choose a particular AWS Availability Zone in an AWS Region for their data, which enables the co-location of storage and computing resources, ultimately enhancing performance and reducing compute expenses while expediting workloads. The data is managed within a specialized bucket type known as an S3 directory bucket, which can handle hundreds of thousands of requests every second efficiently. Furthermore, S3 Express One Zone can seamlessly integrate with services like Amazon SageMaker Model Training, Amazon Athena, Amazon EMR, and AWS Glue Data Catalog, thereby speeding up both machine learning and analytical tasks. This combination of features makes S3 Express One Zone an attractive option for businesses looking to optimize their data management and processing capabilities.

TotalView debugging software offers essential tools designed to expedite the debugging, analysis, and scaling of high-performance computing (HPC) applications. This software adeptly handles highly dynamic, parallel, and multicore applications that can operate on a wide range of hardware, from personal computers to powerful supercomputers. By utilizing TotalView, developers can enhance the efficiency of HPC development, improve the quality of their code, and reduce the time needed to bring products to market through its advanced capabilities for rapid fault isolation, superior memory optimization, and dynamic visualization. It allows users to debug thousands of threads and processes simultaneously, making it an ideal solution for multicore and parallel computing environments. TotalView equips developers with an unparalleled set of tools that provide detailed control over thread execution and processes, while also offering extensive insights into program states and data, ensuring a smoother debugging experience. With these comprehensive features, TotalView stands out as a vital resource for those engaged in high-performance computing.

Develop a hybrid storage solution that seamlessly integrates with your current network-attached storage (NAS) and Azure Blob Storage. This on-premises caching appliance enhances data accessibility whether it resides in your datacenter, within Azure, or traversing a wide-area network (WAN). Comprising both software and hardware, the Microsoft Azure FXT Edge Filer offers exceptional throughput and minimal latency, designed specifically for hybrid storage environments that cater to high-performance computing (HPC) applications. Utilizing a scale-out clustering approach, it enables non-disruptive performance scaling of NAS capabilities. You can connect up to 24 FXT nodes in each cluster, allowing for an impressive expansion to millions of IOPS and several hundred GB/s speeds. When performance and scalability are critical for file-based tasks, Azure FXT Edge Filer ensures that your data remains on the quickest route to processing units. Additionally, managing your data storage becomes straightforward with Azure FXT Edge Filer, enabling you to transfer legacy data to Azure Blob Storage for easy access with minimal latency. This solution allows for a balanced approach between on-premises and cloud storage, ensuring optimal efficiency in data management while adapting to evolving business needs. Furthermore, this hybrid model supports organizations in maximizing their existing infrastructure investments while leveraging the benefits of cloud technology.

Amazon EC2 G4 instances are specifically designed to enhance the performance of machine learning inference and applications that require high graphics capabilities. Users can select between NVIDIA T4 GPUs (G4dn) and AMD Radeon Pro V520 GPUs (G4ad) according to their requirements. The G4dn instances combine NVIDIA T4 GPUs with bespoke Intel Cascade Lake CPUs, ensuring an optimal mix of computational power, memory, and networking bandwidth. These instances are well-suited for tasks such as deploying machine learning models, video transcoding, game streaming, and rendering graphics. On the other hand, G4ad instances, equipped with AMD Radeon Pro V520 GPUs and 2nd-generation AMD EPYC processors, offer a budget-friendly option for handling graphics-intensive workloads. Both instance types utilize Amazon Elastic Inference, which permits users to add economical GPU-powered inference acceleration to Amazon EC2, thereby lowering costs associated with deep learning inference. They come in a range of sizes tailored to meet diverse performance demands and seamlessly integrate with various AWS services, including Amazon SageMaker, Amazon ECS, and Amazon EKS. Additionally, this versatility makes G4 instances an attractive choice for organizations looking to leverage cloud-based machine learning and graphics processing capabilities.

Accelerate computational tasks such as those found in machine learning and high-performance computing (HPC) with a diverse array of GPUs suited for various performance levels and budget constraints. With adaptable pricing and customizable machines, you can fine-tune your setup to enhance your workload efficiency. Google Cloud offers high-performance GPUs ideal for machine learning, scientific analyses, and 3D rendering. The selection includes NVIDIA K80, P100, P4, T4, V100, and A100 GPUs, providing a spectrum of computing options tailored to meet different cost and performance requirements. You can effectively balance processor power, memory capacity, high-speed storage, and up to eight GPUs per instance to suit your specific workload needs. Enjoy the advantage of per-second billing, ensuring you only pay for the resources consumed during usage. Leverage GPU capabilities on Google Cloud Platform, where you benefit from cutting-edge storage, networking, and data analytics solutions. Compute Engine allows you to easily integrate GPUs into your virtual machine instances, offering an efficient way to enhance processing power. Explore the potential uses of GPUs and discover the various types of GPU hardware available to elevate your computational projects.

IREN’s AI Cloud is a cutting-edge GPU cloud infrastructure that utilizes NVIDIA's reference architecture along with a high-speed, non-blocking InfiniBand network capable of 3.2 TB/s, specifically engineered for demanding AI training and inference tasks through its bare-metal GPU clusters. This platform accommodates a variety of NVIDIA GPU models, providing ample RAM, vCPUs, and NVMe storage to meet diverse computational needs. Fully managed and vertically integrated by IREN, the service ensures clients benefit from operational flexibility, robust reliability, and comprehensive 24/7 in-house support. Users gain access to performance metrics monitoring, enabling them to optimize their GPU expenditures while maintaining secure and isolated environments through private networking and tenant separation. The platform empowers users to deploy their own data, models, and frameworks such as TensorFlow, PyTorch, and JAX, alongside container technologies like Docker and Apptainer, all while granting root access without any limitations. Additionally, it is finely tuned to accommodate the scaling requirements of complex applications, including the fine-tuning of extensive language models, ensuring efficient resource utilization and exceptional performance for sophisticated AI projects.

Azure Disk Storage is carefully crafted for deployment alongside Azure Virtual Machines and the preview version of Azure VMware Solution, providing robust and high-performance block storage solutions for critical business applications. Transitioning to Azure infrastructure becomes seamless with four distinct disk storage options available—Ultra Disk Storage, Premium SSD, Standard SSD, and Standard HDD—that allow you to balance performance and costs effectively for your specific workload needs. It ensures exceptional performance with sub-millisecond latency tailored for demanding applications like SAP HANA, SQL Server, and Oracle, which require intensive throughput and transaction capabilities. Additionally, shared disks facilitate the economical operation of clustered or high-availability applications in the cloud environment. With a remarkable 0% annual failure rate, you can expect consistent enterprise-level durability. Ultra Disk Storage allows you to scale without compromising performance, meeting increasing demands effortlessly. Furthermore, your data is protected with built-in encryption options, utilizing either Microsoft-managed keys or your personal encryption keys for enhanced security. This comprehensive approach ensures that your critical applications operate smoothly and securely in the cloud.

The NVIDIA DGX Cloud provides an AI infrastructure as a service that simplifies the deployment of large-scale AI models and accelerates innovation. By offering a comprehensive suite of tools for machine learning, deep learning, and HPC, this platform enables organizations to run their AI workloads efficiently on the cloud. With seamless integration into major cloud services, it offers the scalability, performance, and flexibility necessary for tackling complex AI challenges, all while eliminating the need for managing on-premise hardware.

FPT Cloud represents an advanced cloud computing and AI solution designed to enhance innovation through a comprehensive and modular suite of more than 80 services, encompassing areas such as computing, storage, databases, networking, security, AI development, backup, disaster recovery, and data analytics, all adhering to global standards. Among its features are scalable virtual servers that provide auto-scaling capabilities and boast a 99.99% uptime guarantee; GPU-optimized infrastructure specifically designed for AI and machine learning tasks; the FPT AI Factory, which offers a complete AI lifecycle suite enhanced by NVIDIA supercomputing technology, including infrastructure, model pre-training, fine-tuning, and AI notebooks; high-performance object and block storage options that are S3-compatible and encrypted; a Kubernetes Engine that facilitates managed container orchestration with portability across different cloud environments; as well as managed database solutions that support both SQL and NoSQL systems. Additionally, it incorporates sophisticated security measures with next-generation firewalls and web application firewalls, alongside centralized monitoring and activity logging features, ensuring a holistic approach to cloud services. This multifaceted platform is designed to meet the diverse needs of modern enterprises, making it a key player in the evolving landscape of cloud technology.

AWS ParallelCluster is a free, open-source tool designed for efficient management and deployment of High-Performance Computing (HPC) clusters within the AWS environment. It streamlines the configuration of essential components such as compute nodes, shared filesystems, and job schedulers, while accommodating various instance types and job submission queues. Users have the flexibility to engage with ParallelCluster using a graphical user interface, command-line interface, or API, which allows for customizable cluster setups and oversight. The tool also works seamlessly with job schedulers like AWS Batch and Slurm, making it easier to transition existing HPC workloads to the cloud with minimal adjustments. Users incur no additional costs for the tool itself, only paying for the AWS resources their applications utilize. With AWS ParallelCluster, users can effectively manage their computing needs through a straightforward text file that allows for the modeling, provisioning, and dynamic scaling of necessary resources in a secure and automated fashion. This ease of use significantly enhances productivity and optimizes resource allocation for various computational tasks.

Azure offers high-performance computing (HPC) solutions that drive innovative breakthroughs, tackle intricate challenges, and enhance your resource-heavy tasks. You can create and execute your most demanding applications in the cloud with a comprehensive solution specifically designed for HPC. Experience the benefits of supercomputing capabilities, seamless interoperability, and nearly limitless scalability for compute-heavy tasks through Azure Virtual Machines. Enhance your decision-making processes and advance next-generation AI applications using Azure's top-tier AI and analytics services. Additionally, protect your data and applications while simplifying compliance through robust, multilayered security measures and confidential computing features. This powerful combination ensures that organizations can achieve their computational goals with confidence and efficiency.

Qlustar presents an all-encompassing full-stack solution that simplifies the setup, management, and scaling of clusters while maintaining control and performance. It enhances your HPC, AI, and storage infrastructures with exceptional ease and powerful features. The journey begins with a bare-metal installation using the Qlustar installer, followed by effortless cluster operations that encompass every aspect of management. Experience unparalleled simplicity and efficiency in both establishing and overseeing your clusters. Designed with scalability in mind, it adeptly handles even the most intricate workloads with ease. Its optimization for speed, reliability, and resource efficiency makes it ideal for demanding environments. You can upgrade your operating system or handle security patches without requiring reinstallations, ensuring minimal disruption. Regular and dependable updates safeguard your clusters against potential vulnerabilities, contributing to their overall security. Qlustar maximizes your computing capabilities, ensuring peak efficiency for high-performance computing settings. Additionally, its robust workload management, built-in high availability features, and user-friendly interface provide a streamlined experience, making operations smoother than ever before. This comprehensive approach ensures that your computing infrastructure remains resilient and adaptable to changing needs.

Our team has developed the quickest AI accelerator, utilizing the most extensive processor available in the market, and have ensured its user-friendliness. With Cerebras, you can experience rapid training speeds, extremely low latency for inference, and an unprecedented time-to-solution that empowers you to reach your most daring AI objectives. Just how bold can these objectives be? We not only make it feasible but also convenient to train language models with billions or even trillions of parameters continuously, achieving nearly flawless scaling from a single CS-2 system to expansive Cerebras Wafer-Scale Clusters like Andromeda, which stands as one of the largest AI supercomputers ever constructed. This capability allows researchers and developers to push the boundaries of AI innovation like never before.

Together AI offers a cloud platform purpose-built for developers creating AI-native applications, providing optimized GPU infrastructure for training, fine-tuning, and inference at unprecedented scale. Its environment is engineered to remain stable even as customers push workloads to trillions of tokens, ensuring seamless reliability in production. By continuously improving inference runtime performance and GPU utilization, Together AI delivers a cost-effective foundation for companies building frontier-level AI systems. The platform features a rich model library including open-source, specialized, and multimodal models for chat, image generation, video creation, and coding tasks. Developers can replace closed APIs effortlessly through OpenAI-compatible endpoints. Innovations such as ATLAS, FlashAttention, Flash Decoding, and Mixture of Agents highlight Together AI’s strong research contributions. Instant GPU clusters allow teams to scale from prototypes to distributed workloads in minutes. AI-native companies rely on Together AI to break performance barriers and accelerate time to market.

The Intel® Tiber™ AI Cloud serves as a robust platform tailored to efficiently scale artificial intelligence workloads through cutting-edge computing capabilities. Featuring specialized AI hardware, including the Intel Gaudi AI Processor and Max Series GPUs, it enhances the processes of model training, inference, and deployment. Aimed at enterprise-level applications, this cloud offering allows developers to create and refine models using well-known libraries such as PyTorch. Additionally, with a variety of deployment choices, secure private cloud options, and dedicated expert assistance, Intel Tiber™ guarantees smooth integration and rapid deployment while boosting model performance significantly. This comprehensive solution is ideal for organizations looking to harness the full potential of AI technologies.

AWS Trainium represents a next-generation machine learning accelerator specifically designed for the training of deep learning models with over 100 billion parameters. Each Amazon Elastic Compute Cloud (EC2) Trn1 instance can utilize as many as 16 AWS Trainium accelerators, providing an efficient and cost-effective solution for deep learning training in a cloud environment. As the demand for deep learning continues to rise, many development teams often find themselves constrained by limited budgets, which restricts the extent and frequency of necessary training to enhance their models and applications. The EC2 Trn1 instances equipped with Trainium address this issue by enabling faster training times while also offering up to 50% savings in training costs compared to similar Amazon EC2 instances. This innovation allows teams to maximize their resources and improve their machine learning capabilities without the financial burden typically associated with extensive training.

Achieve prices that are 3-5 times more competitive than conventional cloud services. FluidStack combines underutilized GPUs from data centers globally to provide unmatched economic advantages in the industry. With just one platform and API, you can deploy over 50,000 high-performance servers in mere seconds. Gain access to extensive A100 and H100 clusters equipped with InfiniBand in just a few days. Utilize FluidStack to train, fine-tune, and launch large language models on thousands of cost-effective GPUs in a matter of minutes. By connecting multiple data centers, FluidStack effectively disrupts monopolistic GPU pricing in the cloud. Experience computing speeds that are five times faster while enhancing cloud efficiency. Instantly tap into more than 47,000 idle servers, all with tier 4 uptime and security, through a user-friendly interface. You can train larger models, set up Kubernetes clusters, render tasks more quickly, and stream content without delays. The setup process requires only one click, allowing for custom image and API deployment in seconds. Additionally, our engineers are available around the clock through Slack, email, or phone, acting as a seamless extension of your team to ensure you receive the support you need. This level of accessibility and assistance can significantly streamline your operations.

It enables efficient training on Amazon Elastic Compute Cloud (Amazon EC2) Trn1 instances powered by AWS Trainium. Additionally, for model deployment, it facilitates both high-performance and low-latency inference utilizing AWS Inferentia-based Amazon EC2 Inf1 instances along with AWS Inferentia2-based Amazon EC2 Inf2 instances. With the Neuron SDK, users can leverage widely-used frameworks like TensorFlow and PyTorch to effectively train and deploy machine learning (ML) models on Amazon EC2 Trn1, Inf1, and Inf2 instances with minimal alterations to their code and no reliance on vendor-specific tools. The integration of the AWS Neuron SDK with these frameworks allows for seamless continuation of existing workflows, requiring only minor code adjustments to get started. For those involved in distributed model training, the Neuron SDK also accommodates libraries such as Megatron-LM and PyTorch Fully Sharded Data Parallel (FSDP), enhancing its versatility and scalability for various ML tasks. By providing robust support for these frameworks and libraries, it significantly streamlines the process of developing and deploying advanced machine learning solutions.

The Cloud GPU Service is a flexible computing solution that offers robust GPU processing capabilities, ideal for high-performance parallel computing tasks. Positioned as a vital resource within the IaaS framework, it supplies significant computational power for various demanding applications such as deep learning training, scientific simulations, graphic rendering, and both video encoding and decoding tasks. Enhance your operational efficiency and market standing through the advantages of advanced parallel computing power. Quickly establish your deployment environment with automatically installed GPU drivers, CUDA, and cuDNN, along with preconfigured driver images. Additionally, speed up both distributed training and inference processes by leveraging TACO Kit, an all-in-one computing acceleration engine available from Tencent Cloud, which simplifies the implementation of high-performance computing solutions. This ensures your business can adapt swiftly to evolving technological demands while optimizing resource utilization.

Genesis Cloud is designed to support a wide range of applications, whether you are developing machine learning models or performing advanced data analytics. In just minutes, you can set up a virtual machine with either GPU or CPU capabilities, and with various configurations available, you’re sure to find a solution that fits your project's scale, from initial deployment to large-scale operations. You can also create storage volumes that automatically grow in response to your data needs; these are secured by a reliable storage cluster and encrypted to protect against unauthorized access or data loss. Our data centers utilize a state-of-the-art non-blocking leaf-spine architecture featuring 100G switches, ensuring that each server has multiple 25G uplinks, while every account operates within its own isolated virtual network for enhanced security and privacy. Additionally, our cloud services utilize renewable energy, making it not only environmentally friendly but also the most cost-effective option available in the marketplace. This commitment to sustainability and affordability sets Genesis Cloud apart as a leader in cloud infrastructure solutions.

There's no requirement to modify your coding practices or the methods you use to develop your projects. You can conduct profiling for applications that operate on multiple servers and involve various processes, providing clear insights into potential bottlenecks related to I/O, computational tasks, threading, or multi-process operations. You'll gain a profound understanding of the specific types of processor instructions that impact your overall performance. Additionally, you can monitor memory usage over time, allowing you to identify peak usage points and fluctuations throughout the entire memory landscape. Arm MAP stands out as a uniquely scalable profiler with low overhead, available both as an independent tool and as part of the comprehensive Arm Forge debugging and profiling suite. It is designed to assist developers of server and high-performance computing (HPC) software in speeding up their applications by pinpointing the root causes of sluggish performance. This tool is versatile enough to be employed on everything from multicore Linux workstations to advanced supercomputers. You have the option to profile realistic scenarios that matter the most to you while typically incurring less than 5% in runtime overhead. The user interface is interactive, fostering clarity and ease of use, making it well-suited for both developers and computational scientists alike, enhancing their productivity and efficiency.

The NVIDIA GPU-Optimized AMI serves as a virtual machine image designed to enhance your GPU-accelerated workloads in Machine Learning, Deep Learning, Data Science, and High-Performance Computing (HPC). By utilizing this AMI, you can quickly launch a GPU-accelerated EC2 virtual machine instance, complete with a pre-installed Ubuntu operating system, GPU driver, Docker, and the NVIDIA container toolkit, all within a matter of minutes. This AMI simplifies access to NVIDIA's NGC Catalog, which acts as a central hub for GPU-optimized software, enabling users to easily pull and run performance-tuned, thoroughly tested, and NVIDIA-certified Docker containers. The NGC catalog offers complimentary access to a variety of containerized applications for AI, Data Science, and HPC, along with pre-trained models, AI SDKs, and additional resources, allowing data scientists, developers, and researchers to concentrate on creating and deploying innovative solutions. Additionally, this GPU-optimized AMI is available at no charge, with an option for users to purchase enterprise support through NVIDIA AI Enterprise. For further details on obtaining support for this AMI, please refer to the section labeled 'Support Information' below. Moreover, leveraging this AMI can significantly streamline the development process for projects requiring intensive computational resources.

CUDO Compute is an advanced cloud platform for high-performance GPU computing that is specifically tailored for artificial intelligence applications, featuring both on-demand and reserved clusters that can efficiently scale to meet user needs. Users have the option to utilize a diverse array of powerful GPUs from a global selection, including top models like the NVIDIA H100 SXM, H100 PCIe, and a variety of other high-performance graphics cards such as the A800 PCIe and RTX A6000. This platform enables users to launch instances in a matter of seconds, granting them comprehensive control to execute AI workloads quickly while ensuring they can scale operations globally and adhere to necessary compliance standards. Additionally, CUDO Compute provides adaptable virtual machines suited for agile computing tasks, making it an excellent choice for development, testing, and lightweight production scenarios, complete with minute-based billing, rapid NVMe storage, and extensive customization options. For teams that demand direct access to hardware, dedicated bare metal servers are also available, maximizing performance without the overhead of virtualization, thus enhancing efficiency for resource-intensive applications. This combination of features makes CUDO Compute a compelling choice for organizations looking to leverage the power of AI in their operations.

Slurm Workload Manager, which was previously referred to as Simple Linux Utility for Resource Management (SLURM), is an open-source and cost-free job scheduling and cluster management system tailored for Linux and Unix-like operating systems. Its primary function is to oversee computing tasks within high-performance computing (HPC) clusters and high-throughput computing (HTC) settings, making it a popular choice among numerous supercomputers and computing clusters globally. As technology continues to evolve, Slurm remains a critical tool for researchers and organizations requiring efficient resource management.

Parasail is a network designed for deploying AI that offers scalable and cost-effective access to high-performance GPUs tailored for various AI tasks. It features three main services: serverless endpoints for real-time inference, dedicated instances for private model deployment, and batch processing for extensive task management. Users can either deploy open-source models like DeepSeek R1, LLaMA, and Qwen, or utilize their own models, with the platform’s permutation engine optimally aligning workloads with hardware, which includes NVIDIA’s H100, H200, A100, and 4090 GPUs. The emphasis on swift deployment allows users to scale from a single GPU to large clusters in just minutes, providing substantial cost savings, with claims of being up to 30 times more affordable than traditional cloud services. Furthermore, Parasail boasts day-zero availability for new models and features a self-service interface that avoids long-term contracts and vendor lock-in, enhancing user flexibility and control. This combination of features makes Parasail an attractive choice for those looking to leverage high-performance AI capabilities without the usual constraints of cloud computing.

Elastic computing instances equipped with GPU accelerators are ideal for various applications, including artificial intelligence, particularly deep learning and machine learning, high-performance computing, and advanced graphics processing. The Elastic GPU Service delivers a comprehensive system that integrates both software and hardware, enabling users to allocate resources with flexibility, scale their systems dynamically, enhance computational power, and reduce expenses related to AI initiatives. This service is applicable in numerous scenarios, including deep learning, video encoding and decoding, video processing, scientific computations, graphical visualization, and cloud gaming, showcasing its versatility. Furthermore, the Elastic GPU Service offers GPU-accelerated computing capabilities along with readily available, scalable GPU resources, which harness the unique strengths of GPUs in executing complex mathematical and geometric calculations, especially in floating-point and parallel processing. When compared to CPUs, GPUs can deliver an astounding increase in computing power, often being 100 times more efficient, making them an invaluable asset for demanding computational tasks. Overall, this service empowers businesses to optimize their AI workloads while ensuring that they can meet evolving performance requirements efficiently.

The Quantum exact simulation toolkit serves as a robust simulator for quantum circuits, state-vectors, and density matrices. QuEST harnesses the power of multithreading, GPU acceleration, and distributed computing to execute tasks rapidly on devices ranging from laptops to networked supercomputers. It operates seamlessly without requiring installation and can be easily compiled for immediate use. With no setup needed, users can download, compile, and launch QuEST in just seconds. Additionally, it has no external dependencies, allowing for native compilation on various operating systems including Windows, Linux, and MacOS. No matter if you are using a laptop, desktop, supercomputer, or even a microcontroller in the cloud, getting QuEST up and running typically requires only a handful of terminal commands. This accessibility makes QuEST a preferred choice for those delving into quantum simulations.