Access optimized solutions for every workload.
To advance data science and AI, organizations need access to tools that can optimize their GPU-based systems, from the cloud and data center to the edge. NVIDIA’s software solutions span all modern workloads, giving IT admins, data scientists, DevOps teams, and developers quick and easy access to what they need.
Data science workflows have traditionally been slow and cumbersome, relying on CPUs to load, filter, and manipulate data and train and deploy models. GPUs substantially reduce infrastructure costs and provide superior performance for end-to-end data science workflows using NVIDIA RAPIDS™ open-source software libraries.
With deep learning neural networks becoming more complex, AI training times have dramatically increased, resulting in lower productivity and higher costs. NVIDIA software solutions significantly accelerate training, resulting in deeper insights in less time, significant cost savings, and faster time to ROI.
There's an increasing demand for sophisticated AI-enabled services like computer vision, conversational AI, and personalized recommender systems. At the same time, datasets are growing, networks are getting more complex, and latency requirements are tightening to meet user expectations. GPU-optimized resources make these services possible with the speed and performance they need.
High-performance computing (HPC) is one of the most essential tools fueling the advancement of science. Over 700 applications in a broad range of domains are accelerated by GPUs computing and popular languages like C, C++, Fortran, and Python are being used to develop, optimize, and deploy these applications, paving the way to scientific discovery.
From data science to virtual environments, enterprises are tackling larger, more complex graphics workloads than ever before. NVIDIA® Quadro RTX™ GPUs with NVIDIA virtual GPU software in the data center delivers the power to meet these demanding visual computing challenges. With advanced technology for AI, real-time ray tracing, and graphics, IT teams can deploy servers capable of a wide range of workloads at a fraction of the cost, space, and power requirements of CPU-based solutions.
The NVIDIA CUDA® Toolkit provides a development environment for creating high-performance, GPU-accelerated applications. With the CUDA Toolkit, you can develop, optimize, and deploy your applications on GPU-accelerated embedded systems, desktop workstations, and laptops enterprise data centers, and cloud-based platforms.
NGC™ has everything you need for your teams to get started, from development to deployment.
Spanning AI, data science, and HPC, the NGC container registry features an extensive range of GPU-optimized software for NVIDIA GPUs.
Tuned, tested and optimized by NVIDIA. NGC containers provide powerful and easy-to-deploy software proven to deliver the fastest results, allowing users to build solutions from a tested framework with complete control.
Many AI applications have common needs: classification, object detection, language translation, text-to-speech, recommender engines, sentiment analysis, and more. When developing applications with these capabilities, it’s much faster to start with a pre-trained model and then tune it for a specific use case. NGC offers pre-trained models for a variety of common AI tasks that are optimized for NVIDIA Tensor Core GPUs and can be easily re-trained by updating just a few layers, saving valuable time.
NGC offers step-by-step instructions and scripts for creating deep learning models, with sample performance and accuracy metrics to compare against your results. With expert guidance on building deep learning models for image classification, language translation, text-to-speech, and more, data scientists can quickly build performance-optimized models by easily adjusting the hyperparameters.
Helm charts automate software deployment on Kubernetes clusters, allowing users to focus on using—rather than installing—their software. NGC hosts Kubernetes-ready Helm charts that make it easy to deploy powerful third-party software. NGC also allows DevOps to push and share their Helm charts, so teams can take advantage of consistent, secure, and reliable environments to speed up development-to-production cycles. With Helm charts, NGC offers NVIDIA GPU Operator, a suite of NVIDIA drivers, container runtime, device plug-in, and management software that IT teams can install on Kubernetes clusters to give users faster access to run their workloads
NGC has industry-specific SDKs, including healthcare, autonomous vehicles, robotics, smart cities and more. With features such as TAO Toolkit, developers and data scientists can build an application quickly and seamlessly—such as re-training object detection and image classification models—and then easily deploy with the NVIDIA DeepStream SDK for intelligent video analytics.
NVIDIA Riva is a framework for building and deploying multimodal conversational AI services that use state-of-the-art deep learning models and run in real time on accelerated computing.
NVIDIA Merlin is a framework for building high-performance, deep learning-based recommender systems that provide better predictions than traditional methods and increase click-through rates
NVIDIA Metropolis makes sense of the flood of data created by trillions of sensors for frictionless retail, traffic engineering in smart cities, optical inspection in factories, patient care in hospitals, and more.
The NVIDIA HPC SDK is a comprehensive suite of compilers, libraries, and software tools that maximize developer productivity, performance, and portability and streamline the building of GPU-accelerated HPC applications.
NVIDIA Modulus is a neural network framework that blends the power of physics and partial differential equations (PDEs) with AI to build more robust models for better analysis.
NVIDIA IndeX® is a 3D volumetric interactive visualization SDK that allows scientists and researchers to visualize and interact with massive datasets, make real-time modifications, and navigate to the most pertinent parts of their data.
IndeX is integrated in ParaView—one of the most popular visualization tools in HPC. The easy access to IndeX features lets users experience real-time interactivity with their existing volume visualization workflows.
With NVIDIA RTX™-accelerated software, designers and artists can boost local workstation performance, render jobs offline, and produce final ray-traced images at a fraction of the time of traditional CPU-based render nodes.
NVIDIA virtual GPU (vGPU) software lets users experience graphics-rich virtual desktops and workstations and run compute-intensive server workloads, including AI, deep learning, data science, and HPC—all on a virtual machine.
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