Alternatives to CUDA
Compare CUDA alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to CUDA in 2024. Compare features, ratings, user reviews, pricing, and more from CUDA competitors and alternatives in order to make an informed decision for your business.
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1
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. -
2
Mojo
Modular
Mojo 🔥 — a new programming language for all AI developers. Mojo combines the usability of Python with the performance of C, unlocking unparalleled programmability of AI hardware and extensibility of AI models. Write Python or scale all the way down to the metal. Program the multitude of low-level AI hardware. No C++ or CUDA required. Utilize the full power of the hardware, including multiple cores, vector units, and exotic accelerator units, with the world's most advanced compiler and heterogenous runtime. Achieve performance on par with C++ and CUDA without the complexity.Starting Price: Free -
3
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 DRIVE
NVIDIA
Software is what turns a vehicle into an intelligent machine. The NVIDIA DRIVE™ Software stack is open, empowering developers to efficiently build and deploy a variety of state-of-the-art AV applications, including perception, localization and mapping, planning and control, driver monitoring, and natural language processing. The foundation of the DRIVE Software stack, DRIVE OS is the first safe operating system for accelerated computing. It includes NvMedia for sensor input processing, NVIDIA CUDA® libraries for efficient parallel computing implementations, NVIDIA TensorRT™ for real-time AI inference, and other developer tools and modules to access hardware engines. The NVIDIA DriveWorks® SDK provides middleware functions on top of DRIVE OS that are fundamental to autonomous vehicle development. These consist of the sensor abstraction layer (SAL) and sensor plugins, data recorder, vehicle I/O support, and a deep neural network (DNN) framework. -
5
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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NVIDIA Parabricks
NVIDIA
NVIDIA® Parabricks® is the only GPU-accelerated suite of genomic analysis applications that delivers fast and accurate analysis of genomes and exomes for sequencing centers, clinical teams, genomics researchers, and high-throughput sequencing instrument developers. NVIDIA Parabricks provides GPU-accelerated versions of tools used every day by computational biologists and bioinformaticians—enabling significantly faster runtimes, workflow scalability, and lower compute costs. From FastQ to Variant Call Format (VCF), NVIDIA Parabricks accelerates runtimes across a series of hardware configurations with NVIDIA A100 Tensor Core GPUs. Genomic researchers can experience acceleration across every step of their analysis workflows, from alignment to sorting to variant calling. When more GPUs are used, a near-linear scaling in compute time is observed compared to CPU-only systems, allowing up to 107X acceleration. -
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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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NVIDIA Iray
NVIDIA
NVIDIA® Iray® is an intuitive physically based rendering technology that generates photorealistic imagery for interactive and batch rendering workflows. Leveraging AI denoising, CUDA®, NVIDIA OptiX™, and Material Definition Language (MDL), Iray delivers world-class performance and impeccable visuals—in record time—when paired with the newest NVIDIA RTX™-based hardware. The latest version of Iray adds support for RTX, which includes dedicated ray-tracing-acceleration hardware support (RT Cores) and an advanced acceleration structure to enable real-time ray tracing in your graphics applications. In the 2019 release of the Iray SDK, all render modes utilize NVIDIA RTX technology. In combination with AI denoising, this enables you to create photorealistic rendering in seconds instead of minutes. Using Tensor Cores on the newest NVIDIA hardware brings the power of deep learning to both final-frame and interactive photorealistic renderings. -
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FonePaw Video Converter Ultimate
FonePaw
Multifunctional software makes it possible for you to convert, edit and play videos, DVD and audios. In addition, you can also create you own videos or GIF image freely with it. You can convert one video at a time or add several video files for converting simultaneously. It can decode and encode videos on a CUDA-enabled graphics card, leading to your fast and high quality HD and SD video conversion. Your video will not be quality loss. Equipped with NVIDIA's CUDA and AMD APP acceleration technology, you're able to experience 6X faster conversion speed and supports multi-core processor completely. Supported with NVIDIA® CUDA™, AMD®, etc. technologies, FonePaw Video Converter Ultimate can decode and encode videos on a CUDA-enabled graphics card, leading to your fast and high quality HD and SD video conversion. This all-in-one video converter is capable of converting video, audio and DVD files efficiently and even editing them with better effect.Starting Price: $39 one-time payment -
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MATLAB
The MathWorks
MATLAB® combines a desktop environment tuned for iterative analysis and design processes with a programming language that expresses matrix and array mathematics directly. It includes the Live Editor for creating scripts that combine code, output, and formatted text in an executable notebook. MATLAB toolboxes are professionally developed, rigorously tested, and fully documented. MATLAB apps let you see how different algorithms work with your data. Iterate until you’ve got the results you want, then automatically generate a MATLAB program to reproduce or automate your work. Scale your analyses to run on clusters, GPUs, and clouds with only minor code changes. There’s no need to rewrite your code or learn big data programming and out-of-memory techniques. Automatically convert MATLAB algorithms to C/C++, HDL, and CUDA code to run on your embedded processor or FPGA/ASIC. MATLAB works with Simulink to support Model-Based Design. -
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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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Mitsuba
Mitsuba
Mitsuba 2 is a research-oriented retargetable rendering system, written in portable C++17 on top of the Enoki library. It is developed by the Realistic Graphics Lab at EPFL. It can be compiled into many variants which include color handling (RGB, spectral, monochrome), vectorization (scalar, SIMD, CUDA) and differentiable rendering. Mitsuba 2 consists of a small set of core libraries and a wide variety of plugins that implement functionality ranging from materials and light sources to complete rendering algorithms. It strives to retain scene compatibility with its predecessor Mitsuba 0.6. The renderer includes a large automated test suite written in Python, and its development relies on several continuous integration servers that compile and test new commits on different operating systems using various compilation settings (e.g. debug/release builds, single/double precision, etc). -
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Bright for Deep Learning
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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Fortran
Fortran
Fortran has been designed from the ground up for computationally intensive applications in science and engineering. Mature and battle-tested compilers and libraries allow you to write code that runs close to the metal, fast. Fortran is statically and strongly typed, which allows the compiler to catch many programming errors early on for you. This also allows the compiler to generate efficient binary code. Fortran is a relatively small language that is surprisingly easy to learn and use. Expressing most mathematical and arithmetic operations over large arrays is as simple as writing them as equations on a whiteboard. Fortran is a natively parallel programming language with intuitive array-like syntax to communicate data between CPUs. You can run almost the same code on a single CPU, on a shared-memory multicore system, or on a distributed-memory HPC or cloud-based system.Starting Price: Free -
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MediaCoder
MediaCoder
MediaCoder is a universal media transcoding software actively developed and maintained since 2005. It puts together most cutting-edge audio/video technologies into an out-of-box transcoding solution with a rich set of adjustable parameters which let you take full control of your transcoding. New features and latest codecs are added or updated constantly. MediaCoder might not be the easiest tool out there, but what matters here is quality and performance. It will be your swiss army knife for media transcoding once you grasp it. Converting between most popular audio and video formats. H.264/H.265 GPU accelerated encoding (QuickSync, NVENC, CUDA). Ripping BD/DVD/VCD/CD and capturing from video cameras. Enhancing audio and video contents by various filters. An extremely rich set of transcoding parameters for adjusting and tuning. Multi-threaded design and parallel filtering unleashing multi-core power. Segmental Video Encoding technology for improved parallelization. -
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Arm DDT
Arm
Arm DDT is the number one server and HPC debugger in research, industry, and academia for software engineers and scientists developing C++, C, Fortran parallel and threaded applications on CPUs, GPUs, Intel, and Arm. Arm DDT is trusted as a powerful tool for the automatic detection of memory bugs and divergent behavior to achieve lightning-fast performance at all scales. Cross-platform for multiple servers and HPC architectures. Native parallel debugging of Python applications. Has market-leading memory debugging. Outstanding C++ debugging support. Complete Fortran debugging support. Has an offline mode for debugging non-interactively. Handles and visualizes huge data sets. Arm DDT is a powerful parallel debugger, available standalone or as part of the Arm Forge debug and profile suite. Its intuitive graphical interface provides automatic detection of memory bugs and divergent behavior at all scales. -
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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 -
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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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ccminer
ccminer
ccminer is an open-source project for CUDA compatible GPUs (nVidia). The project is compatible with both Linux and Windows platforms. This site is intended to share cryptocurrencies mining tools you can trust. Available open-source binaries will be compiled and signed by us. Most of these projects are open-source but could require technical abilities to be compiled correctly. -
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Darknet
Darknet
Darknet is an open-source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation. You can find the source on GitHub or you can read more about what Darknet can do. Darknet is easy to install with only two optional dependencies, OpenCV if you want a wider variety of supported image types, and CUDA if you want GPU computation. Darknet on the CPU is fast but it's like 500 times faster on GPU! You'll have to have an Nvidia GPU and you'll have to install CUDA. By default, Darknet uses stb_image.h for image loading. If you want more support for weird formats (like CMYK jpegs, thanks Obama) you can use OpenCV instead! OpenCV also allows you to view images and detections without having to save them to disk. Classify images with popular models like ResNet and ResNeXt. Recurrent neural networks are all the rage for time-series data and NLP. -
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Deeplearning4j
Deeplearning4j
DL4J takes advantage of the latest distributed computing frameworks including Apache Spark and Hadoop to accelerate training. On multi-GPUs, it is equal to Caffe in performance. The libraries are completely open-source, Apache 2.0, and maintained by the developer community and Konduit team. Deeplearning4j is written in Java and is compatible with any JVM language, such as Scala, Clojure, or Kotlin. The underlying computations are written in C, C++, and Cuda. Keras will serve as the Python API. Eclipse Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Apache Spark, DL4J brings AI to business environments for use on distributed GPUs and CPUs. There are a lot of parameters to adjust when you're training a deep-learning network. We've done our best to explain them, so that Deeplearning4j can serve as a DIY tool for Java, Scala, Clojure, and Kotlin programmers. -
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NVIDIA Morpheus
NVIDIA
NVIDIA Morpheus is a GPU-accelerated, end-to-end AI framework that enables developers to create optimized applications for filtering, processing, and classifying large volumes of streaming cybersecurity data. Morpheus incorporates AI to reduce the time and cost associated with identifying, capturing, and acting on threats, bringing a new level of security to the data center, cloud, and edge. Morpheus also extends human analysts’ capabilities with generative AI by automating real-time analysis and responses, producing synthetic data to train AI models that identify risks accurately and run what-if scenarios. Morpheus is available as open-source software on GitHub for developers interested in using the latest pre-release features and who want to build from source. Get unlimited usage on all clouds, access to NVIDIA AI experts, and long-term support for production deployments with a purchase of NVIDIA AI Enterprise. -
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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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NVIDIA Virtual PC
NVIDIA
NVIDIA GRID® Virtual PC (GRID vPC) and Virtual Apps (GRID vApps) are virtualization solutions that deliver a user experience that’s nearly indistinguishable from a native PC. With server-side graphics and comprehensive management and monitoring capabilities, GRID future-proofs your VDI environment. Deliver the power of GPU acceleration to every VM (virtual machine) in your organization, creating an unparalleled user experience that leaves your IT team with the time they need to work on business goals and strategy. Whether you’re home or in the office, the way people work is changing dynamically. Today’s applications demand exponentially more graphics power. Although tools like MS teams and Zoom help teams collaborate in real-time, regardless of location, modern workers require multiple monitors to run a range of apps, simultaneously. GPU-acceleration with NVIDIA vPC takes on the needs of the new digital world. -
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Chainer
Chainer
A powerful, flexible, and intuitive framework for neural networks. Chainer supports CUDA computation. It only requires a few lines of code to leverage a GPU. It also runs on multiple GPUs with little effort. Chainer supports various network architectures including feed-forward nets, convnets, recurrent nets and recursive nets. It also supports per-batch architectures. Forward computation can include any control flow statements of Python without lacking the ability of backpropagation. It makes code intuitive and easy to debug. Comes with ChainerRLA, a library that implements various state-of-the-art deep reinforcement algorithms. Also, with ChainerCVA, a collection of tools to train and run neural networks for computer vision tasks. Chainer supports CUDA computation. It only requires a few lines of code to leverage a GPU. It also runs on multiple GPUs with little effort. -
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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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qikkDB
qikkDB
QikkDB is a GPU accelerated columnar database, delivering stellar performance for complex polygon operations and big data analytics. When you count your data in billions and want to see real-time results you need qikkDB. We support Windows and Linux operating systems. We use Google Tests as the testing framework. There are hundreds of unit tests and tens of integration tests in the project. For development on Windows, Microsoft Visual Studio 2019 is recommended, and its dependencies are CUDA version 10.2 minimal, CMake 3.15 or newer, vcpkg, boost. For development on Linux, the dependencies are CUDA version 10.2 minimal, CMake 3.15 or newer, and boost. This project is licensed under the Apache License, Version 2.0. You can use an installation script or dockerfile to install qikkDB. -
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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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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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Nyriad
Nyriad
A New Era of Data Storage Has Arrived. Combining the power of GPUs and CPUs to achieve unprecedented capacity, reliability and security, Nyriad is disrupting conventional thinking of current storage architectures. Developer of a compression technology platform designed to provide advanced data storage services for big data and high-performance computing. The company's platform is a GPU-accelerated block storage device that utilizes massively parallel processing to provide extremely resilient data storage, enabling clients to meet the scale, security, efficiency and performance needs of any type of computing project. Nyriad's concept of 'liquid data', which flows through storage, networking and processing bottlenecks to achieve speed, efficiency and performance, needed cloud support. Nyriad are busy putting the finishing touches on Ambigraph, which will be a significant operating system for exascale computing. -
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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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Torch
Torch
Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation. The goal of Torch is to have maximum flexibility and speed in building your scientific algorithms while making the process extremely simple. Torch comes with a large ecosystem of community-driven packages in machine learning, computer vision, signal processing, parallel processing, image, video, audio and networking among others, and builds on top of the Lua community. At the heart of Torch are the popular neural network and optimization libraries which are simple to use, while having maximum flexibility in implementing complex neural network topologies. You can build arbitrary graphs of neural networks, and parallelize them over CPUs and GPUs in an efficient manner. -
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Polargrid
Polargrid
The brand-new NVIDIA RTX A4000 with 16GB VRAM, 6144 CUDA cores, 48RT cores, and 192 Tensor cores make your projects fly. For only €99 a week, you get 2 units of these for unlimited cloud rendering. The Polargrid RTX Flat has an Octanebench 2020.1 result of 855. This free program is for Blender Artists who have great ideas but no render resources. Polargrid is supporting the Blender Community with this offering. We see this as an investment into the Blender community. The only limitation is the resolution of your output images; The free service is limited to a frame size of 1920 x 1080 pixels. Your projects will render on incredibly fast AMD EPYC ROME 7642 48Core Blade Systems. Much faster and more reliable than any other free or paid Blender cloud service. The machines run on green energy in our new data center in Boden, Sweden.Starting Price: €99 a week -
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NVIDIA EGX Platform
NVIDIA
From rendering and virtualization to engineering analysis and data science, accelerate multiple workloads on any device with the NVIDIA® EGX™ Platform for professional visualization. A highly flexible reference design that combines high-end NVIDIA GPUs with NVIDIA virtual GPU (vGPU) software and high-performance networking, these systems deliver exceptional graphics and compute power, enabling artists and engineers to do their best work—from anywhere—at a fraction of the cost, space, and power of CPU-based solutions. The EGX Platform combined with NVIDIA RTX Virtual Workstation (vWS) software can simplify deployment of a high-performance, cost-effective infrastructure, providing a solution that is tested and certified with industry-leading partners and ISV applications on trusted OEM servers. It enables professionals to do their work from anywhere, while increasing productivity, improving data center utilization, and reducing IT and maintenance costs. -
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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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GPU Mart
Database Mart
A cloud GPU server is a type of cloud computing service that provides access to a remote server equipped with Graphics Processing Units (GPUs). These GPUs are designed to perform complex, highly parallel computations at a much faster rate than conventional central processing units (CPUs). The GPU models include NVIDIA K40, K80, A2, RTX A4000, A10, and RTX A5000. The GPUs combine a range of compute options to cover your needs for various business workloads. Nvidia GPU cloud servers allow designers to rapidly iterate by shortening the rendering time. You can invest your time in innovation rather than rendering or computing, and your team productivity will be significantly improved. Resources allocated to users are fully isolated to ensure data security. GPU Mart protects against DDoS from the edge fast while ensuring legitimate traffic of Nvidia GPU cloud server is not compromised.Starting Price: $109 per month -
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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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Deliver enterprise-class management for running compute and data-intensive distributed applications on a scalable, shared grid. IBM Spectrum Symphony® software delivers powerful enterprise-class management for running compute-intensive and data-intensive distributed applications on a scalable, shared grid. It accelerates dozens of parallel applications for faster results and better utilization of all available resources. With IBM Spectrum Symphony, you can improve IT performance, reduce infrastructure costs and expenses and quickly meet business demands. Get faster throughput and performance for compute-intensive and data-intensive analytics applications to accelerate time-to-results. Achieve higher levels of resource utilization by controlling and optimizing the massive compute power available in your technical computing systems. Reduce infrastructure, application development, deployment and management costs by gaining control of large-scale jobs.
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Ray
Anyscale
Develop on your laptop and then scale the same Python code elastically across hundreds of nodes or GPUs on any cloud, with no changes. Ray translates existing Python concepts to the distributed setting, allowing any serial application to be easily parallelized with minimal code changes. Easily scale compute-heavy machine learning workloads like deep learning, model serving, and hyperparameter tuning with a strong ecosystem of distributed libraries. Scale existing workloads (for eg. Pytorch) on Ray with minimal effort by tapping into integrations. Native Ray libraries, such as Ray Tune and Ray Serve, lower the effort to scale the most compute-intensive machine learning workloads, such as hyperparameter tuning, training deep learning models, and reinforcement learning. For example, get started with distributed hyperparameter tuning in just 10 lines of code. Creating distributed apps is hard. Ray handles all aspects of distributed execution.Starting Price: Free -
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Samadii Multiphysics
Metariver Technology Co.,Ltd
Metariver Technology Co., Ltd. is developing innovative and creative computer-aided engineering (CAE) analysis S/W based on the latest HPC technology and S/W technology including CUDA technology. We will change the paradigm of CAE technology by applying particle-based CAE technology and high-speed computation technology using GPUs to CAE analysis software. Here is an introduction to our products. 1. Samadii-DEM (the discrete element method): works with the discrete element method and solid particles. 2. Samadii-SCIV (Statistical Contact In Vacuum): working with high vacuum system gas-flow simulation. Using Monte Carlo simulation. 3. Samadii-EM (Electromagnetics): For full-field interpretation 4. Samadii-Plasma: Plasma simulation for Analysis of ion and electron behavior in an electromagnetic field. 5. Vampire (Virtual Additive Manufacturing System): Specializes in transient heat transfer analysis. additive manufacturing and 3D printing simulation software -
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OpenVINO
Intel
The Intel Distribution of OpenVINO toolkit makes it simple to adopt and maintain your code. Open Model Zoo provides optimized, pretrained models and Model Optimizer API parameters make it easier to convert your model and prepare it for inferencing. The runtime (inference engine) allows you to tune for performance by compiling the optimized network and managing inference operations on specific devices. It also auto-optimizes through device discovery, load balancing, and inferencing parallelism across CPU, GPU, and more. Deploy your same application across combinations of host processors and accelerators (CPUs, GPUs, VPUs) and environments (on-premise, on-device, in the browser, or in the cloud). -
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Elastic Cloud Server (ECS) provides secure, scalable, on-demand computing resources, enabling you to flexibly deploy applications and workloads. Worry-free comprehensive security protection. Use general computing ECSs, which provide a balance of computing, memory, and network resources. This ECS type is ideal for light- and medium-load applications. Use memory-optimized ECSs, which have a large amount of memory and support ultra-high I/O EVS disks and flexible bandwidths. This ECS type is ideal for applications that process large volumes of data. Use disk-intensive ECSs, which are designed for applications requiring sequential read/write on ultra-large datasets in local storage (such as distributed Hadoop computing) as well as large-scale parallel data processing and log processing. Disk-intensive ECSs are HDD-compatible, feature a default network bandwidth of 10GE, and deliver high PPS and low network latency.Starting Price: $6.13 per month
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Aimersot Video Converter
Aimersot
Being tested with more than 10,000 video files, Aimersoft Video Converter has assured the fastest video converter for Mac and Windows, which runs an unbeatable 90X faster conversion speed than contemporaries. The fast file converter not only supports a large number of media formats but also preserves the original quality in HD and Ultra HD. Aimersoft Video Converter has optimized with APEXTRANS™, NVIDIA® CUDA™, Intel® Core™, and AMD® acceleration technology, which speeds up conversion by up to 90X faster than regular quick video converters. Furthermore, the ultra-fast video converter guarantees high output quality. Aimersoft Video Converter supports a wide range of video and audio formats, which includes the popular MP4, MOV, WMV, MKV, AVI, FLV, MP3, WMA, WAV, AAC, AC3, M4A, and the uncommon VOB, MXF, TS, ASF, SWF, 3GP, 3G2, Divx, XviD, M4B, M4R, AU, APE. The super video converter enables you to convert video to fit your portable media players for easy playback or further editing.Starting Price: $25.95 per year -
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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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Bodo.ai
Bodo.ai
Bodo’s powerful compute engine and parallel computing approach provides efficient execution and effective scalability even for 10,000+ cores and PBs of data. Bodo enables faster development and easier maintenance for data science, data engineering and ML workloads with standard Python APIs like Pandas. Avoid frequent failures with bare-metal native code execution and catch errors before they appear in production with end-to-end compilation. Experiment faster with large datasets on your laptop with the simplicity that only Python can provide. Write production-ready code without the hassle of refactoring for scaling on large infrastructure! -
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NVIDIA Clara
NVIDIA
Clara’s domain-specific tools, AI pre-trained models, and accelerated applications are enabling AI breakthroughs in numerous fields, including medical devices, imaging, drug discovery, and genomics. Explore the end-to-end pipeline of medical device development and deployment with the Holoscan platform. Build containerized AI apps with the Holoscan SDK and MONAI, and streamline deployment in next-generation AI devices with the NVIDIA IGX developer kits. The NVIDIA Holoscan SDK includes healthcare-specific acceleration libraries, pre-trained AI models, and reference applications for computational medical devices. -
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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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Microsoft Cognitive Toolkit
Microsoft
The Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers. CNTK can be included as a library in your Python, C#, or C++ programs, or used as a standalone machine-learning tool through its own model description language (BrainScript). In addition you can use the CNTK model evaluation functionality from your Java programs. CNTK supports 64-bit Linux or 64-bit Windows operating systems. To install you can either choose pre-compiled binary packages, or compile the toolkit from the source provided in GitHub. -
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NVIDIA Holoscan
NVIDIA
NVIDIA® Holoscan is a domain-agnostic AI computing platform that delivers the accelerated, full-stack infrastructure required for scalable, software-defined, and real-time processing of streaming data running at the edge or in the cloud. Holoscan supports a camera serial interface and front-end sensors for video capture, ultrasound research, data acquisition, and connection to legacy medical devices. Use the NVIDIA Holoscan SDK’s data transfer latency tool to measure complete, end-to-end latency for video processing applications. Access AI reference pipelines for radar, high-energy light sources, endoscopy, ultrasound, and other streaming video applications. NVIDIA Holoscan includes optimized libraries for network connectivity, data processing, and AI, as well as examples to create and run low-latency data-streaming applications using either C++, Python, or Graph Composer. -
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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