Alternatives to Bright for Deep Learning
Compare Bright for Deep Learning alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Bright for Deep Learning in 2024. Compare features, ratings, user reviews, pricing, and more from Bright for Deep Learning competitors and alternatives in order to make an informed decision for your business.
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Rocky Linux
Ctrl IQ, Inc.
CIQ empowers people to do amazing things by providing innovative and stable software infrastructure solutions for all computing needs. From the base operating system, through containers, orchestration, provisioning, computing, and cloud applications, CIQ works with every part of the technology stack to drive solutions for customers and communities with stable, scalable, secure production environments. CIQ is the founding support and services partner of Rocky Linux, and the creator of the next generation federated computing stack. - Rocky Linux, open, Secure Enterprise Linux - Apptainer, application Containers for High Performance Computing - Warewulf, cluster Management and Operating System Provisioning - HPC2.0, the Next Generation of High Performance Computing, a Cloud Native Federated Computing Platform - Traditional HPC, turnkey computing stack for traditional HPC -
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NVIDIA NGC
NVIDIA
NVIDIA GPU Cloud (NGC) is a GPU-accelerated cloud platform optimized for deep learning and scientific computing. NGC manages a catalog of fully integrated and optimized deep learning framework containers that take full advantage of NVIDIA GPUs in both single GPU and multi-GPU configurations. NVIDIA train, adapt, and optimize (TAO) is an AI-model-adaptation platform that simplifies and accelerates the creation of enterprise AI applications and services. By fine-tuning pre-trained models with custom data through a UI-based, guided workflow, enterprises can produce highly accurate models in hours rather than months, eliminating the need for large training runs and deep AI expertise. Looking to get started with containers and models on NGC? This is the place to start. Private Registries from NGC allow you to secure, manage, and deploy your own assets to accelerate your journey to AI. -
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NVIDIA GPU-Optimized AMI
Amazon
The NVIDIA GPU-Optimized AMI is a virtual machine image for accelerating your GPU accelerated Machine Learning, Deep Learning, Data Science and HPC workloads. Using this AMI, you can spin up a GPU-accelerated EC2 VM instance in minutes with a pre-installed Ubuntu OS, GPU driver, Docker and NVIDIA container toolkit. This AMI provides easy access to NVIDIA's NGC Catalog, a hub for GPU-optimized software, for pulling & running performance-tuned, tested, and NVIDIA certified docker containers. The NGC catalog provides free access to containerized AI, Data Science, and HPC applications, pre-trained models, AI SDKs and other resources to enable data scientists, developers, and researchers to focus on building and deploying solutions. This GPU-optimized AMI is free with an option to purchase enterprise support offered through NVIDIA AI Enterprise. For how to get support for this AMI, scroll down to 'Support Information'Starting Price: $3.06 per hour -
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ONTAP AI
NetApp
D-I-Y has its place, like weed control. Building out your AI infrastructure is another story. ONTAP AI consolidates a data center’s worth of analytics, training, and inference compute into a single, 5-petaflop AI system. Powered by NVIDIA DGX™ systems and NetApp cloud-connected all-flash storage, NetApp ONTAP AI helps you fully realize the promise of AI and deep learning (DL). You can simplify, accelerate, and integrate your data pipeline with the ONTAP AI proven architecture. Streamline the flow of data reliably and speed up analytics, training, and inference with your data fabric that spans from edge to core to cloud. NetApp ONTAP AI is one of the first converged infrastructure stacks to incorporate NVIDIA DGX A100, the world’s first 5-petaflop AI system, and NVIDIA Mellanox® high-performance Ethernet switches. You get unified AI workloads, simplified deployment, and fast return on investment. -
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Google Cloud GPUs
Google
Speed up compute jobs like machine learning and HPC. A wide selection of GPUs to match a range of performance and price points. Flexible pricing and machine customizations to optimize your workload. High-performance GPUs on Google Cloud for machine learning, scientific computing, and 3D visualization. NVIDIA K80, P100, P4, T4, V100, and A100 GPUs provide a range of compute options to cover your workload for each cost and performance need. Optimally balance the processor, memory, high-performance disk, and up to 8 GPUs per instance for your individual workload. All with the per-second billing, so you only pay only for what you need while you are using it. Run GPU workloads on Google Cloud Platform where you have access to industry-leading storage, networking, and data analytics technologies. Compute Engine provides GPUs that you can add to your virtual machine instances. Learn what you can do with GPUs and what types of GPU hardware are available.Starting Price: $0.160 per GPU -
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AWS Neuron
Amazon Web Services
It supports high-performance training on AWS Trainium-based Amazon Elastic Compute Cloud (Amazon EC2) Trn1 instances. For model deployment, it supports high-performance and low-latency inference on AWS Inferentia-based Amazon EC2 Inf1 instances and AWS Inferentia2-based Amazon EC2 Inf2 instances. With Neuron, you can use popular frameworks, such as TensorFlow and PyTorch, and optimally train and deploy machine learning (ML) models on Amazon EC2 Trn1, Inf1, and Inf2 instances with minimal code changes and without tie-in to vendor-specific solutions. AWS Neuron SDK, which supports Inferentia and Trainium accelerators, is natively integrated with PyTorch and TensorFlow. This integration ensures that you can continue using your existing workflows in these popular frameworks and get started with only a few lines of code changes. For distributed model training, the Neuron SDK supports libraries, such as Megatron-LM and PyTorch Fully Sharded Data Parallel (FSDP). -
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Provision a VM quickly with everything you need to get your deep learning project started on Google Cloud. Deep Learning VM Image makes it easy and fast to instantiate a VM image containing the most popular AI frameworks on a Google Compute Engine instance without worrying about software compatibility. You can launch Compute Engine instances pre-installed with TensorFlow, PyTorch, scikit-learn, and more. You can also easily add Cloud GPU and Cloud TPU support. Deep Learning VM Image supports the most popular and latest machine learning frameworks, like TensorFlow and PyTorch. To accelerate your model training and deployment, Deep Learning VM Images are optimized with the latest NVIDIA® CUDA-X AI libraries and drivers and the Intel® Math Kernel Library. Get started immediately with all the required frameworks, libraries, and drivers pre-installed and tested for compatibility. Deep Learning VM Image delivers a seamless notebook experience with integrated support for JupyterLab.
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Fuzzball
CIQ
Fuzzball accelerates innovation for researchers and scientists by eliminating the burdens of infrastructure provisioning and management. Fuzzball streamlines and optimizes high-performance computing (HPC) workload design and execution. A user-friendly GUI for designing, editing, and executing HPC jobs. Comprehensive control and automation of all HPC tasks via CLI. Automated data ingress and egress with full compliance logs. Native integration with GPUs and both on-prem and cloud storage on-prem and cloud storage. Human-readable, portable workflow files that execute anywhere. CIQ’s Fuzzball modernizes traditional HPC with an API-first, container-optimized architecture. Operating on Kubernetes, it provides all the security, performance, stability, and convenience found in modern software and infrastructure. Fuzzball not only abstracts the infrastructure layer but also automates the orchestration of complex workflows, driving greater efficiency and collaboration. -
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NVIDIA HPC SDK
NVIDIA
The NVIDIA HPC Software Development Kit (SDK) includes the proven compilers, libraries and software tools essential to maximizing developer productivity and the performance and portability of HPC applications. The NVIDIA HPC SDK C, C++, and Fortran compilers support GPU acceleration of HPC modeling and simulation applications with standard C++ and Fortran, OpenACC® directives, and CUDA®. GPU-accelerated math libraries maximize performance on common HPC algorithms, and optimized communications libraries enable standards-based multi-GPU and scalable systems programming. Performance profiling and debugging tools simplify porting and optimization of HPC applications, and containerization tools enable easy deployment on-premises or in the cloud. With support for NVIDIA GPUs and Arm, OpenPOWER, or x86-64 CPUs running Linux, the HPC SDK provides the tools you need to build NVIDIA GPU-accelerated HPC applications. -
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Azure FXT Edge Filer
Microsoft
Create cloud-integrated hybrid storage that works with your existing network-attached storage (NAS) and Azure Blob Storage. This on-premises caching appliance optimizes access to data in your datacenter, in Azure, or across a wide-area network (WAN). A combination of software and hardware, Microsoft Azure FXT Edge Filer delivers high throughput and low latency for hybrid storage infrastructure supporting high-performance computing (HPC) workloads.Scale-out clustering provides non-disruptive NAS performance scaling. Join up to 24 FXT nodes per cluster to scale to millions of IOPS and hundreds of GB/s. When you need performance and scale in file-based workloads, Azure FXT Edge Filer keeps your data on the fastest path to processing resources. Managing data storage is easy with Azure FXT Edge Filer. Shift aging data to Azure Blob Storage to keep it easily accessible with minimal latency. Balance on-premises and cloud storage. -
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NVIDIA Triton™ inference server delivers fast and scalable AI in production. Open-source inference serving software, Triton inference server streamlines AI inference by enabling teams deploy trained AI models from any framework (TensorFlow, NVIDIA TensorRT®, PyTorch, ONNX, XGBoost, Python, custom and more on any GPU- or CPU-based infrastructure (cloud, data center, or edge). Triton runs models concurrently on GPUs to maximize throughput and utilization, supports x86 and ARM CPU-based inferencing, and offers features like dynamic batching, model analyzer, model ensemble, and audio streaming. Triton helps developers deliver high-performance inference aTriton integrates with Kubernetes for orchestration and scaling, exports Prometheus metrics for monitoring, supports live model updates, and can be used in all major public cloud machine learning (ML) and managed Kubernetes platforms. Triton helps standardize model deployment in production.Starting Price: Free
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HPE Performance Cluster Manager
Hewlett Packard Enterprise
HPE Performance Cluster Manager (HPCM) delivers an integrated system management solution for Linux®-based high performance computing (HPC) clusters. HPE Performance Cluster Manager provides complete provisioning, management, and monitoring for clusters scaling up to Exascale sized supercomputers. The software enables fast system setup from bare-metal, comprehensive hardware monitoring and management, image management, software updates, power management, and cluster health management. Additionally, it makes scaling HPC clusters easier and efficient while providing integration with a plethora of 3rd party tools for running and managing workloads. HPE Performance Cluster Manager reduces the time and resources spent administering HPC systems - lowering total cost of ownership, increasing productivity and providing a better return on hardware investments. -
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Build your deep learning project quickly on Google Cloud: Quickly prototype with a portable and consistent environment for developing, testing, and deploying your AI applications with Deep Learning Containers. These Docker images use popular frameworks and are performance optimized, compatibility tested, and ready to deploy. Deep Learning Containers provide a consistent environment across Google Cloud services, making it easy to scale in the cloud or shift from on-premises. You have the flexibility to deploy on Google Kubernetes Engine (GKE), AI Platform, Cloud Run, Compute Engine, Kubernetes, and Docker Swarm.
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AWS Inferentia
Amazon
AWS Inferentia accelerators are designed by AWS to deliver high performance at the lowest cost for your deep learning (DL) inference applications. The first-generation AWS Inferentia accelerator powers Amazon Elastic Compute Cloud (Amazon EC2) Inf1 instances, which deliver up to 2.3x higher throughput and up to 70% lower cost per inference than comparable GPU-based Amazon EC2 instances. Many customers, including Airbnb, Snap, Sprinklr, Money Forward, and Amazon Alexa, have adopted Inf1 instances and realized its performance and cost benefits. The first-generation Inferentia has 8 GB of DDR4 memory per accelerator and also features a large amount of on-chip memory. Inferentia2 offers 32 GB of HBM2e per accelerator, increasing the total memory by 4x and memory bandwidth by 10x over Inferentia. -
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NVIDIA Base Command Platform
NVIDIA
NVIDIA Base Command™ Platform is a software service for enterprise-class AI training that enables businesses and their data scientists to accelerate AI development. Part of the NVIDIA DGX™ platform, Base Command Platform provides centralized, hybrid control of AI training projects. It works with NVIDIA DGX Cloud and NVIDIA DGX SuperPOD. Base Command Platform, in combination with NVIDIA-accelerated AI infrastructure, provides a cloud-hosted solution for AI development, so users can avoid the overhead and pitfalls of deploying and running a do-it-yourself platform. Base Command Platform efficiently configures and manages AI workloads, delivers integrated dataset management, and executes them on right-sized resources ranging from a single GPU to large-scale, multi-node clusters in the cloud or on-premises. Because NVIDIA’s own engineers and researchers rely on it every day, the platform receives continuous software enhancements. -
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Arm Forge
Arm
Build reliable and optimized code for the right results on multiple Server and HPC architectures, from the latest compilers and C++ standards to Intel, 64-bit Arm, AMD, OpenPOWER, and Nvidia GPU hardware. Arm Forge combines Arm DDT, the leading debugger for time-saving high-performance application debugging, Arm MAP, the trusted performance profiler for invaluable optimization advice across native and Python HPC codes, and Arm Performance Reports for advanced reporting capabilities. Arm DDT and Arm MAP are also available as standalone products. Efficient application development for Linux Server and HPC with Full technical support from Arm experts. Arm DDT is the debugger of choice for developing of C++, C, or Fortran parallel, and threaded applications on CPUs, and GPUs. Its powerful intuitive graphical interface helps you easily detect memory bugs and divergent behavior at all scales, making Arm DDT the number one debugger in research, industry, and academia. -
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AWS Deep Learning AMIs
Amazon
AWS Deep Learning AMIs (DLAMI) provides ML practitioners and researchers with a curated and secure set of frameworks, dependencies, and tools to accelerate deep learning in the cloud. Built for Amazon Linux and Ubuntu, Amazon Machine Images (AMIs) come preconfigured with TensorFlow, PyTorch, Apache MXNet, Chainer, Microsoft Cognitive Toolkit (CNTK), Gluon, Horovod, and Keras, allowing you to quickly deploy and run these frameworks and tools at scale. Develop advanced ML models at scale to develop autonomous vehicle (AV) technology safely by validating models with millions of supported virtual tests. Accelerate the installation and configuration of AWS instances, and speed up experimentation and evaluation with up-to-date frameworks and libraries, including Hugging Face Transformers. Use advanced analytics, ML, and deep learning capabilities to identify trends and make predictions from raw, disparate health data. -
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DataRobot
DataRobot
AI Cloud is a new approach built for the demands, challenges and opportunities of AI today. A single system of record, accelerating the delivery of AI to production for every organization. All users collaborate in a unified environment built for continuous optimization across the entire AI lifecycle. The AI Catalog enables seamlessly finding, sharing, tagging, and reusing data, helping to speed time to production and increase collaboration. The catalog provides easy access to the data needed to answer a business problem while ensuring security, compliance, and consistency. If your database is protected by a network policy that only allows connections from specific IP addresses, contact Support for a list of addresses that an administrator must add to your network policy (whitelist). -
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Intel DevCloud
Intel
Intel® DevCloud offers complimentary access to a wide range of Intel® architectures to help you get instant hands-on experience with Intel® software and execute your edge, AI, high-performance computing (HPC), and rendering workloads. With preinstalled Intel® optimized frameworks, tools, and libraries, you have everything you need to fast-track your learning and project prototyping. Learn, prototype, test, and run your workloads for free on a cluster of the latest Intel® hardware and software. Learn through a new suite of curated experiences, including market vertical samples, Jupyter Notebook tutorials, and more. Build your solution in JupyterLab and test with bare metal or develop your containerized solution. Quickly bring it to Intel DevCloud for testing. Optimize your solution for a specific target edge device with the deep learning workbench and take advantage of the new, more robust telemetry dashboard.Starting Price: Free -
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Intel oneAPI HPC Toolkit
Intel
High-performance computing (HPC) is at the core of AI, machine learning, and deep learning applications. The Intel® oneAPI HPC Toolkit (HPC Kit) delivers what developers need to build, analyze, optimize, and scale HPC applications with the latest techniques in vectorization, multithreading, multi-node parallelization, and memory optimization. This toolkit is an add-on to the Intel® oneAPI Base Toolkit, which is required for full functionality. It also includes access to the Intel® Distribution for Python*, the Intel® oneAPI DPC++/C++ C¿compiler, powerful data-centric libraries, and advanced analysis tools. Get what you need to build, test, and optimize your oneAPI projects for free. With an Intel® Developer Cloud account, you get 120 days of access to the latest Intel® hardware, CPUs, GPUs, FPGAs, and Intel oneAPI tools and frameworks. No software downloads. No configuration steps, and no installations. -
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The Nimbix Supercomputing Suite is a set of flexible and secure as-a-service high-performance computing (HPC) solutions. This as-a-service model for HPC, AI, and Quantum in the cloud provides customers with access to one of the broadest HPC and supercomputing portfolios, from hardware to bare metal-as-a-service to the democratization of advanced computing in the cloud across public and private data centers. Nimbix Supercomputing Suite allows you access to HyperHub Application Marketplace, our high-performance marketplace with over 1,000 applications and workflows. Leverage powerful dedicated BullSequana HPC servers as bare metal-as-a-service for the best of infrastructure and on-demand scalability, convenience, and agility. Federated supercomputing-as-a-service offers a unified service console to manage all compute zones and regions in a public or private HPC, AI, and supercomputing federation.
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Hyperstack
Hyperstack
Hyperstack is the ultimate self-service, on-demand GPUaaS Platform offering the H100, A100, L40 and more, delivering its services to some of the most promising AI start-ups in the world. Hyperstack is built for enterprise-grade GPU-acceleration and optimised for AI workloads, offering NexGen Cloud’s enterprise-grade infrastructure to a wide spectrum of users, from SMEs to Blue-Chip corporations, Managed Service Providers, and tech enthusiasts. Running on 100% renewable energy and powered by NVIDIA architecture, Hyperstack offers its services at up to 75% more cost-effective than Legacy Cloud Providers. The platform supports a diverse range of high-intensity workloads, such as Generative AI, Large Language Modelling, machine learning, and rendering.Starting Price: $0.18 per GPU per hour -
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ScaleCloud
ScaleMatrix
Data-intensive AI, IoT and HPC workloads requiring multiple parallel processes have always run best on expensive high-end processors or accelerators, such as Graphic Processing Units (GPU). Moreover, when running compute-intensive workloads on cloud-based solutions, businesses and research organizations have had to accept tradeoffs, many of which were problematic. For example, the age of processors and other hardware in cloud environments is often incompatible with the latest applications or high energy expenditure levels that cause concerns related to environmental values. In other cases, certain aspects of cloud solutions have simply been frustrating to deal with. This has limited flexibility for customized cloud environments to support business needs or trouble finding right-size billing models or support. -
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FluidStack
FluidStack
Unlock 3-5x better prices than traditional clouds. FluidStack aggregates under-utilized GPUs from data centers around the world to deliver the industry’s best economics. Deploy 50,000+ high-performance servers in seconds via a single platform and API. Access large-scale A100 and H100 clusters with InfiniBand in days. Train, fine-tune, and deploy LLMs on thousands of affordable GPUs in minutes with FluidStack. FluidStack unites individual data centers to overcome monopolistic GPU cloud pricing. Compute 5x faster while making the cloud efficient. Instantly access 47,000+ unused servers with tier 4 uptime and security from one simple interface. Train larger models, deploy Kubernetes clusters, render quicker, and stream with no latency. Setup in one click with custom images and APIs to deploy in seconds. 24/7 direct support via Slack, emails, or calls, our engineers are an extension of your team.Starting Price: $1.49 per month -
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Options for every business to train deep learning and machine learning models cost-effectively. AI accelerators for every use case, from low-cost inference to high-performance training. Simple to get started with a range of services for development and deployment. Tensor Processing Units (TPUs) are custom-built ASIC to train and execute deep neural networks. Train and run more powerful and accurate models cost-effectively with faster speed and scale. A range of NVIDIA GPUs to help with cost-effective inference or scale-up or scale-out training. Leverage RAPID and Spark with GPUs to execute deep learning. Run GPU workloads on Google Cloud where you have access to industry-leading storage, networking, and data analytics technologies. Access CPU platforms when you start a VM instance on Compute Engine. Compute Engine offers a range of both Intel and AMD processors for your VMs.
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NVIDIA AI Enterprise
NVIDIA
The software layer of the NVIDIA AI platform, NVIDIA AI Enterprise accelerates the data science pipeline and streamlines development and deployment of production AI including generative AI, computer vision, speech AI and more. With over 50 frameworks, pretrained models and development tools, NVIDIA AI Enterprise is designed to accelerate enterprises to the leading edge of AI, while also simplifying AI to make it accessible to every enterprise. The adoption of artificial intelligence and machine learning has gone mainstream, and is core to nearly every company’s competitive strategy. One of the toughest challenges for enterprises is the struggle with siloed infrastructure across the cloud and on-premises data centers. AI requires their environments to be managed as a common platform, instead of islands of compute. -
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Azure CycleCloud
Microsoft
Create, manage, operate, and optimize HPC and big compute clusters of any scale. Deploy full clusters and other resources, including scheduler, compute VMs, storage, networking, and cache. Customize and optimize clusters through advanced policy and governance features, including cost controls, Active Directory integration, monitoring, and reporting. Use your current job scheduler and applications without modification. Give admins full control over which users can run jobs, as well as where and at what cost. Take advantage of built-in autoscaling and battle-tested reference architectures for a wide range of HPC workloads and industries. CycleCloud supports any job scheduler or software stack—from proprietary in-house to open-source, third-party, and commercial applications. Your resource demands evolve over time, and your cluster should, too. With scheduler-aware autoscaling, you can fit your resources to your workload.Starting Price: $0.01 per hour -
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Civo
Civo
Setup should be easy. We've listened to real user feedback from our community to simplify the developer experience. Our billing model has been designed from scratch for cloud-native, only pay for the resources you need, with no surprises. Boost productivity through industry-leading launch times. Accelerate development cycles, innovate, and deliver results faster. Blazing fast, simplified, managed Kubernetes. Host your applications and scale as and when you need them, with 90-second cluster launch times and a free control plane. Enterprise-class compute instances powered by Kubernetes. With multi-region support, DDoS protection, bandwidth pooling, and all the developer tools you need. A fully managed, auto-scaling machine learning environment. No Kubernetes or ML expertise is needed. Effortlessly set up and scale managed databases straight from your Civo dashboard or via our developer API. Scale up and down as you need, only pay for what you use.Starting Price: $250 per month -
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Nebius
Nebius
Training-ready platform with NVIDIA® H100 Tensor Core GPUs. Competitive pricing. Dedicated support. Built for large-scale ML workloads: Get the most out of multihost training on thousands of H100 GPUs of full mesh connection with latest InfiniBand network up to 3.2Tb/s per host. Best value for money: Save at least 50% on your GPU compute compared to major public cloud providers*. Save even more with reserves and volumes of GPUs. Onboarding assistance: We guarantee a dedicated engineer support to ensure seamless platform adoption. Get your infrastructure optimized and k8s deployed. Fully managed Kubernetes: Simplify the deployment, scaling and management of ML frameworks on Kubernetes and use Managed Kubernetes for multi-node GPU training. Marketplace with ML frameworks: Explore our Marketplace with its ML-focused libraries, applications, frameworks and tools to streamline your model training. Easy to use. We provide all our new users with a 1-month trial period.Starting Price: $2.66/hour -
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Run:AI
Run:AI
Virtualization Software for AI Infrastructure. Gain visibility and control over AI workloads to increase GPU utilization. Run:AI has built the world’s first virtualization layer for deep learning training models. By abstracting workloads from underlying infrastructure, Run:AI creates a shared pool of resources that can be dynamically provisioned, enabling full utilization of expensive GPU resources. Gain control over the allocation of expensive GPU resources. Run:AI’s scheduling mechanism enables IT to control, prioritize and align data science computing needs with business goals. Using Run:AI’s advanced monitoring tools, queueing mechanisms, and automatic preemption of jobs based on priorities, IT gains full control over GPU utilization. By creating a flexible ‘virtual pool’ of compute resources, IT leaders can visualize their full infrastructure capacity and utilization across sites, whether on premises or in the cloud. -
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Lambda GPU Cloud
Lambda
Train the most demanding AI, ML, and Deep Learning models. Scale from a single machine to an entire fleet of VMs with a few clicks. Start or scale up your Deep Learning project with Lambda Cloud. Get started quickly, save on compute costs, and easily scale to hundreds of GPUs. Every VM comes preinstalled with the latest version of Lambda Stack, which includes major deep learning frameworks and CUDA® drivers. In seconds, access a dedicated Jupyter Notebook development environment for each machine directly from the cloud dashboard. For direct access, connect via the Web Terminal in the dashboard or use SSH directly with one of your provided SSH keys. By building compute infrastructure at scale for the unique requirements of deep learning researchers, Lambda can pass on significant savings. Benefit from the flexibility of using cloud computing without paying a fortune in on-demand pricing when workloads rapidly increase.Starting Price: $1.25 per hour -
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SynapseAI
Habana Labs
Like our accelerator hardware, was purpose-designed to optimize deep learning performance, efficiency, and most importantly for developers, ease of use. With support for popular frameworks and models, the goal of SynapseAI is to facilitate ease and speed for developers, using the code and tools they use regularly and prefer. In essence, SynapseAI and its many tools and support are designed to meet deep learning developers where you are — enabling you to develop what and how you want. Habana-based deep learning processors, preserve software investments, and make it easy to build new models— for both training and deployment of the numerous and growing models defining deep learning, generative AI and large language models. -
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Mystic
Mystic
With Mystic you can deploy ML in your own Azure/AWS/GCP account or deploy in our shared GPU cluster. All Mystic features are directly in your own cloud. In a few simple steps, you get the most cost-effective and scalable way of running ML inference. Our shared cluster of GPUs is used by 100s of users simultaneously. Low cost but performance will vary depending on real-time GPU availability. Good AI products need good models and infrastructure; we solve the infrastructure part. A fully managed Kubernetes platform that runs in your own cloud. Open-source Python library and API to simplify your entire AI workflow. You get a high-performance platform to serve your AI models. Mystic will automatically scale up and down GPUs depending on the number of API calls your models receive. You can easily view, edit, and monitor your infrastructure from your Mystic dashboard, CLI, and APIs.Starting Price: Free -
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Arm Allinea Studio is a suite of tools for developing server and HPC applications on Arm-based platforms. It contains Arm-specific compilers and libraries, and debug and optimization tools. Arm Performance Libraries provide optimized standard core math libraries for high-performance computing applications on Arm processors. The library routines, which are available through both Fortran and C interfaces. Arm Performance Libraries are built with OpenMP across many BLAS, LAPACK, FFT, and sparse routines in order to maximize your performance in multi-processor environments.
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NVIDIA RAPIDS
NVIDIA
The RAPIDS suite of software libraries, built on CUDA-X AI, gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposes that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces. RAPIDS also focuses on common data preparation tasks for analytics and data science. This includes a familiar DataFrame API that integrates with a variety of machine learning algorithms for end-to-end pipeline accelerations without paying typical serialization costs. RAPIDS also includes support for multi-node, multi-GPU deployments, enabling vastly accelerated processing and training on much larger dataset sizes. Accelerate your Python data science toolchain with minimal code changes and no new tools to learn. Increase machine learning model accuracy by iterating on models faster and deploying them more frequently. -
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GMI Cloud
GMI Cloud
Build your generative AI applications in minutes on GMI GPU Cloud. GMI Cloud is more than bare metal. Train, fine-tune, and infer state-of-the-art models. Our clusters are ready to go with scalable GPU containers and preconfigured popular ML frameworks. Get instant access to the latest GPUs for your AI workloads. Whether you need flexible on-demand GPUs or dedicated private cloud instances, we've got you covered. Maximize GPU resources with our turnkey Kubernetes software. Easily allocate, deploy, and monitor GPUs or nodes with our advanced orchestration tools. Customize and serve models to build AI applications using your data. GMI Cloud lets you deploy any GPU workload quickly and easily, so you can focus on running ML models, not managing infrastructure. Launch pre-configured environments and save time on building container images, installing software, downloading models, and configuring environment variables. Or use your own Docker image to fit your needs.Starting Price: $2.50 per hour -
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Foundry
Foundry
Foundry is a new breed of public cloud, powered by an orchestration platform that makes accessing AI compute as easy as flipping a light switch. Explore the high-impact features of our GPU cloud services designed for maximum performance and reliability. Whether you’re managing training runs, serving clients, or meeting research deadlines. Industry giants have invested for years in infra teams that build sophisticated cluster management and workload orchestration tools to abstract away the hardware. Foundry makes this accessible to everyone else, ensuring that users can reap compute leverage without a twenty-person team at scale. The current GPU ecosystem is first-come, first-serve, and fixed-price. Availability is a challenge in peak times, and so are the puzzling gaps in rates across vendors. Foundry is powered by a sophisticated mechanism design that delivers better price performance than anyone on the market. -
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Arm MAP
Arm
No need to change your code or the way you build it. Profiling for applications running on more than one server and multiple processes. Clear views of bottlenecks in I/O, in computing, in a thread, or in multi-process activity. Deep insight into actual processor instruction types that affect your performance. View memory usage over time to discover high watermarks and changes across the complete memory footprint. Arm MAP is a unique scalable low-overhead profiler, available standalone or as part of the Arm Forge debug and profile suite. It helps server and HPC code developers to accelerate their software by revealing the causes of slow performance. It is used from multicore Linux workstations through to supercomputers. You can profile realistic test cases that you care most about with typically under 5% runtime overhead. The interactive user interface is clear and intuitive, designed for developers and computational scientists. -
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Substrate
Substrate
Substrate is the platform for agentic AI. Elegant abstractions and high-performance components, optimized models, vector database, code interpreter, and model router. Substrate is the only compute engine designed to run multi-step AI workloads. Describe your task by connecting components and let Substrate run it as fast as possible. We analyze your workload as a directed acyclic graph and optimize the graph, for example, merging nodes that can be run in a batch. The Substrate inference engine automatically schedules your workflow graph with optimized parallelism, reducing the complexity of chaining multiple inference APIs. No more async programming, just connect nodes and let Substrate parallelize your workload. Our infrastructure guarantees your entire workload runs in the same cluster, often on the same machine. You won’t spend fractions of a second per task on unnecessary data roundtrips and cross-region HTTP transport.Starting Price: $30 per month -
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Covalent
Agnostiq
Covalent’s serverless HPC architecture allows you to easily scale jobs from your laptop to your HPC/Cloud. Covalent is a Pythonic workflow tool for computational scientists, AI/ML software engineers, and anyone who needs to run experiments on limited or expensive computing resources including quantum computers, HPC clusters, GPU arrays, and cloud services. Covalent enables a researcher to run computation tasks on an advanced hardware platform – such as a quantum computer or serverless HPC cluster – using a single line of code. The latest release of Covalent includes two new feature sets and three major enhancements. True to its modular nature, Covalent now allows users to define custom pre- and post-hooks to electrons to facilitate various use cases from setting up remote environments (using DepsPip) to running custom functions.Starting Price: Free -
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Segmind
Segmind
Segmind provides simplified access to large computing. You can use it to run your high-performance workloads such as Deep learning training or other complex processing jobs. Segmind offers zero-setup environments within minutes and lets your share access with your team members. Segmind's MLOps platform can also be used to manage deep learning projects end-to-end with integrated data storage and experiment tracking. ML engineers are not cloud engineers and cloud infrastructure management is a pain. So, we abstracted away all of it so that your ML team can focus on what they do best, and build models better and faster. Training ML/DL models take time and can get expensive quickly. But with Segmind, you can scale up your compute seamlessly while also reducing your costs by up to 70%, with our managed spot instances. ML managers today don't have a bird's eye view of ML development activities and cost.Starting Price: $5 -
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ClearML
ClearML
ClearML is the leading open source MLOps and AI platform that helps data science, ML engineering, and DevOps teams easily develop, orchestrate, and automate ML workflows at scale. Our frictionless, unified, end-to-end MLOps suite enables users and customers to focus on developing their ML code and automation. ClearML is used by more than 1,300 enterprise customers to develop a highly repeatable process for their end-to-end AI model lifecycle, from product feature exploration to model deployment and monitoring in production. Use all of our modules for a complete ecosystem or plug in and play with the tools you have. ClearML is trusted by more than 150,000 forward-thinking Data Scientists, Data Engineers, ML Engineers, DevOps, Product Managers and business unit decision makers at leading Fortune 500 companies, enterprises, academia, and innovative start-ups worldwide within industries such as gaming, biotech , defense, healthcare, CPG, retail, financial services, among others.Starting Price: $15 -
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Neysa Nebula
Neysa
Nebula allows you to deploy and scale your AI projects quickly, easily and cost-efficiently2 on highly robust, on-demand GPU infrastructure. Train and infer your models securely and easily on the Nebula cloud powered by the latest on-demand Nvidia GPUs and create and manage your containerized workloads through Nebula’s user-friendly orchestration layer. Access Nebula’s MLOps and low-code/no-code engines to build and deploy AI use cases for business teams and to deploy AI-powered applications swiftly and seamlessly with little to no coding. Choose between the Nebula containerized AI cloud, your on-prem environment, or any cloud of your choice. Build and scale AI-enabled business use-cases within a matter of weeks, not months, with the Nebula Unify platform.Starting Price: $0.12 per hour -
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Ansys HPC
Ansys
With the Ansys HPC software suite, you can use today’s multicore computers to perform more simulations in less time. These simulations can be bigger, more complex and more accurate than ever using high-performance computing (HPC). The various Ansys HPC licensing options let you scale to whatever computational level of simulation you require, from single-user or small user group options for entry-level parallel processing up to virtually unlimited parallel capacity. For large user groups, Ansys facilitates highly scalable, multiple parallel processing simulations for the most challenging projects when needed. Apart from parallel computing, Ansys also offers solutions for parametric computing, which enables you to more fully explore the design parameters (size, weight, shape, materials, mechanical properties, etc.) of your product early in the development process. -
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TotalView
Perforce
TotalView debugging software provides the specialized tools you need to quickly debug, analyze, and scale high-performance computing (HPC) applications. This includes highly dynamic, parallel, and multicore applications that run on diverse hardware — from desktops to supercomputers. Improve HPC development efficiency, code quality, and time-to-market with TotalView’s powerful tools for faster fault isolation, improved memory optimization, and dynamic visualization. Simultaneously debug thousands of threads and processes. Purpose-built for multicore and parallel computing, TotalView delivers a set of tools providing unprecedented control over processes and thread execution, along with deep visibility into program states and data. -
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Kombyne
Kombyne
Kombyne™ is an innovative new SaaS high-performance computing (HPC) workflow tool, initially developed for customers in the defense, automotive, and aerospace industries and academic research. It allows users to subscribe to a range of workflow solutions for HPC CFD jobs, from on-the-fly extract generation and rendering to simulation steering. Interactive monitoring and control are also available, all with minimal simulation disruption and no reliance on VTK. The need for large files is eliminated via extract workflows and real-time visualization. An in-transit workflow uses a separate process that quickly receives data from the solver code and performs visualization and analysis without interfering with the running solver. This process, called an endpoint, can directly output extracts, cutting planes or point samples for data science and can render images as well. The Endpoint can also act as a bridge to popular visualization codes. -
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PowerFLOW
Dassault Systèmes
By leveraging our unique, inherently transient Lattice Boltzmann-based physics PowerFLOW CFD solution performs simulations that accurately predict real world conditions. Using the PowerFLOW suite, engineers evaluate product performance early in the design process prior to any prototype being built — when the impact of change is most significant for design and budgets. PowerFLOW imports fully complex model geometry and accurately and efficiently performs aerodynamic, aeroacoustic and thermal management simulations. Automated domain discretization and turbulence modeling with wall treatment eliminates the need for manual volume meshing and boundary layer meshing. Confidently run PowerFLOW simulations using large number of compute cores on common High Performance Computing (HPC) platforms. -
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NVIDIA Modulus
NVIDIA
NVIDIA Modulus is a neural network framework that blends the power of physics in the form of governing partial differential equations (PDEs) with data to build high-fidelity, parameterized surrogate models with near-real-time latency. Whether you’re looking to get started with AI-driven physics problems or designing digital twin models for complex non-linear, multi-physics systems, NVIDIA Modulus can support your work. Offers building blocks for developing physics machine learning surrogate models that combine both physics and data. The framework is generalizable to different domains and use cases—from engineering simulations to life sciences and from forward simulations to inverse/data assimilation problems. Provides parameterized system representation that solves for multiple scenarios in near real time, letting you train once offline to infer in real time repeatedly. -
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AWS Trainium
Amazon Web Services
AWS Trainium is the second-generation Machine Learning (ML) accelerator that AWS purpose built for deep learning training of 100B+ parameter models. Each Amazon Elastic Compute Cloud (EC2) Trn1 instance deploys up to 16 AWS Trainium accelerators to deliver a high-performance, low-cost solution for deep learning (DL) training in the cloud. Although the use of deep learning is accelerating, many development teams are limited by fixed budgets, which puts a cap on the scope and frequency of training needed to improve their models and applications. Trainium-based EC2 Trn1 instances solve this challenge by delivering faster time to train while offering up to 50% cost-to-train savings over comparable Amazon EC2 instances. -
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Ori GPU Cloud
Ori
Launch GPU-accelerated instances highly configurable to your AI workload & budget. Reserve thousands of GPUs in a next-gen AI data center for training and inference at scale. The AI world is shifting to GPU clouds for building and launching groundbreaking models without the pain of managing infrastructure and scarcity of resources. AI-centric cloud providers outpace traditional hyperscalers on availability, compute costs and scaling GPU utilization to fit complex AI workloads. Ori houses a large pool of various GPU types tailored for different processing needs. This ensures a higher concentration of more powerful GPUs readily available for allocation compared to general-purpose clouds. Ori is able to offer more competitive pricing year-on-year, across on-demand instances or dedicated servers. When compared to per-hour or per-usage pricing of legacy clouds, our GPU compute costs are unequivocally cheaper to run large-scale AI workloads.Starting Price: $3.24 per month