GPUonCLOUD Description
In the past, tasks such as deep learning, 3D modeling, simulations, distributed analytics, and molecular modeling could take several days or even weeks to complete. Thanks to GPUonCLOUD’s specialized GPU servers, these processes can now be accomplished in just a few hours. You can choose from a range of pre-configured systems or ready-to-use instances equipped with GPUs that support popular deep learning frameworks like TensorFlow, PyTorch, MXNet, and TensorRT, along with libraries such as the real-time computer vision library OpenCV, all of which enhance your AI/ML model-building journey. Among the diverse selection of GPUs available, certain servers are particularly well-suited for graphics-intensive tasks and multiplayer accelerated gaming experiences. Furthermore, instant jumpstart frameworks significantly boost the speed and flexibility of the AI/ML environment while ensuring effective and efficient management of the entire lifecycle. This advancement not only streamlines workflows but also empowers users to innovate at an unprecedented pace.
GPUonCLOUD Alternatives
Google Compute Engine
Compute Engine (IaaS), a platform from Google that allows organizations to create and manage cloud-based virtual machines, is an infrastructure as a services (IaaS).
Computing infrastructure in predefined sizes or custom machine shapes to accelerate cloud transformation. General purpose machines (E2, N1,N2,N2D) offer a good compromise between price and performance. Compute optimized machines (C2) offer high-end performance vCPUs for compute-intensive workloads. Memory optimized (M2) systems offer the highest amount of memory and are ideal for in-memory database applications. Accelerator optimized machines (A2) are based on A100 GPUs, and are designed for high-demanding applications. Integrate Compute services with other Google Cloud Services, such as AI/ML or data analytics. Reservations can help you ensure that your applications will have the capacity needed as they scale. You can save money by running Compute using the sustained-use discount, and you can even save more when you use the committed-use discount.
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RunPod
RunPod provides a cloud infrastructure that enables seamless deployment and scaling of AI workloads with GPU-powered pods. By offering access to a wide array of NVIDIA GPUs, such as the A100 and H100, RunPod supports training and deploying machine learning models with minimal latency and high performance. The platform emphasizes ease of use, allowing users to spin up pods in seconds and scale them dynamically to meet demand. With features like autoscaling, real-time analytics, and serverless scaling, RunPod is an ideal solution for startups, academic institutions, and enterprises seeking a flexible, powerful, and affordable platform for AI development and inference.
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CoreWeave
CoreWeave stands out as a cloud infrastructure service that focuses on GPU-centric computing solutions specifically designed for artificial intelligence applications. Their platform delivers scalable, high-performance GPU clusters that enhance both training and inference processes for AI models, catering to sectors such as machine learning, visual effects, and high-performance computing. In addition to robust GPU capabilities, CoreWeave offers adaptable storage, networking, and managed services that empower AI-focused enterprises, emphasizing reliability, cost-effectiveness, and top-tier security measures. This versatile platform is widely adopted by AI research facilities, labs, and commercial entities aiming to expedite their advancements in artificial intelligence technology. By providing an infrastructure that meets the specific demands of AI workloads, CoreWeave plays a crucial role in driving innovation across various industries.
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Skyportal
Skyportal is a cloud platform utilizing GPUs specifically designed for AI engineers, boasting a 50% reduction in cloud expenses while delivering 100% GPU performance. By providing an affordable GPU infrastructure tailored for machine learning tasks, it removes the uncertainty of fluctuating cloud costs and hidden charges. The platform features a smooth integration of Kubernetes, Slurm, PyTorch, TensorFlow, CUDA, cuDNN, and NVIDIA Drivers, all finely tuned for Ubuntu 22.04 LTS and 24.04 LTS, enabling users to concentrate on innovation and scaling effortlessly. Users benefit from high-performance NVIDIA H100 and H200 GPUs, which are optimized for ML/AI tasks, alongside instant scalability and round-the-clock expert support from a knowledgeable team adept in ML workflows and optimization strategies. In addition, Skyportal's clear pricing model and absence of egress fees ensure predictable expenses for AI infrastructure. Users are encouraged to communicate their AI/ML project needs and ambitions, allowing them to deploy models within the infrastructure using familiar tools and frameworks while adjusting their infrastructure capacity as necessary. Ultimately, Skyportal empowers AI engineers to streamline their workflows effectively while managing costs efficiently.
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Pricing
Pricing Starts At:
$1 per hour
Integrations
Company Details
Company:
GPUonCLOUD
Headquarters:
India
Website:
gpuoncloud.com/gpu-as-a-service/
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Web-Based
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Training Docs
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Online Support
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