Alternatives to Run:AI

Amazon SageMaker, a fully managed service, provides data scientists and developers with the ability to quickly build, train, deploy, and deploy machine-learning (ML) models. SageMaker takes the hard work out of each step in the machine learning process, making it easier to create high-quality models. Traditional ML development can be complex, costly, and iterative. This is made worse by the lack of integrated tools to support the entire machine learning workflow. It is tedious and error-prone to combine tools and workflows. SageMaker solves the problem by combining all components needed for machine learning into a single toolset. This allows models to be produced faster and with less effort. Amazon SageMaker Studio is a web-based visual interface that allows you to perform all ML development tasks. SageMaker Studio allows you to have complete control over each step and gives you visibility.

Amazon Elastic Compute Cloud (Amazon EC2) provides secure, resizable cloud computing capacity. It was designed to make cloud computing at web scale easier for developers. Amazon EC2's web service interface makes it easy to configure and obtain capacity with minimal effort. It gives you complete control over your computing resources and allows you to run on Amazon's proven computing environment.

Foundry is the next generation of public cloud powered by an orchestration system that makes it as simple as flicking a switch to access AI computing. Discover the features of our GPU cloud service designed for maximum performance. You can use our GPU cloud services to manage training runs, serve clients, or meet research deadlines. For years, industry giants have invested in infra-teams that build sophisticated tools for cluster management and workload orchestration to abstract the hardware. Foundry makes it possible for everyone to benefit from the compute leverage of a twenty-person team. The current GPU ecosystem operates on a first-come-first-served basis and is fixed-price. The availability of GPUs during peak periods is a problem, as are the wide differences in pricing across vendors. Foundry's price performance is superior to anyone else on the market thanks to a sophisticated mechanism.

Enhance the digital workspace with the efficient and secured delivery of virtual desktops, apps and services from on-premises into the cloud. Horizon 8 is the latest platform from the leader in software defined data centers and digital workspaces for delivering virtual desktops and applications across hybrid clouds. Horizon 8's unique integration with trusted technologies helps IT efficiently scale and deploy virtual desktops and applications from a single control panel with rapid provisioning and automation. It also simplifies management and extends the best digital workspace to end users. Manage desktops and applications across private, hybrid, and multi-cloud infrastructures using a cloud console and SaaS services. Use the intrinsic security built into Horizon's infrastructure to gain secure remote access to corporate assets - protecting from the device to data centers to the cloud.

We have the infrastructure to meet all your computing needs, whether you need one or thousands GPUs or one vCPU or tens of thousand vCPUs. Our resources are available whenever you need them. Our platform makes switching between GPU and CPU instances a breeze. You can easily deploy, modify and rescale instances to meet your needs. You can get outstanding machine learning performance without breaking your bank. The latest technology for a much lower price. Modern GPUs are built to meet your workload demands. Get access to computing resources that are tailored for your models. Our OblivusAI OS allows you to access libraries and leverage our infrastructure for large-scale inference. Use our robust infrastructure to unleash the full potential of gaming by playing games in settings of your choosing.

Launch GPU-accelerated instances that are highly configurable for your AI workload and budget. Reserve thousands of GPUs for training and inference in a next generation AI data center. The AI world is moving to GPU clouds in order to build and launch groundbreaking models without having the hassle of managing infrastructure or scarcity of resources. AI-centric cloud providers are outperforming traditional hyperscalers in terms of availability, compute costs, and scaling GPU utilization for complex AI workloads. Ori has a large pool with different GPU types that are tailored to meet different processing needs. This ensures that a greater concentration of powerful GPUs are readily available to be allocated compared to general purpose clouds. Ori offers more competitive pricing, whether it's for dedicated servers or on-demand instances. Our GPU compute costs are significantly lower than the per-hour and per-use pricing of legacy cloud services.

The most complex AI, ML, Deep Learning models can be trained. With just a few clicks, you can scale from a single machine up to a whole fleet of VMs. Lambda Cloud makes it easy to scale up or start your Deep Learning project. You can get started quickly, save compute costs, and scale up to hundreds of GPUs. Every VM is pre-installed with the most recent version of Lambda Stack. This includes major deep learning frameworks as well as CUDA®. drivers. You can access the cloud dashboard to instantly access a Jupyter Notebook development environment on each machine. You can connect directly via the Web Terminal or use SSH directly using one of your SSH keys. Lambda can make significant savings by building scaled compute infrastructure to meet the needs of deep learning researchers. Cloud computing allows you to be flexible and save money, even when your workloads increase rapidly.

Unlock prices that are 3-5x higher than those of traditional clouds. FluidStack aggregates GPUs from data centres around the world that are underutilized to deliver the best economics in the industry. Deploy up to 50,000 high-performance servers within seconds using a single platform. In just a few days, you can access large-scale A100 or H100 clusters using InfiniBand. FluidStack allows you to train, fine-tune and deploy LLMs for thousands of GPUs at affordable prices in minutes. FluidStack unifies individual data centers in order to overcome monopolistic GPU pricing. Cloud computing can be made more efficient while allowing for 5x faster computation. Instantly access over 47,000 servers with tier four uptime and security through a simple interface. Train larger models, deploy Kubernetes Clusters, render faster, and stream without latency. Setup with custom images and APIs in seconds. Our engineers provide 24/7 direct support through Slack, email, or phone calls.

The first hardware and software computing protocol that integrates both to train and fine tune your AI models. Reduce your training costs up to 80%. Deploy your model in seconds using open-source template models or bring your model. Debug containers easily with GPU, CPU and Memory metrics. You can monitor logs live. You can track all models and training set with cryptographic proofs to ensure complete accountability. You can control the entire training process with just a few commands. You can earn block rewards by adding your computer to the networking. Track key metrics like connectivity and uptime.

The NVIDIA GPU Optimized AMI is a virtual image that accelerates your GPU-accelerated Machine Learning and Deep Learning workloads. This AMI allows you to spin up a GPU accelerated EC2 VM in minutes, with a preinstalled Ubuntu OS and GPU driver. Docker, NVIDIA container toolkit, and Docker are also included. This AMI provides access to NVIDIA’s NGC Catalog. It is a hub of GPU-optimized software for pulling and running performance-tuned docker containers that have been tested and certified by NVIDIA. The NGC Catalog provides free access to containerized AI and HPC applications. It also includes pre-trained AI models, AI SDKs, and other resources. This GPU-optimized AMI comes free, but you can purchase enterprise support through NVIDIA Enterprise. Scroll down to the 'Support information' section to find out how to get support for AMI.

Amazon Elastic Compute Cloud Trn1 instances powered by AWS Trainium are designed for high-performance deep-learning training of generative AI model, including large language models, latent diffusion models, and large language models. Trn1 instances can save you up to 50% on the cost of training compared to other Amazon EC2 instances. Trn1 instances can be used to train 100B+ parameters DL and generative AI model across a wide range of applications such as text summarizations, code generation and question answering, image generation and video generation, fraud detection, and recommendation. The AWS neuron SDK allows developers to train models on AWS trainsium (and deploy them on the AWS Inferentia chip). It integrates natively into frameworks like PyTorch and TensorFlow, so you can continue to use your existing code and workflows for training models on Trn1 instances.

Amazon EC2 Trn2 instances powered by AWS Trainium2 are designed for high-performance deep-learning training of generative AI model, including large language models, diffusion models, and diffusion models. They can save up to 50% on the cost of training compared to comparable Amazon EC2 Instances. Trn2 instances can support up to 16 Trainium2 accelerations, delivering up to 3 petaflops FP16/BF16 computing power and 512GB of high bandwidth memory. Trn2 instances support up to 1600 Gbps second-generation Elastic Fabric Adapter network bandwidth. NeuronLink is a high-speed nonblocking interconnect that facilitates efficient data and models parallelism. They are deployed as EC2 UltraClusters and can scale up to 30,000 Trainium2 processors interconnected by a nonblocking, petabit-scale, network, delivering six exaflops in compute performance. The AWS neuron SDK integrates with popular machine-learning frameworks such as PyTorch or TensorFlow.

Setup should be simple. We've listened carefully to the feedback of our community in order to simplify the developer experience. Our billing model was designed from the ground up for cloud-native. You only pay for what you need and there are no surprises. Launch times that are industry-leading will boost productivity. Accelerate the development cycle, innovate and deliver faster results. Blazing fast, simplified, managed Kubernetes. Host applications and scale them as you need, with a 90-second cluster launch time and a free controller plane. Kubernetes-powered enterprise-class compute instances. Multi-region support, DDoS Protection, bandwidth pooling and all the developer tool you need. Fully managed, auto-scaling machine-learning environment. No Kubernetes, ML or Kubernetes expertise is required. Setup and scale managed databases easily from your Civo dashboard, or our developer API. Scale up or down as needed, and only pay for the resources you use.

D-I-Y can be used in certain situations, such as weed control. It's a different story to build your AI infrastructure. ONTAP AI consolidates the data center's worth in analytics, training, inference computation, and training into one, 5-petaflop AI system. NetApp ONTAP AI is powered by NVIDIA's DGX™, and NetApp's cloud-connected all flash storage. This allows you to fully realize the promise and potential of deep learning (DL). With the proven ONTAP AI architecture, you can simplify, accelerate and integrate your data pipeline. Your data fabric, which spans from the edge to the core to the cloud, will streamline data flow and improve analytics, training, inference, and performance. NetApp ONTAPAI is the first converged infrastructure platform to include NVIDIA DGX A100 (the world's first 5-petaflop AIO system) and NVIDIA Mellanox®, high-performance Ethernet switches. You get unified AI workloads and simplified deployment.

Amazon EC2 capacity blocks for ML allow you to reserve accelerated compute instance in Amazon EC2 UltraClusters that are dedicated to machine learning workloads. This service supports Amazon EC2 P5en instances powered by NVIDIA Tensor Core GPUs H200, H100 and A100, as well Trn2 and TRn1 instances powered AWS Trainium. You can reserve these instances up to six months ahead of time in cluster sizes from one to sixty instances (512 GPUs, or 1,024 Trainium chip), providing flexibility for ML workloads. Reservations can be placed up to 8 weeks in advance. Capacity Blocks can be co-located in Amazon EC2 UltraClusters to provide low-latency and high-throughput connectivity for efficient distributed training. This setup provides predictable access to high performance computing resources. It allows you to plan ML application development confidently, run tests, build prototypes and accommodate future surges of demand for ML applications.

Platform with NVIDIA H100 Tensor core GPUs. Competitive pricing. Support from a dedicated team. Built for large-scale ML workloads. Get the most from multihost training with thousands of H100 GPUs in full mesh connections using the latest InfiniBand networks up to 3.2Tb/s. Best value: Save up to 50% on GPU compute when compared with major public cloud providers*. You can save even more by purchasing GPUs in large quantities and reserving GPUs. Onboarding assistance: We provide a dedicated engineer to ensure smooth platform adoption. Get your infrastructure optimized, and k8s installed. Fully managed Kubernetes - Simplify the deployment and scaling of ML frameworks using Kubernetes. Use Managed Kubernetes to train GPUs on multiple nodes. Marketplace with ML Frameworks: Browse our Marketplace to find ML-focused libraries and applications, frameworks, and tools that will streamline your model training. Easy to use. All new users are entitled to a one-month free trial.

GMI GPU Cloud allows you to create generative AI applications within minutes. GMI Cloud offers more than just bare metal. Train, fine-tune and infer the latest models. Our clusters come preconfigured with popular ML frameworks and scalable GPU containers. Instantly access the latest GPUs to power your AI workloads. We can provide you with flexible GPUs on-demand or dedicated private cloud instances. Our turnkey Kubernetes solution maximizes GPU resources. Our advanced orchestration tools make it easy to allocate, deploy and monitor GPUs or other nodes. Create AI applications based on your data by customizing and serving models. GMI Cloud allows you to deploy any GPU workload quickly, so that you can focus on running your ML models and not managing infrastructure. Launch pre-configured environment and save time building container images, downloading models, installing software and configuring variables. You can also create your own Docker images to suit your needs.

Accelerate compute jobs such as machine learning and HPC. There are many GPUs available to suit different price points and performance levels. Flexible pricing and machine customizations are available to optimize your workload. High-performance GPUs available on Google Cloud for machine intelligence, scientific computing, 3D visualization, and machine learning. NVIDIA K80 and P100 GPUs, T4, V100 and A100 GPUs offer a variety of compute options to meet your workload's cost and performance requirements. You can optimize the processor, memory and high-performance disk for your specific workload by using up to 8 GPUs per instance. All this with per-second billing so that you only pay for what you use. You can run GPU workloads on Google Cloud Platform, which offers industry-leading storage, networking and data analytics technologies. Compute Engine offers GPUs that can be added to virtual machine instances. Learn more about GPUs and the types of hardware available.

CORE is a high performance computing platform that can be used for a variety of applications. CORE is easy to use with its point-and-click interface. You can run the most complex applications. CORE provides unlimited computing power on-demand. Cloud computing is available without the high-cost. CORE for teams offers powerful tools that allow you to sort, filter, create, connect, and create users, machines, networks, and machines. With an intuitive and simple GUI, it's easier than ever to see all of your infrastructure from one place. It is easy to add Active Directory integration or VPN through our simple but powerful management console. It's now possible to do things that used to take days, or even weeks. Even complex network configurations can be managed with just a few clicks.

Hyperstack, the ultimate self-service GPUaaS Platform, offers the H100 and A100 as well as the L40, and delivers its services to the most promising AI start ups in the world. Hyperstack was built for enterprise-grade GPU acceleration and optimised for AI workloads. NexGen Cloud offers enterprise-grade infrastructure for a wide range of users from SMEs, Blue-Chip corporations to Managed Service Providers and tech enthusiasts. Hyperstack, powered by NVIDIA architecture and running on 100% renewable energy, offers its services up to 75% cheaper than Legacy Cloud Providers. The platform supports diverse high-intensity workloads such as Generative AI and Large Language Modeling, machine learning and rendering.

Manage, optimize, and optimize HPC and large compute clusters at any scale. You can deploy full clusters and other resources including schedulers, compute VMs (storage, networking, and caching), and other resources such as cache, network, networking, and storage. Advanced policy and governance features allow you to customize and optimize clusters, including cost controls, Active Directory integration and monitoring. You can continue using your existing job scheduler and other applications. Administrators have complete control over who can run jobs and where they are located. You can take advantage of autoscaling and battle-tested references architectures for a wide variety of HPC workloads. CycleCloud supports every job scheduler and software stack, from proprietary in-house to open source, third-party, or commercial. Your cluster should adapt to your changing resource requirements. Scheduler-aware autoscaling allows you to match your resources to your workload.

We are ready to meet all your business needs, whether it is quick engineering, fine-tuning or training. The Together Inference API makes it easy to integrate your new model in your production application. Together AI's elastic scaling and fastest performance allows it to grow with you. To increase accuracy and reduce risks, you can examine how models are created and what data was used. You are the owner of the model that you fine-tune and not your cloud provider. Change providers for any reason, even if the price changes. Store data locally or on our secure cloud to maintain complete data privacy.

It is a simple and flexible task orchestrator that deploys and manages containers and non-containerized apps across on-prem as well as cloud environments. One 35MB binary that can be integrated into existing infrastructure. It is easy to use on-prem and in the cloud with minimal overhead. You can orchestrate any type of application, not just containers. First-class support for Docker and Windows, Java, VMs, VMs, and other technologies. Orchestration benefits can be added to existing services. Zero downtime deployments, increased resilience, higher resource utilization, as well as greater resilience can all be achieved without containerization. Multi-region, multicloud federation - single command Nomad is a single control plane that allows you to deploy applications worldwide to any region. One workflow to deploy to cloud or bare metal environments. Multi-cloud applications can be enabled with ease. Nomad seamlessly integrates with Terraform Consul and Vault for provisioning and service networking. Secrets management is also possible.

Appvia Wayfinder provides a dynamic solution to manage your cloud infrastructure. It gives your developers self-service capabilities that let them manage and provision cloud resources without any hitch. Wayfinder's core is its security-first strategy, which is built on principles of least privilege and isolation. You can rest assured that your resources are safe. Platform teams rejoice! Centralised control allows you to guide your team and maintain organisational standards. But it's not just business. Wayfinder provides a single pane for visibility. It gives you a bird's-eye view of your clusters, applications, and resources across all three clouds. Join the leading engineering groups worldwide who rely on Appvia Wayfinder for cloud deployments. Do not let your competitors leave behind you. Watch your team's efficiency and productivity soar when you embrace Wayfinder!

You can use advanced language models and coding to solve a variety of problems. To build cutting-edge applications, leverage large-scale, generative AI models that have deep understandings of code and language to allow for new reasoning and comprehension. These coding and language models can be applied to a variety use cases, including writing assistance, code generation, reasoning over data, and code generation. Access enterprise-grade Azure security and detect and mitigate harmful use. Access generative models that have been pretrained with trillions upon trillions of words. You can use them to create new scenarios, including code, reasoning, inferencing and comprehension. A simple REST API allows you to customize generative models with labeled information for your particular scenario. To improve the accuracy of your outputs, fine-tune the hyperparameters of your model. You can use the API's few-shot learning capability for more relevant results and to provide examples.

Klu.ai, a Generative AI Platform, simplifies the design, deployment, and optimization of AI applications. Klu integrates your Large Language Models and incorporates data from diverse sources to give your applications unique context. Klu accelerates the building of applications using language models such as Anthropic Claude (Azure OpenAI), GPT-4 (Google's GPT-4), and over 15 others. It allows rapid prompt/model experiments, data collection and user feedback and model fine tuning while cost-effectively optimising performance. Ship prompt generation, chat experiences and workflows in minutes. Klu offers SDKs for all capabilities and an API-first strategy to enable developer productivity. Klu automatically provides abstractions to common LLM/GenAI usage cases, such as: LLM connectors and vector storage, prompt templates, observability and evaluation/testing tools.

We have all the infrastructure (computers, Frameworks, Cuda) and software (Cuda) you need to train and deploy deep-learning models. You can launch GPU/CPU instances directly from your web browser or automate the process through our Python API.

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).

Vast.ai offers the lowest-cost cloud GPU rentals. Save up to 5-6 times on GPU computation with a simple interface. Rent on-demand for convenience and consistency in pricing. You can save up to 50% more by using spot auction pricing for interruptible instances. Vast offers a variety of providers with different levels of security, from hobbyists to Tier-4 data centres. Vast.ai can help you find the right price for the level of reliability and security you need. Use our command-line interface to search for offers in the marketplace using scriptable filters and sorting options. Launch instances directly from the CLI, and automate your deployment. Use interruptible instances to save an additional 50% or even more. The highest bidding instance runs; other conflicting instances will be stopped.

Runyour AI offers the best environment for artificial intelligence. From renting machines to research AI to specialized templates, Runyour AI has it all. Runyour AI provides GPU resources and research environments to artificial intelligence researchers. Renting high-performance GPU machines is possible at a reasonable cost. You can also register your own GPUs in order to generate revenue. Transparent billing policy, where you only pay for the charging points that are used. We offer specialized GPUs that are suitable for a wide range of users, from casual hobbyists to researchers. Even first-time users can easily and conveniently work on AI projects. Runyour AI GPU machines allow you to start your AI research quickly and with minimal setup. It is designed for quick access to GPUs and provides a seamless environment for machine learning, AI development, and research.

Qubrid AI is a company that specializes in Artificial Intelligence. Its mission is to solve complex real-world problems across multiple industries. Qubrid AI’s software suite consists of AI Hub, an all-in-one shop for AI models, AI Compute GPU cloud and On-Prem appliances, and AI Data Connector. You can train or infer industry-leading models, or your own custom creations. All within a streamlined and user-friendly interface. Test and refine models with ease. Then, deploy them seamlessly to unlock the power AI in your projects. AI Hub enables you to embark on a journey of AI, from conception to implementation, in a single powerful platform. Our cutting-edge AI Compute Platform harnesses the power from GPU Cloud and On Prem Server Appliances in order to efficiently develop and operate next generation AI applications. Qubrid is a team of AI developers, research teams and partner teams focused on enhancing the unique platform to advance scientific applications.

Each GPU contains 16896 CUDA Cores and 528 Tensor cores. This is the current flagship chip from NVidia®, which is unmatched in terms of raw performance for AI operations. We use the SXM5 module of NVLINK, which has a memory bandwidth up to 2.6 Gbps. It also offers 900GB/s bandwidth P2P. Fourth generation AMD Genoa with up to 384 Threads and a boost clock 3.7GHz. We only use the SXM4 "for NVLINK" module, which has a memory bandwidth exceeding 2TB/s as well as a P2P bandwidth up to 600GB/s. Second generation AMD EPYC Rome with up to 192 Threads and a boost clock 3.3GHz. The name 8A100.176V consists of 8x RTX, 176 CPU cores threads and virtualized. It is faster at processing tensor operations than the V100 despite having fewer tensors. This is due to its different architecture. Second generation AMD EPYC Rome with up to 96 threads and a boost clock speed of 3.35GHz.

Amazon EC2 instances P4d deliver high performance in cloud computing for machine learning applications and high-performance computing. They offer 400 Gbps networking and are powered by NVIDIA Tensor Core GPUs. P4d instances offer up to 60% less cost for training ML models. They also provide 2.5x better performance compared to the previous generation P3 and P3dn instance. P4d instances are deployed in Amazon EC2 UltraClusters which combine high-performance computing with networking and storage. Users can scale from a few NVIDIA GPUs to thousands, depending on their project requirements. Researchers, data scientists and developers can use P4d instances to build ML models to be used in a variety of applications, including natural language processing, object classification and detection, recommendation engines, and HPC applications.

Fal is a serverless Python Runtime that allows you to scale your code on the cloud without any infrastructure management. Build real-time AI apps with lightning-fast inferences (under 120ms). You can start building AI applications with some of the models that are ready to use. They have simple API endpoints. Ship custom model endpoints that allow for fine-grained control of idle timeout, maximum concurrency and autoscaling. APIs are available for models like Stable Diffusion Background Removal ControlNet and more. These models will be kept warm for free. Join the discussion and help shape the future AI. Scale up to hundreds GPUs and down to zero GPUs when idle. Pay only for the seconds your code runs. You can use fal in any Python project simply by importing fal and wrapping functions with the decorator.

You can quickly provision a VM with everything you need for your deep learning project on Google Cloud. Deep Learning VM Image makes it simple and quick to create a VM image containing all the most popular AI frameworks for a Google Compute Engine instance. Compute Engine instances can be launched pre-installed in TensorFlow and PyTorch. Cloud GPU and Cloud TPU support can be easily added. Deep Learning VM Image supports all the most popular and current machine learning frameworks like TensorFlow, PyTorch, and more. Deep Learning VM Images can be used to accelerate model training and deployment. They are optimized with the most recent NVIDIA®, CUDA-X AI drivers and libraries, and the Intel®, Math Kernel Library. All the necessary frameworks, libraries and drivers are pre-installed, tested and approved for compatibility. Deep Learning VM Image provides seamless notebook experience with integrated JupyterLab support.

Amazon EC2 instances G5 are the latest generation NVIDIA GPU instances. They can be used to run a variety of graphics-intensive applications and machine learning use cases. They offer up to 3x faster performance for graphics-intensive apps and machine learning inference, and up to 3.33x faster performance for machine learning learning training when compared to Amazon G4dn instances. Customers can use G5 instance for graphics-intensive apps such as video rendering, gaming, and remote workstations to produce high-fidelity graphics real-time. Machine learning customers can use G5 instances to get a high-performance, cost-efficient infrastructure for training and deploying larger and more sophisticated models in natural language processing, computer visualisation, and recommender engines. G5 instances offer up to three times higher graphics performance, and up to forty percent better price performance compared to G4dn instances. They have more ray tracing processor cores than any other GPU based EC2 instance.

AWS Inferentia Accelerators are designed by AWS for high performance and low cost for deep learning (DL), inference applications. The first-generation AWS Inferentia accelerator powers Amazon Elastic Compute Cloud, Amazon EC2 Inf1 instances. These instances deliver up to 2.3x more throughput and up 70% lower cost per input than comparable GPU-based Amazon EC2 instances. Inf1 instances have been adopted by many customers including Snap, Sprinklr and Money Forward. They have seen the performance and cost savings. The first-generation Inferentia features 8 GB of DDR4 memory per accelerator, as well as a large amount on-chip memory. Inferentia2 has 32 GB of HBM2e, which increases the total memory by 4x and memory bandwidth 10x more than Inferentia.

NVIDIA GPU Cloud is a GPU-accelerated cloud platform that is optimized for scientific computing and deep learning. NGC is responsible for a catalogue of fully integrated and optimized deep-learning framework containers that take full benefit of NVIDIA GPUs in single and multi-GPU configurations.

Slurm Workload Manager (formerly Simple Linux Utility for Resource Management, or SLURM) is a free and open-source cluster management system for Linux-like kernels. It is designed to manage computing jobs on high-performance computing (HPC), high throughput computing environments (HTC), and is used by most supercomputers and computer groups around the world.

VMware NSX enables full-stack network and security virtualization. Your virtual cloud network can connect and protect applications from your data center, multi-cloud, container infrastructure, and bare metal. VMware NSX Data Center is a complete L2-L7 security and networking platform that allows you to manage your entire network from one pane of glass. You can easily provision your security and networking services with one click. You can manage consistent security and networking policies across private and publicly cloud environments from one pane of glass. This is regardless of whether your application runs on a VM, container or bare metal. Micro-segmentation allows you to provide granular protection for your apps, depending on the workload.

AWS ParallelCluster, an open-source tool for cluster management, simplifies the deployment of High-Performance Computing clusters (HPC) on AWS. It automates resource setup, including compute nodes and a shared filesystem. It also supports multiple instance types and queues for job submission. ParallelCluster can be accessed via a graphical interface, command line interface, or API. This allows for flexible cluster management and configuration. The tool integrates with AWS Batch and Slurm to facilitate seamless migration of HPC workloads into the cloud. AWS ParallelCluster comes at no extra cost; users pay only for the AWS resources used by their applications. AWS ParallelCluster allows you to use a simple text document to model, provision and dynamically scale resources for your applications. This can be done in an automated, secure and automated manner.

NVIDIA Base command manager offers end-to-end management and fast deployment for heterogeneous AI clusters and high-performance computing at the edge, data center and in multi-cloud and hybrid environments. It automates provisioning and management of clusters from a few nodes up to hundreds of thousands of nodes, supports NVIDIA GPU accelerated systems and other systems and enables orchestration using Kubernetes. The platform integrates Kubernetes to orchestrate workloads and provides tools for infrastructure monitoring and workload management. Base Command Manager has been optimized for accelerated computing environments and is suitable for diverse HPC workloads. It is available on NVIDIA DGX Systems and as part the NVIDIA AI enterprise software suite. NVIDIA Base Command Manager allows you to quickly build and manage high-performance Linux clusters for HPC, machine learning and analytics applications.

The ultimate full-stack clustering solution that allows you to manage and scale clusters with ease and control. Qlustar provides unmatched simplicity and robust capability to your HPC and AI environments. Qlustar offers a seamless cluster operation, from bare-metal installations with the Qlustar Installer to seamless cluster operations. Setup and manage your clusters in an unmatched manner. Designed to grow along with your needs and handle even the most complex workloads without any hassle. Designed for speed, reliability and resource efficiency. Upgrade your OS, manage security patches and avoid reinstallations. Regular updates protect your clusters from vulnerabilities. Qlustar optimizes computing power to deliver peak efficiency in high-performance computing environments. Our solution provides robust workload management, high availability built-in, and an intuitive user interface for streamlined operations.

Container Engine for Kubernetes is an Oracle-managed container orchestration platform that can help you build modern cloud native apps in a shorter time and at a lower cost. Oracle Cloud Infrastructure offers Container Engine for Kubernetes free of charge, running on more efficient and lower-cost compute shapes than most other vendors. Open-source Kubernetes can be used by DevOps engineers for application workload portability, and to simplify operations with automatic updates. With a single click, deploy Kubernetes clusters, including the underlying virtual clouds networks, internet gateways and NAT gateways. Automate Kubernetes operations using web-based REST API or CLI. This includes cluster creation, scaling, operations, and maintenance. Cluster management is free with Oracle Container Engine for Kubernetes. You can easily and quickly upgrade container clusters with zero downtime to keep them current with the latest stable version Kubernetes.

Welcome to the OpenHPC website. OpenHPC was created as a collaborative community effort to gather a number common ingredients needed to deploy and manage High Performance Computing Linux clusters. These include provisioning tools and resource management, I/O Clients, development tools and a variety scientific libraries. OpenHPC packages have been built with HPC integration and reuse in mind. Over time, the HPC community will also identify and develop abstractions interfaces between key component to further enhance modularity. The community is made up of representatives from many sources, including software vendors and equipment manufacturers, as well as research institutions, supercomputing centers, and other organizations. This community integrates a variety of components that are widely used in HPC systems, and are available for free open source distribution.

Warewulf, a cluster management system and provisioning tool, has been a pioneer in stateless node-management for more than two decades. It allows provisioning containers directly on bare metal hardware, at scales ranging from 10s to 10,000s of compute systems, while maintaining simplicity and versatility. The platform is extensible and allows users to modify the default functionalities and images of nodes to suit different clustering use cases. Warewulf provides stateless provisioning using SELinux and per-node asset keys-based provisioning. It also offers access controls to ensure secure deployments. Its minimal requirements, ease of customization, integration, and optimization make it accessible to a wide range of industries. Warewulf is a highly successful HPC cluster platform that is used across many sectors. It's supported by OpenHPC, and has contributors from around the world. Easy to start, easy to customize and integrate, minimal system requirements.

You can easily create anti-affinity groups to spawn virtual servers at different data centers. This will ensure high availability. Security groups allow you to securely configure firewall rules across multiple instances. You can manage your team members and control who has access to your infrastructure using keypairs, organizations, and multi-factor authentication. Simple and intuitive interfaces make complex concepts simple to use for any size team. A trusted partner is essential when running critical production workloads in cloud. Our customer success engineers have assisted hundreds of customers across Europe to migrate, scale and scale cloud native production workloads. A partner that you can trust is crucial when running critical production workloads in cloud computing.

Our Linux virtual machines simplify cloud infrastructure and provide a robust set of tools that make it easy to develop, deploy, scale, and scale modern applications faster and more efficiently. Linode believes virtual computing is essential to enable innovation in the cloud. It must be accessible, affordable, and easy. Our infrastructure-as-a-service platform is deployed across 11 global markets from our data centers around the world and is supported by our Next Generation Network, advanced APIs, comprehensive services, and vast library of educational resources. Linode products, services and people allow developers and businesses to create, deploy, scale, and scale applications in the cloud more efficiently and cost-effectively.

You can migrate your business and mission-critical workloads to Azure to improve operational efficiencies. Azure Virtual Machines can run SQL Server, SAP, Oracle®, and other high-performance computing software. Choose your favorite Linux distribution and Windows Server.

A modern Kubernetes native cloud, specifically designed for large-scale, GPU-accelerated workloads. CoreWeave was designed with engineers and innovators as its primary focus. It offers unprecedented access to a wide range of compute solutions that are up 35x faster than traditional cloud providers and up to 80% cheaper than legacy ones. Each component of our infrastructure was carefully designed to allow our clients to access the compute power they need to create and innovate. Our core differentiation is the ability to scale up or down in seconds. We're always available to meet customer demand. We mean it when we say that you can access thousands of GPUs in a matter of seconds. We provide compute at a fair price and the flexibility to configure your instances to your requirements.