Alternatives to Mystic

Your ML model can be served in minutes in any cloud. Unified model packaging format that allows online and offline delivery on any platform. Our micro-batching technology allows for 100x more throughput than a regular flask-based server model server. High-quality prediction services that can speak the DevOps language, and seamlessly integrate with common infrastructure tools. Unified format for deployment. High-performance model serving. Best practices in DevOps are incorporated. The service uses the TensorFlow framework and the BERT model to predict the sentiment of movie reviews. DevOps-free BentoML workflow. This includes deployment automation, prediction service registry, and endpoint monitoring. All this is done automatically for your team. This is a solid foundation for serious ML workloads in production. Keep your team's models, deployments and changes visible. You can also control access via SSO and RBAC, client authentication and auditing logs.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Burncloud is one of the leading cloud computing providers, focusing on providing businesses with efficient, reliable and secure GPU rental services. Our platform is based on a systemized design that meets the high-performance computing requirements of different enterprises. Core Services Online GPU Rental Services - We offer a wide range of GPU models to rent, including data-center-grade devices and edge consumer computing equipment, in order to meet the diverse computing needs of businesses. Our best-selling products include: RTX4070, RTX3070 Ti, H100PCIe, RTX3090 Ti, RTX3060, NVIDIA4090, L40 RTX3080 Ti, L40S RTX4090, RTX3090, A10, H100 SXM, H100 NVL, A100PCIe 80GB, and many more. Our technical team has a vast experience in IB networking and has successfully set up five 256-node Clusters. Contact the Burncloud customer service team for cluster setup services.

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.

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.

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.

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.

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.

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.

Amazon EC2 UltraClusters allow you to scale up to thousands of GPUs and machine learning accelerators such as AWS trainium, providing access to supercomputing performance on demand. They enable supercomputing to be accessible for ML, generative AI and high-performance computing through a simple, pay-as you-go model, without any setup or maintenance fees. UltraClusters are made up of thousands of accelerated EC2 instance co-located within a specific AWS Availability Zone and interconnected with Elastic Fabric Adapter networking to create a petabit scale non-blocking network. This architecture provides high-performance networking, and access to Amazon FSx, a fully-managed shared storage built on a parallel high-performance file system. It allows rapid processing of large datasets at sub-millisecond latency. EC2 UltraClusters offer scale-out capabilities to reduce training times for distributed ML workloads and tightly coupled HPC workloads.

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.

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.

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.

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.

Elastic Fabric Adapter is a network-interface for Amazon EC2 instances. It allows customers to run applications that require high levels of internode communication at scale. Its custom-built OS bypass hardware interface improves the performance of interinstance communications which is crucial for scaling these applications. EFA allows High-Performance Computing applications (HPC) using the Message Passing Interface, (MPI), and Machine Learning applications (ML) using NVIDIA's Collective Communications Library, (NCCL), to scale up to thousands of CPUs and GPUs. You get the performance of HPC clusters on-premises, with the elasticity and flexibility on-demand of AWS. EFA is a free networking feature available on all supported EC2 instances. Plus, EFA works with the most common interfaces, libraries, and APIs for inter-node communication.

Self-service machine learning platform that allows you to turn models into APIs with just a few mouse clicks. Sign up for a Deep Infra Account using GitHub, or login using GitHub. Choose from hundreds of popular ML models. Call your model using a simple REST API. Our serverless GPUs allow you to deploy models faster and cheaper than if you were to build the infrastructure yourself. Depending on the model, we have different pricing models. Some of our models have token-based pricing. The majority of models are charged by the time it takes to execute an inference. This pricing model allows you to only pay for the services you use. You can easily scale your business as your needs change. There are no upfront costs or long-term contracts. All models are optimized for low latency and inference performance on A100 GPUs. Our system will automatically scale up the model based on your requirements.

Trooper.AI GPU rental service in Europe unlocks AI potential. We offer high-performance GPU servers made from upcycled gaming equipment, providing an eco-friendly, cost-effective solution for machine learning, large language models, and generative AI. Our customized solutions provide up to 328 TFLOPs of power. They are ideal for IT teams who need scalable AI infrastructure. You'll enjoy guaranteed data security, EU-compliance, and exclusive hardware allocation - no shared GPUs. Rent powerful GPUs and join the future of AI. Contact us today to find the perfect server setup for you and start innovating.

Amazon SageMaker makes it easy for you to deploy ML models to make predictions (also called inference) at the best price and performance for your use case. It offers a wide range of ML infrastructure options and model deployment options to meet your ML inference requirements. It integrates with MLOps tools to allow you to scale your model deployment, reduce costs, manage models more efficiently in production, and reduce operational load. Amazon SageMaker can handle all your inference requirements, including low latency (a few seconds) and high throughput (hundreds upon thousands of requests per hour).

Amazon EC2 Inf1 instances were designed to deliver high-performance, cost-effective machine-learning inference. Amazon EC2 Inf1 instances offer up to 2.3x higher throughput, and up to 70% less cost per inference compared with other Amazon EC2 instance. Inf1 instances are powered by up to 16 AWS inference accelerators, designed by AWS. They also feature Intel Xeon Scalable 2nd generation processors, and up to 100 Gbps of networking bandwidth, to support large-scale ML apps. These instances are perfect for deploying applications like search engines, recommendation system, computer vision and speech recognition, natural-language processing, personalization and fraud detection. Developers can deploy ML models to Inf1 instances by using the AWS Neuron SDK. This SDK integrates with popular ML Frameworks such as TensorFlow PyTorch and Apache MXNet.

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.

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.

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.

It supports high-performance learning on AWS Trainium based Amazon Elastic Compute Cloud Trn1 instances. It supports low-latency and high-performance inference for model deployment on AWS Inferentia based Amazon EC2 Inf1 and AWS Inferentia2-based Amazon EC2 Inf2 instance. Neuron allows you to use popular frameworks such as TensorFlow or PyTorch and train and deploy machine-learning (ML) models using Amazon EC2 Trn1, inf1, and inf2 instances without requiring vendor-specific solutions. AWS Neuron SDK is natively integrated into PyTorch and TensorFlow, and supports Inferentia, Trainium, and other accelerators. This integration allows you to continue using your existing workflows within these popular frameworks, and get started by changing only a few lines. The Neuron SDK provides libraries for distributed model training such as Megatron LM and PyTorch Fully Sharded Data Parallel (FSDP).

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.

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.

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.

At competitive prices, you can access dedicated machines that are pre-configured with professional AI development tools. Apolo offers everything from HPC resources to a complete AI platform with a built-in ML toolkit. Apolo is available in a distributed architecture or as a dedicated enterprise cloud. It can also be deployed as a white-label multi-tenant solution that supports dedicated instances or self service cloud. Apolo creates a fully-fledged AI development environment, with all the tools needed at your fingertips. Apolo automates and manages the infrastructure for successful AI development. Apolo's AI services seamlessly integrate your on-prem resources and cloud resources. They also deploy pipelines and integrate your commercial and open-source development tools. Apolo provides enterprises with the resources and tools necessary to achieve breakthroughs when it comes to AI.

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.

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.

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.

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.

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.

Amazon Elastic Compute Cloud's (Amazon EC2) instances P5 powered by NVIDIA Tensor core GPUs and P5e or P5en instances powered NVIDIA Tensor core GPUs provide the best performance in Amazon EC2 when it comes to deep learning and high-performance applications. They can help you accelerate the time to solution up to four times compared to older GPU-based EC2 instance generation, and reduce costs to train ML models up to forty percent. These instances allow you to iterate faster on your solutions and get them to market quicker. You can use P5,P5e,and P5en instances to train and deploy increasingly complex large language and diffusion models that power the most demanding generative artificial intelligent applications. These applications include speech recognition, video and image creation, code generation and question answering. These instances can be used to deploy HPC applications for pharmaceutical discovery.

Fully managed infrastructure, tools and workflows allow you to build, train and deploy models faster. Scale inference and deploy custom AI & LLMs in seconds on any infrastructure. Schedule batch jobs to handle your most demanding tasks, and only pay per second. Optimize costs by utilizing GPUs, spot instances, and automatic failover. YAML simplifies complex infrastructure setups by allowing you to train with a single command. Automate the scaling up of workers during periods of high traffic, and scaling down to zero when inactive. Deploy cutting edge models with persistent endpoints within a serverless environment to optimize resource usage. Monitor system and inference metrics, including worker counts, GPU utilization, throughput, and latency in real-time. Split traffic between multiple models to evaluate.

OctoAI is a world-class computing infrastructure that allows you to run and tune models that will impress your users. Model endpoints that are fast and efficient, with the freedom to run any type of model. OctoAI models can be used or you can bring your own. Create ergonomic model endpoints within minutes with just a few lines code. Customize your model for any use case that benefits your users. You can scale from zero users to millions without worrying about hardware, speed or cost overruns. Use our curated list to find the best open-source foundations models. We've optimized them for faster and cheaper performance using our expertise in machine learning compilation and acceleration techniques. OctoAI selects the best hardware target and applies the latest optimization techniques to keep your running models optimized.

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.

Amazon SageMaker Model training reduces the time and costs of training and tuning machine learning (ML), models at scale, without the need for infrastructure management. SageMaker automatically scales infrastructure up or down from one to thousands of GPUs. This allows you to take advantage of the most performant ML compute infrastructure available. You can control your training costs better because you only pay for what you use. SageMaker distributed libraries can automatically split large models across AWS GPU instances. You can also use third-party libraries like DeepSpeed, Horovod or Megatron to speed up deep learning models. You can efficiently manage your system resources using a variety of GPUs and CPUs, including P4d.24xl instances. These are the fastest training instances available in the cloud. Simply specify the location of the data and indicate the type of SageMaker instances to get started.

Amazon SageMaker JumpStart can help you speed up your machine learning (ML). SageMaker JumpStart gives you access to pre-trained foundation models, pre-trained algorithms, and built-in algorithms to help you with tasks like article summarization or image generation. You can also access prebuilt solutions to common problems. You can also share ML artifacts within your organization, including notebooks and ML models, to speed up ML model building. SageMaker JumpStart offers hundreds of pre-trained models from model hubs such as TensorFlow Hub and PyTorch Hub. SageMaker Python SDK allows you to access the built-in algorithms. The built-in algorithms can be used to perform common ML tasks such as data classifications (images, text, tabular), and sentiment analysis.