Alternatives to Google Cloud GPUs
Compare Google Cloud GPUs alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Google Cloud GPUs in 2025. Compare features, ratings, user reviews, pricing, and more from Google Cloud GPUs competitors and alternatives in order to make an informed decision for your business.
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Elastic GPU Service
Alibaba
Elastic computing instances with GPU computing accelerators suitable for scenarios (such as artificial intelligence (specifically deep learning and machine learning), high-performance computing, and professional graphics processing). Elastic GPU Service provides a complete service system that combines software and hardware to help you flexibly allocate resources, elastically scale your system, improve computing power, and lower the cost of your AI-related business. It applies to scenarios (such as deep learning, video encoding and decoding, video processing, scientific computing, graphical visualization, and cloud gaming). Elastic GPU Service provides GPU-accelerated computing capabilities and ready-to-use, scalable GPU computing resources. GPUs have unique advantages in performing mathematical and geometric computing, especially floating-point and parallel computing. GPUs provide 100 times the computing power of their CPU counterparts.Starting Price: $69.51 per month -
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We listened and lowered our bare metal and virtual server prices. Same power and flexibility. A graphics processing unit (GPU) is “extra brain power” the CPU lacks. Choosing IBM Cloud® for your GPU requirements gives you direct access to one of the most flexible server-selection processes in the industry, seamless integration with your IBM Cloud architecture, APIs and applications, and a globally distributed network of data centers. IBM Cloud Bare Metal Servers with GPUs perform better on 5 TensorFlow ML models than AWS servers. We offer bare metal GPUs and virtual server GPUs. Google Cloud only offers virtual server instances. Like Google Cloud, Alibaba Cloud only offers GPU options on virtual machines.
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RunPod
RunPod
RunPod is on a mission to democratize AI from the ground up. Our first goal is to bring cloud compute to all with very competitive prices without sacrificing usability, experience, or features. Secure Cloud runs in T3/T4 data centers by our trusted partners. Our close partnership comes with high reliability, redundancy, security, and fast response times to mitigate any downtimes. For any sensitive and enterprise workloads, we highly recommend Secure Cloud.Starting Price: $0.20 per hour -
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Tencent Cloud GPU Service
Tencent
Cloud GPU Service is an elastic computing service that provides GPU computing power with high-performance parallel computing capabilities. As a powerful tool at the IaaS layer, it delivers high computing power for deep learning training, scientific computing, graphics and image processing, video encoding and decoding, and other highly intensive workloads. Improve your business efficiency and competitiveness with high-performance parallel computing capabilities. Set up your deployment environment quickly with auto-installed GPU drivers, CUDA, and cuDNN and preinstalled driver images. Accelerate distributed training and inference by using TACO Kit, an out-of-the-box computing acceleration engine provided by Tencent Cloud.Starting Price: $0.204/hour -
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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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AWS Elastic Fabric Adapter (EFA)
United States
Elastic Fabric Adapter (EFA) is a network interface for Amazon EC2 instances that enables customers to run applications requiring high levels of inter-node communications at scale on AWS. Its custom-built operating system (OS) bypass hardware interface enhances the performance of inter-instance communications, which is critical to scaling these applications. With EFA, High-Performance Computing (HPC) applications using the Message Passing Interface (MPI) and Machine Learning (ML) applications using NVIDIA Collective Communications Library (NCCL) can scale to thousands of CPUs or GPUs. As a result, you get the application performance of on-premises HPC clusters with the on-demand elasticity and flexibility of the AWS cloud. EFA is available as an optional EC2 networking feature that you can enable on any supported EC2 instance at no additional cost. Plus, it works with the most commonly used interfaces, APIs, and libraries for inter-node communications. -
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Amazon EC2 UltraClusters
Amazon
Amazon EC2 UltraClusters enable you to scale to thousands of GPUs or purpose-built machine learning accelerators, such as AWS Trainium, providing on-demand access to supercomputing-class performance. They democratize supercomputing for ML, generative AI, and high-performance computing developers through a simple pay-as-you-go model without setup or maintenance costs. UltraClusters consist of thousands of accelerated EC2 instances co-located in a given AWS Availability Zone, interconnected using Elastic Fabric Adapter (EFA) networking in a petabit-scale nonblocking network. This architecture offers high-performance networking and access to Amazon FSx for Lustre, a fully managed shared storage built on a high-performance parallel file system, enabling rapid processing of massive datasets with sub-millisecond latencies. EC2 UltraClusters provide scale-out capabilities for distributed ML training and tightly coupled HPC workloads, reducing training times. -
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Amazon EC2 P4 Instances
Amazon
Amazon EC2 P4d instances deliver high performance for machine learning training and high-performance computing applications in the cloud. Powered by NVIDIA A100 Tensor Core GPUs, they offer industry-leading throughput and low-latency networking, supporting 400 Gbps instance networking. P4d instances provide up to 60% lower cost to train ML models, with an average of 2.5x better performance for deep learning models compared to previous-generation P3 and P3dn instances. Deployed in hyperscale clusters called Amazon EC2 UltraClusters, P4d instances combine high-performance computing, networking, and storage, enabling users to scale from a few to thousands of NVIDIA A100 GPUs based on project needs. Researchers, data scientists, and developers can utilize P4d instances to train ML models for use cases such as natural language processing, object detection and classification, and recommendation engines, as well as to run HPC applications like pharmaceutical discovery and more.Starting Price: $11.57 per hour -
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Amazon EC2 P5 Instances
Amazon
Amazon Elastic Compute Cloud (Amazon EC2) P5 instances, powered by NVIDIA H100 Tensor Core GPUs, and P5e and P5en instances powered by NVIDIA H200 Tensor Core GPUs deliver the highest performance in Amazon EC2 for deep learning and high-performance computing applications. They help you accelerate your time to solution by up to 4x compared to previous-generation GPU-based EC2 instances, and reduce the cost to train ML models by up to 40%. These instances help you iterate on your solutions at a faster pace and get to market more quickly. You can use P5, P5e, and P5en instances for training and deploying increasingly complex large language models and diffusion models powering the most demanding generative artificial intelligence applications. These applications include question-answering, code generation, video and image generation, and speech recognition. You can also use these instances to deploy demanding HPC applications at scale for pharmaceutical discovery. -
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Amazon EC2 Capacity Blocks for ML enable you to reserve accelerated compute instances in Amazon EC2 UltraClusters for your machine learning workloads. This service supports Amazon EC2 P5en, P5e, P5, and P4d instances, powered by NVIDIA H200, H100, and A100 Tensor Core GPUs, respectively, as well as Trn2 and Trn1 instances powered by AWS Trainium. You can reserve these instances for up to six months in cluster sizes ranging from one to 64 instances (512 GPUs or 1,024 Trainium chips), providing flexibility for various ML workloads. Reservations can be made up to eight weeks in advance. By colocating in Amazon EC2 UltraClusters, Capacity Blocks offer low-latency, high-throughput network connectivity, facilitating efficient distributed training. This setup ensures predictable access to high-performance computing resources, allowing you to plan ML development confidently, run experiments, build prototypes, and accommodate future surges in demand for ML applications.
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Amazon EC2 G5 Instances
Amazon
Amazon EC2 G5 instances are the latest generation of NVIDIA GPU-based instances that can be used for a wide range of graphics-intensive and machine-learning use cases. They deliver up to 3x better performance for graphics-intensive applications and machine learning inference and up to 3.3x higher performance for machine learning training compared to Amazon EC2 G4dn instances. Customers can use G5 instances for graphics-intensive applications such as remote workstations, video rendering, and gaming to produce high-fidelity graphics in real time. With G5 instances, machine learning customers get high-performance and cost-efficient infrastructure to train and deploy larger and more sophisticated models for natural language processing, computer vision, and recommender engine use cases. G5 instances deliver up to 3x higher graphics performance and up to 40% better price performance than G4dn instances. They have more ray tracing cores than any other GPU-based EC2 instance.Starting Price: $1.006 per hour -
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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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Runyour AI
Runyour AI
From renting machines for AI research to specialized templates and servers, Runyour AI provides the optimal environment for artificial intelligence research. Runyour AI is an AI cloud service that provides easy access to GPU resources and research environments for artificial intelligence research. You can rent various high-performance GPU machines and environments at a reasonable price. Additionally, you can register your own GPUs to generate revenue. Transparent billing policy where you pay for charging points used through minute-by-minute real-time monitoring. From casual hobbyists to seasoned researchers, we provide specialized GPUs for AI projects, catering to a range of needs. An AI project environment that is easy and convenient for even first-time users. By utilizing Runyour AI's GPU machines, you can kickstart your AI research with minimal setup. Designed for quick access to GPUs, it provides a seamless research environment for machine learning and AI development. -
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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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Oracle Cloud Infrastructure provides fast, flexible, and affordable compute capacity to fit any workload need from performant bare metal servers and VMs to lightweight containers. OCI Compute provides uniquely flexible VM and bare metal instances for optimal price-performance. Select exactly the number of cores and the memory your applications need. Delivering high performance for enterprise workloads. Simplify application development with serverless computing. Your choice of technologies includes Kubernetes and containers. NVIDIA GPUs for machine learning, scientific visualization, and other graphics processing. Capabilities such as RDMA, high-performance storage, and network traffic isolation. Oracle Cloud Infrastructure consistently delivers better price performance than other cloud providers. Virtual machine-based (VM) shapes offer customizable core and memory combinations. Customers can optimize costs by choosing a specific number of cores.Starting Price: $0.007 per hour
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Oblivus
Oblivus
Our infrastructure is equipped to meet your computing requirements, be it one or thousands of GPUs, or one vCPU to tens of thousands of vCPUs, we've got you covered. Our resources are readily available to cater to your needs, whenever you need them. Switching between GPU and CPU instances is a breeze with our platform. You have the flexibility to deploy, modify, and rescale your instances according to your needs, without any hassle. Outstanding machine learning performance without breaking the bank. The latest technology at a significantly lower cost. Cutting-edge GPUs are designed to meet the demands of your workloads. Gain access to computational resources that are tailored to suit the intricacies of your models. Leverage our infrastructure to perform large-scale inference and access necessary libraries with our OblivusAI OS. Unleash the full potential of your gaming experience by utilizing our robust infrastructure to play games in the settings of your choice.Starting Price: $0.29 per hour -
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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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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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Nscale
Nscale
Nscale is the Hyperscaler engineered for AI, offering high-performance computing optimized for training, fine-tuning, and intensive workloads. From our data centers to our software stack, we are vertically integrated in Europe to provide unparalleled performance, efficiency, and sustainability. Access thousands of GPUs tailored to your requirements using our AI cloud platform. Reduce costs, grow revenue, and run your AI workloads more efficiently on a fully integrated platform. Whether you're using Nscale's built-in AI/ML tools or your own, our platform is designed to simplify the journey from development to production. The Nscale Marketplace offers users access to various AI/ML tools and resources, enabling efficient and scalable model development and deployment. Serverless allows seamless, scalable AI inference without the need to manage infrastructure. It automatically scales to meet demand, ensuring low latency and cost-effective inference for popular generative AI models. -
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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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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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NeevCloud
NeevCloud
NeevCloud delivers cutting-edge GPU cloud solutions powered by NVIDIA GPUs like the H200, H100, GB200 NVL72, and many more offering unmatched performance for AI, HPC, and data-intensive workloads. Scale dynamically with flexible pricing and energy-efficient GPUs that reduce costs while maximizing output. Ideal for AI model training, scientific research, media production, and real-time analytics, NeevCloud ensures seamless integration and global accessibility. Experience unparalleled speed, scalability, and sustainability with NeevCloud GPU cloud solutions.Starting Price: $1.69/GPU/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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Burncloud
Burncloud
Burncloud is a leading cloud computing service provider focused on delivering efficient, reliable, and secure GPU rental solutions for businesses. Our platform operates on a systemized model designed to meet the high-performance computing needs of various enterprises. Core Services Online GPU Rental Services: We offer a variety of GPU models for rent, including data center-grade devices and edge consumer-level computing equipment, to meet the diverse computational needs of businesses. Our best-selling products currently include: RTX 4070, RTX 3070 Ti, H100 PCIe, RTX 3090 Ti, RTX 3060, NVIDIA 4090, L40, RTX 3080 Ti, L40S, RTX 4090, RTX 3090, A10, H100 SXM, H100 NVL, A100 PCIe 80GB, and more. Compute Cluster Setup Services: Our technical team has extensive experience in IB networking technology and has successfully completed the setup of five 256-node clusters. For cluster setup services, please contact the customer service team on the Burncloud official website.Starting Price: $0.03/hour -
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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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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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Paperspace
Paperspace
CORE is a high-performance computing platform built for a range of applications. CORE offers a simple point-and-click interface that makes it simple to get up and running. Run the most demanding applications. CORE offers limitless computing power on demand. Enjoy the benefits of cloud computing without the high cost. CORE for teams includes powerful tools that let you sort, filter, create, and connect users, machines, and networks. It has never been easier to get a birds-eye view of your infrastructure in a single place with an intuitive and effortless GUI. Our simple yet powerful management console makes it easy to do things like adding a VPN or Active Directory integration. Things that used to take days or even weeks can now be done with just a few clicks and even complex network configurations become easy to manage. Paperspace is used by some of the most advanced organizations in the world.Starting Price: $5 per month -
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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 -
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Lumino
Lumino
The first integrated hardware and software compute protocol to train and fine-tune your AI models. Lower your training costs by up to 80%. Deploy in seconds with open-source model templates or bring your own model. Seamlessly debug containers with access to GPU, CPU, Memory, and other metrics. You can monitor logs in real time. Trace all models and training sets with cryptographic verified proofs for complete accountability. Control the entire training workflow with a few simple commands. Earn block rewards for adding your computer to the network. Track key metrics such as connectivity and uptime. -
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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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Azure Virtual Machines
Microsoft
Migrate your business- and mission-critical workloads to Azure infrastructure and improve operational efficiency. Run SQL Server, SAP, Oracle® software and high-performance computing applications on Azure Virtual Machines. Choose your favorite Linux distribution or Windows Server. Deploy virtual machines featuring up to 416 vCPUs and 12 TB of memory. Get up to 3.7 million local storage IOPS per VM. Take advantage of up to 30 Gbps Ethernet and cloud’s first deployment of 200 Gbps InfiniBand. Select the underlying processors – AMD, Ampere (Arm-based), or Intel - that best meet your requirements. Encrypt sensitive data, protect VMs from malicious threats, secure network traffic, and meet regulatory and compliance requirements. Use Virtual Machine Scale Sets to build scalable applications. Reduce your cloud spend with Azure Spot Virtual Machines and reserved instances. Build your private cloud with Azure Dedicated Host. Run mission-critical applications in Azure to increase resiliency. -
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DataCrunch
DataCrunch
Up to 8 NVidia® H100 80GB GPUs, each containing 16896 CUDA cores and 528 Tensor Cores. This is the current flagship silicon from NVidia®, unbeaten in raw performance for AI operations. We deploy the SXM5 NVLINK module, which offers a memory bandwidth of 2.6 Gbps and up to 900GB/s P2P bandwidth. Fourth generation AMD Genoa, up to 384 threads with a boost clock of 3.7GHz. We only use the SXM4 'for NVLINK' module, which offers a memory bandwidth of over 2TB/s and Up to 600GB/s P2P bandwidth. Second generation AMD EPYC Rome, up to 192 threads with a boost clock of 3.3GHz. The name 8A100.176V is composed as follows: 8x RTX A100, 176 CPU core threads & virtualized. Despite having less tensor cores than the V100, it is able to process tensor operations faster due to a different architecture. Second generation AMD EPYC Rome, up to 96 threads with a boost clock of 3.35GHz.Starting Price: $3.01 per hour -
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Amazon EC2 Trn2 Instances
Amazon
Amazon EC2 Trn2 instances, powered by AWS Trainium2 chips, are purpose-built for high-performance deep learning training of generative AI models, including large language models and diffusion models. They offer up to 50% cost-to-train savings over comparable Amazon EC2 instances. Trn2 instances support up to 16 Trainium2 accelerators, providing up to 3 petaflops of FP16/BF16 compute power and 512 GB of high-bandwidth memory. To facilitate efficient data and model parallelism, Trn2 instances feature NeuronLink, a high-speed, nonblocking interconnect, and support up to 1600 Gbps of second-generation Elastic Fabric Adapter (EFAv2) network bandwidth. They are deployed in EC2 UltraClusters, enabling scaling up to 30,000 Trainium2 chips interconnected with a nonblocking petabit-scale network, delivering 6 exaflops of compute performance. The AWS Neuron SDK integrates natively with popular machine learning frameworks like PyTorch and TensorFlow. -
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Amazon EC2 Inf1 Instances
Amazon
Amazon EC2 Inf1 instances are purpose-built to deliver high-performance and cost-effective machine learning inference. They provide up to 2.3 times higher throughput and up to 70% lower cost per inference compared to other Amazon EC2 instances. Powered by up to 16 AWS Inferentia chips, ML inference accelerators designed by AWS, Inf1 instances also feature 2nd generation Intel Xeon Scalable processors and offer up to 100 Gbps networking bandwidth to support large-scale ML applications. These instances are ideal for deploying applications such as search engines, recommendation systems, computer vision, speech recognition, natural language processing, personalization, and fraud detection. Developers can deploy their ML models on Inf1 instances using the AWS Neuron SDK, which integrates with popular ML frameworks like TensorFlow, PyTorch, and Apache MXNet, allowing for seamless migration with minimal code changes.Starting Price: $0.228 per hour -
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Bright Cluster Manager
NVIDIA
NVIDIA Bright Cluster Manager offers fast deployment and end-to-end management for heterogeneous high-performance computing (HPC) and AI server clusters at the edge, in the data center, and in multi/hybrid-cloud environments. It automates provisioning and administration for clusters ranging in size from a couple of nodes to hundreds of thousands, supports CPU-based and NVIDIA GPU-accelerated systems, and enables orchestration with Kubernetes. Heterogeneous high-performance Linux clusters can be quickly built and managed with NVIDIA Bright Cluster Manager, supporting HPC, machine learning, and analytics applications that span from core to edge to cloud. NVIDIA Bright Cluster Manager is ideal for heterogeneous environments, supporting Arm® and x86-based CPU nodes, and is fully optimized for accelerated computing with NVIDIA GPUs and NVIDIA DGX™ systems. -
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CloudPe
Leapswitch Networks
CloudPe is a global cloud solutions provider offering scalable and secure cloud technologies tailored for businesses of all sizes. As a collaborative venture between Leapswitch Networks and Strad Solutions, CloudPe combines extensive industry expertise to deliver innovative services. Key Offerings: Virtual Machines: High-performance VMs designed for various business needs, including hosting websites, building applications, and data processing. GPU Instances: NVIDIA-powered GPUs for AI, machine learning, and high-performance computing, available on-demand. Kubernetes-as-a-Service: Simplified container orchestration for deploying and managing containerized applications efficiently. S3-Compatible Storage: Highly scalable and cost-effective storage solutions. Load Balancers: Intelligent load balancing to distribute traffic evenly across resources, ensuring fast and reliable performance. Why Choose CloudPe? 1. Reliability 2. Cost Efficiency 3. Instant DeploymentStarting Price: ₹930/month -
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Dataoorts GPU Cloud
Dataoorts
Dataoorts: Revolutionizing GPU Cloud Computing Dataoorts is a cutting-edge GPU cloud platform designed to meet the demands of the modern computational landscape. Launched in August 2024 after extensive beta testing, it offers revolutionary GPU virtualization technology, empowering researchers, developers, and businesses with unmatched flexibility, scalability, and performance. The Technology Behind Dataoorts At the core of Dataoorts lies its proprietary Dynamic Distributed Resource Allocation (DDRA) technology. This breakthrough allows real-time virtualization of GPU resources, ensuring optimal performance for diverse workloads. Whether you're training complex machine learning models, running high-performance simulations, or processing large datasets, Dataoorts delivers computational power with unparalleled efficiency.Starting Price: $0.20/hour -
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XRCLOUD
XRCLOUD
GPU cloud computing is a GPU-based computing service with real-time, high-speed parallel computing and floating-point computing capacity. It is ideal for various scenarios such as 3D graphics applications, video decoding, deep learning, and scientific computing. GPU instances can be managed just like a standard ECS with speed and ease, which effectively relieves computing pressures. RTX6000 GPU contains thousands of computing units and shows substantial advantages in parallel computing. For optimized deep learning, massive computing can be completed in a short time. GPU Direct seamlessly supports the transmission of big data among networks. Built-in acceleration framework, it can focus on the core tasks by quick deployment and fast instance distribution. We offer optimal cloud performance at a transparent price. The price of our cloud solution is open and cost-effective. You may choose to charge on-demand, and you can also get more discounts by subscribing to resources.Starting Price: $4.13 per month -
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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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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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LeaderGPU
LeaderGPU
Conventional CPUs can no longer cope with the increased demand for computing power. GPU processors exceed the data processing speed of conventional CPUs by 100-200 times. We provide servers that are specifically designed for machine learning and deep learning purposes and are equipped with distinctive features. Modern hardware based on the NVIDIA® GPU chipset, which has a high operation speed. The newest Tesla® V100 cards with their high processing power. Optimized for deep learning software, TensorFlow™, Caffe2, Torch, Theano, CNTK, MXNet™. Includes development tools based on the programming languages Python 2, Python 3, and C++. We do not charge fees for every extra service. This means disk space and traffic are already included in the cost of the basic services package. In addition, our servers can be used for various tasks of video processing, rendering, etc. LeaderGPU® customers can now use a graphical interface via RDP out of the box.Starting Price: €0.14 per minute -
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Vast.ai
Vast.ai
Vast.ai is the market leader in low-cost cloud GPU rental. Use one simple interface to save 5-6X on GPU compute. Use on-demand rentals for convenience and consistent pricing. Or save a further 50% or more with interruptible instances using spot auction based pricing. Vast has an array of providers that offer different levels of security: from hobbyists up to Tier-4 data centers. Vast.ai helps you find the best pricing for the level of security and reliability you need. Use our command line interface to search the entire marketplace for offers while utilizing scriptable filters and sort options. Launch instances quickly right from the CLI and easily automate your deployment. Save an additional 50% or more by using interruptible instances and auction pricing. The highest bidding instances run; other conflicting instances are stopped.Starting Price: $0.20 per hour -
43
JarvisLabs.ai
JarvisLabs.ai
We have set up all the infrastructure, computing, and software (Cuda, Frameworks) required for you to train and deploy your favorite deep-learning models. You can spin up GPU/CPU-powered instances directly from your browser or automate it through our Python API.Starting Price: $1,440 per month -
44
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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Amazon SageMaker Model Training reduces the time and cost to train and tune machine learning (ML) models at scale without the need to manage infrastructure. You can take advantage of the highest-performing ML compute infrastructure currently available, and SageMaker can automatically scale infrastructure up or down, from one to thousands of GPUs. Since you pay only for what you use, you can manage your training costs more effectively. To train deep learning models faster, SageMaker distributed training libraries can automatically split large models and training datasets across AWS GPU instances, or you can use third-party libraries, such as DeepSpeed, Horovod, or Megatron. Efficiently manage system resources with a wide choice of GPUs and CPUs including P4d.24xl instances, which are the fastest training instances currently available in the cloud. Specify the location of data, indicate the type of SageMaker instances, and get started with a single click.
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Brev.dev
NVIDIA
Find, provision, and configure AI-ready cloud instances for dev, training, and deployment. Automatically install CUDA and Python, load the model, and SSH in. Use Brev.dev to find a GPU and get it configured to fine-tune or train your model. A single interface between AWS, GCP, and Lambda GPU cloud. Use credits when you have them. Pick an instance based on costs & availability. A CLI to automatically update your SSH config ensuring it's done securely. Build faster with a better dev environment. Brev connects to cloud providers to find you a GPU at the best price, configures it, and wraps SSH to connect your code editor to the remote machine. Change your instance, add or remove a GPU, add GB to your hard drive, etc. Set up your environment to make sure your code always runs, and make it easy to share or clone. You can create your own instance from scratch or use a template. The console should give you a couple of template options.Starting Price: $0.04 per hour -
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Banana
Banana
Banana was started based on a critical gap that we saw in the market. Machine learning is in high demand. Yet, deploying models into production is deeply technical and complex. Banana is focused on building the machine learning infrastructure for the digital economy. We're simplifying the process to deploy, making productionizing models as simple as copying and pasting an API. This enables companies of all sizes to access and leverage state-of-the-art models. We believe that the democratization of machine learning will be one of the critical components fueling the growth of companies on a global scale. We see machine learning as the biggest technological gold rush of the 21st century and Banana is positioned to provide the picks and shovels.Starting Price: $7.4868 per hour -
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Google Cloud TPU
Google
Machine learning has produced business and research breakthroughs ranging from network security to medical diagnoses. We built the Tensor Processing Unit (TPU) in order to make it possible for anyone to achieve similar breakthroughs. Cloud TPU is the custom-designed machine learning ASIC that powers Google products like Translate, Photos, Search, Assistant, and Gmail. Here’s how you can put the TPU and machine learning to work accelerating your company’s success, especially at scale. Cloud TPU is designed to run cutting-edge machine learning models with AI services on Google Cloud. And its custom high-speed network offers over 100 petaflops of performance in a single pod, enough computational power to transform your business or create the next research breakthrough. Training machine learning models is like compiling code: you need to update often, and you want to do so as efficiently as possible. ML models need to be trained over and over as apps are built, deployed, and refined.Starting Price: $0.97 per chip-hour -
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GPUonCLOUD
GPUonCLOUD
Traditionally, deep learning, 3D modeling, simulations, distributed analytics, and molecular modeling take days or weeks time. However, with GPUonCLOUD’s dedicated GPU servers, it's a matter of hours. You may want to opt for pre-configured systems or pre-built instances with GPUs featuring deep learning frameworks like TensorFlow, PyTorch, MXNet, TensorRT, libraries e.g. real-time computer vision library OpenCV, thereby accelerating your AI/ML model-building experience. Among the wide variety of GPUs available to us, some of the GPU servers are best fit for graphics workstations and multi-player accelerated gaming. Instant jumpstart frameworks increase the speed and agility of the AI/ML environment with effective and efficient environment lifecycle management.Starting Price: $1 per hour -
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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.
