Results for “Accelerate Data Analytics”

Speed up dataframe processing, machine learning and graph analytics on GPUs, often without code changes to pandas, Polars, scikit-learn, NetworkX or Spark, so data teams can iterate faster on large datasets.

Model an AI data center's network, power, cooling and layout in software before hardware arrives, to find design conflicts early, test automation and plan how many GPUs fit within a fixed power budget.

Turn camera streams into events, alerts, searchable footage and summaries using real-time detection and tracking plus vision language models, with privacy rules decided before the first camera is connected.

An assistant that answers staff questions from internal documents and shows its sources, while prompts and data stay under company control. The usual pattern is retrieval over your own content, a served language model, guardrails and permission-aware access.

Run AI models on devices close to where data is created, such as cameras, machines, robots and medical equipment, for low latency, limited connectivity or data that should not leave the site.

Apply AI to medical imaging, genomics, drug discovery and medical devices with open frameworks and GPU tools, while keeping patient data governed and treating clinical validation and regulatory approval as part of the project.

Let AI agents work with real tools and data while limiting what they can reach: sandboxed execution, default-deny network and file access, scoped credentials, guardrails on inputs and outputs, and a complete audit trail.

GPU acceleration for dataframes, machine learning, graph analytics and optimization: CUDA-X Data Science (formerly RAPIDS) with cuDF, cuML and cuGraph, the RAPIDS Accelerator for Apache Spark, and the cuOpt decision optimization engine.

The processors behind NVIDIA systems: the Blackwell and Vera Rubin GPU architectures, Grace and Vera CPUs, RTX PRO GPUs for mixed AI and graphics work, and the NVLink interconnect that joins GPUs into larger systems.

Systems and software for building and running large GPU clusters: DGX systems and SuperPOD designs, Mission Control for operations, Run:ai for GPU scheduling, AI Enterprise for the software layer and the DSX platform for designing and powering AI factories.

Controls for isolating and observing AI workloads on NVIDIA platforms: BlueField DPUs with DOCA and DOCA Argus, GPU confidential computing with attestation, and the OpenShell runtime for containing AI agents.

Tools for building physically accurate virtual copies of factories, warehouses, data centers and products: Omniverse libraries, the OpenUSD open standard, DSX simulation for AI factories, Isaac Sim for robots and Cosmos models for synthetic data.

Computers and software for AI outside the data center: Jetson modules for robots and devices, IGX for industrial and medical systems with functional safety, Holoscan for real-time sensor processing, and JetPack.

AI and simulation for weather, climate, engineering and AI data center energy: Earth-2 open weather models and Earth2Studio, PhysicsNeMo (formerly Modulus) for physics AI, and DSX MaxLPS and DSX Flex for power use in AI factories.

Software for building, customizing, deploying and governing AI assistants and agents in a company: Nemotron open models, NIM microservices, the NeMo libraries, AI Blueprints reference code, the OpenShell agent runtime and the AI Enterprise support layer.

Accelerated tools for genomics, drug discovery, medical imaging and medical devices: Parabricks, BioNeMo, the community MONAI framework, Holoscan for real-time device data and Nemotron models for digital health.

Video analytics for cities, factories, stores and warehouses: the Metropolis platform, DeepStream pipelines, the video search and summarization blueprint, TAO model customization, and Jetson or RTX PRO hardware for deployment.

NVIDIA CUDA-X Data Science, known until August 2026 as RAPIDS, is a collection of open source GPU libraries for data science: cuDF for dataframes, cuML for machine learning and cuGraph for graph analytics. Several of them can speed up existing pandas, scikit-learn or NetworkX code without code changes.

NVIDIA BlueField is a family of data processing units (DPUs) that sit in servers and storage systems and run networking, storage and security services on their own processors and accelerators, so host CPUs and GPUs are left for application work.

NVIDIA Cosmos is an open platform of world foundation models and tools for physical AI. Cosmos 3 reasons over images and video and generates video, sound and robot actions; Curator, Evaluator and Cosmos Framework cover data, scoring and post-training. Licenses differ by model (OpenMDW 1.1 for Cosmos 3).

NVIDIA DSX is a platform, not a single product: a set of reference designs, simulation tools, open-source operations software, power management and data-exchange schemas for designing, building and running AI factories, with each part usable by different partners in the build.

DeepStream is NVIDIA's toolkit for building real-time video and multi-sensor analytics pipelines on GPUs and Jetson devices. It is based on GStreamer and part of Metropolis. Its source code has been on GitHub under Apache-2.0 since version 9.0, while the prebuilt runtime libraries stay under an NVIDIA license.

NVIDIA Metropolis is a vision AI application platform and partner ecosystem. It bundles models, libraries and blueprints, such as the Video Search and Summarization (VSS) blueprint, DeepStream and TAO, for building video analytics agents that turn camera streams into events, alerts, search and reports.

NVIDIA NIM packages an AI model, an inference engine and its runtime into a container with standard APIs, so teams can self-host models on NVIDIA GPUs in the cloud, a data center, a workstation or at the edge instead of building their own serving stack.

NVIDIA NeMo is an open suite of libraries for preparing data, training and post-training models, evaluating them and adding guardrails to AI agents. Its containerized NeMo Microservices reached their announced sunset date of October 1, 2026; NVIDIA's docs still list the open source NeMo Framework.

NVIDIA Nemotron is NVIDIA's family of open AI models for building agents: reasoning models in several sizes plus models for vision, retrieval, speech and safety. NVIDIA publishes the weights, much of the training data and the training recipes, and the models run on common open inference engines or as NIM.

NVIDIA Omniverse is a set of GPU-accelerated libraries, APIs and services for building physically based 3D simulations and digital twins on OpenUSD data. NVIDIA states it has been free for development, production and redistribution since May 2026; enterprise support needs an AI Enterprise license.

xAI's Colossus cluster in Memphis started with 100,000 NVIDIA Hopper GPUs, connected with NVIDIA Spectrum-X Ethernet (SN5600 switches and BlueField-3 SuperNICs) to train Grok models. NVIDIA reports 95 percent data throughput, compared with the 60 percent it attributes to standard Ethernet at this scale.