Alternatives to DataCrunch

Cloud servers, bare metal and storage can be easily deployed worldwide. Our high-performance compute instances are ideal for your web application development environment. Once you click deploy, Vultr cloud orchestration takes control and spins up the instance in your preferred data center. In seconds, you can spin up a new instance using your preferred operating system or preinstalled applications. You can increase the capabilities of your cloud servers whenever you need them. For mission-critical systems, automatic backups are essential. You can easily set up scheduled backups via the customer portal. Our API and control panel are easy to use, so you can spend more time programming and less time managing your infrastructure.

All the information you need to deploy and maintain single-tenant, high performance bare metal servers. Latitude.sh is a great alternative to VMs. Latitude.sh has a lot more computing power than VMs. Latitude.sh gives you the speed and flexibility of a dedicated server, as well as the flexibility of the cloud. You can deploy your servers instantly through the Control Panel or use our powerful API to manage them. Latitude.sh offers a variety of hardware and connectivity options to meet your specific needs. Latitude.sh also offers automation. A robust, intuitive control panel that you can access in real-time to power your team, allows you to see and modify your infrastructure. Latitude.sh is what you need to run mission-critical services that require high uptime and low latency. We have our own private datacenter, so we are familiar with the best infrastructure.

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.

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.

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.

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.

Deep learning, 3D modelling, simulations and distributed analytics take days or even weeks. GPUonCLOUD’s dedicated GPU servers can do it in a matter hours. You may choose pre-configured or pre-built instances that feature GPUs with deep learning frameworks such as TensorFlow and PyTorch. MXNet and TensorRT are also available. OpenCV is a real-time computer-vision library that accelerates AI/ML model building. Some of the GPUs we have are the best for graphics workstations or multi-player accelerated games. Instant jumpstart frameworks improve the speed and agility in the AI/ML environment through effective and efficient management of the environment lifecycle.

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.

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.

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.

The increased demand for computing power is too much for conventional CPUs. GPU processors process data at speeds 100-200x faster than conventional CPUs. We offer servers that are designed specifically for machine learning or deep learning, and are equipped with unique features. Modern hardware based upon the NVIDIA®, GPU chipset. This has a high operating speed. The latest Tesla® V100 card with its high processing power. Optimized for deep-learning software, TensorFlow™, Caffe2, Torch, Theano, CNTK, MXNet™. Includes development tools for Python 2, Python 3 and C++. We do not charge extra fees for each service. Disk space and traffic are included in the price of the basic service package. Our servers can also be used to perform various tasks such as video processing, rendering etc. LeaderGPU®, customers can now access a graphical user interface via RDP.

Find, provision and configure AI-ready Cloud instances for development, training and deployment. Install CUDA and Python automatically, load the model and SSH in. Brev.dev can help you find a GPU to train or fine-tune your model. A single interface for AWS, GCP and Lambda GPU clouds. Use credits as you have them. Choose an instance based upon cost & availability. A CLI that automatically updates your SSH configuration, ensuring it is done securely. Build faster using a better development environment. Brev connects you to cloud providers in order to find the best GPU for the lowest price. It configures the GPU and wraps SSH so that your code editor can connect to the remote machine. Change your instance. Add or remove a graphics card. Increase the size of your hard drive. Set up your environment so that your code runs always and is easy to share or copy. You can either create your own instance or use a template. The console should provide you with a few template options.

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.

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.

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.

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.

Oracle Cloud Infrastructure offers fast, flexible, affordable compute capacity that can be used to support any workload, from lightweight containers to performant bare metal servers to VMs and VMs. OCI Compute offers a unique combination of bare metal and virtual machines for optimal price-performance. You can choose exactly how many cores and memory your applications require. High performance for enterprise workloads Serverless computing simplifies application development. Kubernetes, containers and other technologies are available. NVIDIA GPUs are used for scientific visualization, machine learning, and other graphics processing. Capabilities include RDMA, high performance storage and network traffic isolation. Oracle Cloud Infrastructure consistently delivers better pricing performance than other cloud providers. Virtual machine-based (VM), shapes allow for custom core and memory combinations. Customers can choose a number of cores to optimize their costs.

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.

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.

Banana was founded to fill a critical market gap. Machine learning is highly demanded. But deploying models in production is a highly technical and complex process. Banana focuses on building machine learning infrastructures for the digital economy. We simplify the deployment process, making it as easy as copying and paste an API. This allows companies of any size to access and use the most up-to-date models. We believe the democratization and accessibility of machine learning is one of the key components that will fuel the growth of businesses on a global level. Banana is well positioned to take advantage of this technological gold rush.

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.

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.

You can deploy Mystic in your own Azure/AWS/GCP accounts or in our shared GPU cluster. All Mystic features can be accessed directly from your cloud. In just a few steps, you can get the most cost-effective way to run ML inference. Our shared cluster of graphics cards is used by hundreds of users at once. Low cost, but performance may vary depending on GPU availability in real time. We solve the infrastructure problem. A Kubernetes platform fully managed that runs on your own cloud. Open-source Python API and library to simplify your AI workflow. You get a platform that is high-performance to serve your AI models. Mystic will automatically scale GPUs up or down based on the number API calls that your models receive. You can easily view and edit your infrastructure using the Mystic dashboard, APIs, and CLI.

Virtualization Software for AI Infrastructure. Increase GPU utilization by having visibility and control over AI workloads. Run:AI has created the first virtualization layer in the world for deep learning training models. Run:AI abstracts workloads from the underlying infrastructure and creates a pool of resources that can dynamically provisioned. This allows for full utilization of costly GPU resources. You can control the allocation of costly GPU resources. The scheduling mechanism in Run:AI allows IT to manage, prioritize and align data science computing requirements with business goals. IT has full control over GPU utilization thanks to Run:AI's advanced monitoring tools and queueing mechanisms. IT leaders can visualize their entire infrastructure capacity and utilization across sites by creating a flexible virtual pool of compute resources.

We designed a container system in rust from scratch for the fastest cold start times. Scale up to hundreds of GPUs in seconds and down to zero again, paying only for what you need. Deploy functions in the cloud with custom container images, and hardware requirements. Never write a line of YAML. Modal offers up to $25k in free compute credits for startups and academic researchers. These credits can be used to access GPU compute and in-demand GPU types. Modal measures CPU utilization continuously by comparing the number of physical cores to the number of fractional cores. Each physical core is equal to 2 vCPUs. Memory consumption is continuously measured. You only pay for the memory and CPU you actually use.

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.

There are options for every business to train deep and machine learning models efficiently. There are AI accelerators that can be used for any purpose, from low-cost inference to high performance training. It is easy to get started with a variety of services for development or deployment. Tensor Processing Units are ASICs that are custom-built to train and execute deep neural network. You can train and run more powerful, accurate models at a lower cost and with greater speed and scale. NVIDIA GPUs are available to assist with cost-effective inference and scale-up/scale-out training. Deep learning can be achieved by leveraging RAPID and Spark with GPUs. You can run GPU workloads on Google Cloud, which offers industry-leading storage, networking and data analytics technologies. Compute Engine allows you to access CPU platforms when you create a VM instance. Compute Engine provides a variety of Intel and AMD processors to support your VMs.

The RAPIDS software library, which is built on CUDAX AI, allows you to run end-to-end data science pipelines and analytics entirely on GPUs. It uses NVIDIA®, CUDA®, primitives for low level compute optimization. However, it exposes GPU parallelism through Python interfaces and high-bandwidth memories speed through user-friendly Python interfaces. RAPIDS also focuses its attention on data preparation tasks that are common for data science and analytics. This includes a familiar DataFrame API, which integrates with a variety machine learning algorithms for pipeline accelerations without having to pay serialization fees. RAPIDS supports multi-node, multiple-GPU deployments. This allows for greatly accelerated processing and training with larger datasets. You can accelerate your Python data science toolchain by making minimal code changes and learning no new tools. Machine learning models can be improved by being more accurate and deploying them faster.

Open your own high-performance PC on any device, anywhere, anytime. With up to 96x2.8 Ghz and 1360GB of RAM, 16x NVIDIA 80GB, you can perform smoothly. You can increase the storage space or computer specs to suit your needs. We keep things simple so you can concentrate on what is really important - your project. {Pick one of our plans, depending if you want to use the Cloud PC individually or in a team.|Choose from our plans depending on whether you want to use Cloud PC as an individual or in a group.} Choose the hardware configuration you want to use. You can work on your Cloud Desktop in your browser or desktop app, wherever you are. Renderro Cloud Storage allows you to store all of your best designs and resources in one place. Cloud Storage is scalable. This means that you are not restricted by the size of your files and can manage the storage at any time. Cloud Drives can also be shared among multiple Cloud Desktops. This allows you to switch between machines without having to transfer media.

All products include bandwidth and are typically 70 to 90 percent cheaper than similar products on the market. Our team is 100% US-based. Independent hosts run our hypervisor to operate the servers. Cloud that is flexible, resilient, scalable and secure for burstable workloads. Clouds up to 70% cheaper than existing clouds Secure servers at low cost for monthly or longer term contracts. ML inference). Integrating with our customers' technology stacks is an important part of our business. Well-documented, well-maintained, well-everything.

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.

Persistence container technology allows for lightweight operation. Pay-per-use in just seconds, not hours or months. The next month, fees will be paid via credit card. Low price for high performance. Oak Ridge National Laboratory will install it in the fastest supercomputer in the world. Machine learning applications such as deep learning, computational fluid dynamic, video encoding and 3D graphics workstations, 3D renderings, VFXs, computational finance, seismic analyses, molecular modelling, genomics, and server-side GPU computing workloads.

GPU cloud computing is a GPU computing service that offers real-time, high speed parallel computing with floating-point computing capability. It is suitable for a variety of scenarios, including 3D graphics, video decoding and deep learning. The GPU instances can be managed with ease and speed, just like an ECS. This relieves the computing pressure. RTX6000 GPU has thousands of computing units, which gives it a significant advantage in parallel computing. Massive computing can be completed quickly for optimized deep learning. GPU Direct supports the seamless transmission of big data between networks. It has a built-in acceleration framework that allows it to focus on core tasks through quick deployment and instance distribution. We offer transparent pricing and optimal cloud performance. Our cloud solution has an open and cost-effective price. You can choose to charge resources on demand, and get additional discounts by subscribing.

We listened to our customers and have lowered the prices of our virtual and bare metal servers. Same power and flexibility. A graphics processing unit is the "extra brainpower" that a CPU lacks. IBM Cloud®, for your GPU needs, gives you direct access one of the most flexible server selection processes in the industry. It also integrates seamlessly with your IBM Cloud architecture and APIs, applications and a global distributed network of data centres. IBM Cloud Bare Metal Servers equipped with GPUs outperform AWS servers on 5 TensorFlow models. We offer virtual server GPUs as well as bare metal GPUs. Google Cloud only offers virtual servers instances. Alibaba Cloud offers virtual machines only with GPUs, just like Google Cloud.

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.

Cloud GPU servers are a type cloud computing service which provides access to remote servers equipped with Graphics Processing Units. These GPUs are designed for complex, high-speed parallel computations. They perform at a rate much faster than conventional central processor units (CPUs). NVIDIA K40 and K80 GPU models are available. The GPUs offer a variety of computing options to meet your business needs. Nvidia GPU cloud server allows designers to quickly iterate as rendering time is reduced. Your team's productivity will increase significantly if you invest your time in innovation instead of rendering or computing. Data security is ensured by fully isolating the resources allocated to each user. GPU Mart protects from DDoS at the edge while ensuring that legitimate traffic to Nvidia GPU cloud servers is not compromised.

NVIDIA AI Enterprise is the software layer of NVIDIA AI Platform. It accelerates the data science pipeline, streamlines development and deployments of production AI including generative AI, machine vision, speech AI, and more. NVIDIA AI Enterprise has over 50 frameworks, pre-trained models, and development tools. It is designed to help enterprises get to the forefront of AI while simplifying AI to make it more accessible to all. Artificial intelligence and machine learning are now mainstream and a key part of every company's competitive strategy. Enterprises face the greatest challenges when it comes to managing siloed infrastructure in the cloud and on-premises. AI requires that their environments be managed as a single platform and not as isolated clusters of compute.

Cloud GPU Service provides GPU computing power and high-performance parallel computing. It is a powerful tool for the IaaS layer that delivers high computing power to deep learning training, scientific computation, graphics and image processors, video encoding/decoding, and other intensive workloads. Improve your business efficiency with high-performance parallel processing. Install your deployment environment quickly using preinstalled driver and GPU images, CUDA and cuDNN, and auto-installed GPU and CUDA drivers. TACO Kit is a computing acceleration engine that Tencent Cloud provides to accelerate distributed training and inference.

Cyfuture Cloud is a top cloud service provider offering reliable, scalable, and secure cloud solutions. With a focus on innovation and customer satisfaction, Cyfuture Cloud provides a wide range of services, including public, private, and hybrid cloud solutions, cloud storage, GPU cloud server, and disaster recovery. One of the key offering of Cyfuture Cloud include GPU cloud server. These servers are perfect for intensive tasks like artificial intelligence, machine learning, and big data analytics. The platform offers various tools and services for building and deploying machine learning and other GPU-accelerated applications. Moreover, Cyfuture Cloud helps businesses process complex data sets faster and more accurately, keeping them ahead of the competition. With robust infrastructure, expert support, and flexible pricing--Cyfuture Cloud is the ideal choice for businesses looking to leverage cloud computing for growth and innovation.

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.

Our high-throughput systems can serve millions small random files to GPU based training servers, accelerating the overall training time. We offer high-bandwidth networks with low latency for connecting training servers and transporting data from storage to servers. You may be charged extra fees by other cloud providers to remove your data from their storage clouds. These charges can quickly add up. We consider ourselves as an extension of your team. We help you set up scheduling, provide best practices and superior support. Workflows vary from one company to another. Cirrascale will work with you to find the best solution for you. Cirrascale works with you to customize your cloud instances in order to improve performance, remove bottlenecks and optimize your workflow. Cloud-based solutions that accelerate your training, simulation and re-simulation times.

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.

With just one click, you can access the global GPU resources. Instant access to a global network GPUs and CPUs. Spend much less on GPU computing than you would if you were to use the major public clouds, or buy your own servers. Engage with the cloud, customize your choice, and deploy in a matter seconds. You will be refunded if you terminate your cluster. You can also choose between cost and performance. With io.net you can turn your GPU into an income-generating machine. Our simple platform allows you rent out your GPU. Profitable, transparent and simple. Join the largest network of GPU Clusters in the world and earn sky-high returns. Earn much more with your GPU compute than even the best crypto mining pool. You will always know how much money you'll earn and when the job is complete, you'll be paid. The more you invest into your infrastructure, your returns will be higher.

Each cloud compute provider has their own interface, naming convention and pricing systems that make direct comparison shopping difficult. Vendor lock-in further entrenches higher pricing once you select a single vendor. VAST’s search interface allows for fair comparison from all kinds of providers, from hobbyists to Tier 4 data centers. Start saving 4-6X today and get setup on a single interface that connects you to a VAST marketplace.

The dawn of the Fourth Industrial Revolution (4IR) heralds a significant transformation in the way humans interact with technology. This era is characterized by a fusion of technologies that blur the lines between the physical, digital, and biological spheres. Unlike the previous industrial revolutions, which were driven by advancements such as steam power, electricity, and computing, the 4IR is propelled by a constellation of emerging technologies, among which Artificial Intelligence (AI) stands at the forefront. AI, in its essence, represents machines’ ability to perform tasks that typically require human intelligence. This includes problem-solving, recognizing patterns, understanding natural language, and learning from experience. As we delve deeper into the 4IR, AI’s role as a key driver of innovation and transformation becomes increasingly evident. This paper aims to explore the intricate tapestry of AI in the context of the 4IR, dissecting its impacts, the challenges it presents, and the boundless potential it holds for the future.

OVHcloud gives technologists and businesses complete control, allowing them to start their own business. We are a global technology company that provides developers, entrepreneurs, and businesses with dedicated software, infrastructure, and server building blocks to manage, scale, and secure their data. We have always challenged the status-quo and strived to make technology affordable and accessible throughout our history. We believe that an open ecosystem and open cloud is essential to our future in today's digital world. This will allow all to flourish and customers to choose how, when, and where they want to manage their data. We are a trusted global company with more than 1.5 million customers. We manufacture servers, manage 30 datacenters, as well as operate our own fiber-optic network. We are open to powering your data with our products, support, thriving ecosystem, and passionate employees.

Cloudalize GPU-powered solutions provide flexibility, security, and agility for IIoT, Machine Learning and remote working. Cloudalize offers a wide range of GPU-powered Cloud solutions to help your business realize its true potential. Cloudalize's GPU-powered Desktop-as-a-Service solution (DaaS), allows you to design and render whatever you want using a wide range of professional software from the vendors you prefer. Our DaaS solution is quick to boot and allows companies to work remotely and collaborate from anywhere. It offers unparalleled processing power and is a highly efficient way to keep your operations running smoothly and without risk. Cloudalize's GPU-powered DaaS solution is ideal for small and medium enterprises/businesses, as well as larger organisations with thousands of users.

NVIDIA Triton™, an inference server, delivers fast and scalable AI production-ready. Open-source inference server software, Triton inference servers streamlines AI inference. It allows teams to deploy trained AI models from any framework (TensorFlow or NVIDIA TensorRT®, PyTorch or ONNX, XGBoost or Python, custom, and more on any GPU or CPU-based infrastructure (cloud or data center, edge, or edge). Triton supports concurrent models on GPUs to maximize throughput. It also supports x86 CPU-based inferencing and ARM CPUs. Triton is a tool that developers can use to deliver high-performance inference. It integrates with Kubernetes to orchestrate and scale, exports Prometheus metrics and supports live model updates. Triton helps standardize model deployment in production.

AWS Trainium, the second-generation machine-learning (ML) accelerator, is specifically designed by AWS for deep learning training with 100B+ parameter model. Each Amazon Elastic Comput Cloud (EC2) Trn1 example deploys up to sixteen AWS Trainium accelerations to deliver a low-cost, high-performance solution for deep-learning (DL) in the cloud. The use of deep-learning is increasing, but many development teams have fixed budgets that limit the scope and frequency at which they can train to improve their models and apps. Trainium based EC2 Trn1 instance solves this challenge by delivering a faster time to train and offering up to 50% savings on cost-to-train compared to comparable Amazon EC2 instances.