NVIDIA CUDA-X
AI libraries and microservices with GPU acceleration.
GPU programming is used by developers, researchers, and inventors in a variety of fields to speed up their applications. A stable development environment with highly optimized, domain-specific microservices and libraries is necessary for creating these apps. Built on top of CUDA, NVIDIA CUDA-X is a set of microservices, libraries, tools, and technologies for creating applications that perform noticeably better than alternatives in high performance computing (HPC), data processing, and artificial intelligence.
CUDA-X Microservices
CUDA-X microservices are developer tools, GPU-accelerated libraries, and technologies packaged as cloud APIs that were created by NVIDIA’s CUDA expertise. They are simple to deploy, modify, and integrate into AI, data processing, and HPC systems.
NVIDIA Riva, which offers customizable speech and translation AI; NVIDIA Earth-2, which offers high-resolution climate and weather simulations; NVIDIA cuOpt, which optimizes routing; and NVIDIA NeMo Retriever, which offers responsive retrieval-augmented generation (RAG) capabilities for businesses, are examples of CUDA-X microservices.
CUDA-X Libraries
To make the use of NVIDIA‘s acceleration platform in data processing, AI, and HPC easier, CUDA-X Libraries are constructed on top of CUDA. With more than 400 libraries, the CUDA platform makes it simple for developers to create, optimize, scale, and deploy applications on PCs, workstations, the cloud, and supercomputers.
CUDA-X Data Processing
At a time when datasets are expanding by zettabytes annually, businesses must train models on their private, unique data in order to create transformational AI applications. Through the use of a collection of accelerated libraries that expedite and scale out the processing of picture, text, and tabular data, the CUDA-X data processing platform is intended to address this massive compute problem.
CUDA-X AI
Although it can be difficult to harness, modern AI has the potential to upend numerous sectors. Data processing, feature engineering, machine learning, verification, and deployment are all stages in the development of AI systems, and each one requires processing vast amounts of data and carrying out computationally intensive tasks. The methods and tools required to overcome this obstacle are offered by CUDA-X AI.
CUDA-X HPC
Applications for HPC are found in a wide range of fields, including weather simulation and fluid dynamics. A group of libraries, tools, compilers, and APIs known as HPC assist programmers in resolving the most difficult issues in the world. HPC requires precisely tuned kernels, which HPC provides. GPU-accelerated linear algebra, parallel algorithms, signal processing, and image processing libraries enable compute-intensive applications in computational physics, chemistry, molecular dynamics, and seismic exploration.
Accessible Anywhere
CUDA-X is accessible to many people. Leading cloud systems including AWS, Microsoft Azure, and Google Cloud all use its software-acceleration libraries. NGC offers them for free as standalone downloads or as software stacks in containers. Anywhere NVIDIA GPUs are used, such as on PCs, workstations, servers, supercomputers, cloud computing, and internet of things (IoT) devices, CUDA X libraries can be installed.
With NVIDIA CUDA, developers can boost productivity and enjoy continuous application performance more than a million developers are utilizing it. NVIDIA offers the most practical and efficient way to go, regardless of whether you’re developing a new application or attempting to speed up an old one.
NVIDIA CUDA-X Libraries
When compared to CPU-only alternatives, the NVIDIA CUDA-X Libraries, which are based on CUDA, offer much better performance in a variety of application domains, including as high-performance computing and artificial intelligence.
From the biggest supercomputers on the planet to resource-constrained IoT gadgets and self-driving automobiles, NVIDIA libraries are used everywhere. Consequently, an ever-growing collection of algorithms is implemented in highly optimal ways for consumers. For the simplest approach to begin using GPU acceleration, developers can use NVIDIA libraries when creating new applications or speeding up ones that already exist.
