Results for “Build an AI Factory”

Plan, build and run a dedicated GPU cluster for training, fine-tuning and inference, treating compute, network, storage, power, cooling, scheduling and day-two operations as one system rather than separate purchases.

Combine a physically accurate digital twin of the plant, vision AI on the line and simulation-trained robots to plan layouts, catch defects and test changes before they reach the real factory floor.

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.

Build AI agents that plan multi-step tasks, call tools and company systems and report back, with evaluation, guardrails and tracing so their behavior can be measured, limited and improved over time.

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.

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.

Develop robots that perceive, plan and act in changing surroundings by training and testing their behavior in simulation, then running perception and control on an onboard edge computer.

A simulation of a warehouse's layout, robots, people and flows used to test slotting, fleet sizes and safety rules, and to train and check the AI that runs on site, before anything changes in the real building.

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.

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.

Software that serves trained models at a target latency and cost: Dynamo for distributed generative AI serving, Dynamo-Triton (formerly Triton Inference Server), TensorRT LLM for model optimization, NIM packaging and AIPerf benchmarking.

The networks that let many GPUs act together: NVLink inside the rack, Spectrum-X Ethernet and Quantum InfiniBand between racks, BlueField DPUs and ConnectX SuperNICs in each server, and DSX Air for testing a network design in simulation.

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.

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.

Consumer and creator technology on GeForce RTX: DLSS neural rendering, RTX Remix for remastering classic games, NVIDIA ACE for Games for AI characters, the Broadcast app and Studio Drivers.

The stack for robots that perceive and act: Isaac Sim and Isaac Lab for simulation and robot learning, Isaac GR00T for humanoids, Isaac ROS for ROS 2, Cosmos world foundation models and Jetson computers on the robot.

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.

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.

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.

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.

NVIDIA Mission Control is operations software for AI factories built on DGX and GB200/GB300 NVL72 systems. It brings cluster provisioning, Slurm and Kubernetes scheduling, health checks, automated recovery, power policies and building management integration into one supported control plane.

NVIDIA DGX is NVIDIA's own line of AI systems, from the DGX Spark desktop to rack-scale DGX SuperPOD clusters, delivered together with NVIDIA operations software, reference architectures and support, so an organization can build AI infrastructure on one validated stack.

NVIDIA AI Enterprise is a commercial, supported software suite that bundles NVIDIA's AI frameworks, NIM microservices and SDKs with GPU drivers, Kubernetes operators and Run:ai orchestration, adding release branches, security patching and SLA-backed support for production AI.

AI Workbench is NVIDIA's free tool for running containerized, Git-managed AI projects on a laptop, a GPU workstation, a remote server or a cloud instance with the same interface. It handles containers, GPU drivers and remote connections; the latest release, 2026.06.5, shipped in July 2026.

NVIDIA Run:ai is a Kubernetes-based platform that pools GPUs and schedules AI workloads across teams using quotas, priorities and fair sharing, so a shared cluster can serve notebooks, training and inference without each team owning fixed hardware.

NVIDIA Spectrum-X Ethernet is a networking platform for AI clusters that pairs Spectrum-X switches with SuperNICs in the GPU servers, so congestion control, adaptive routing and telemetry work end to end on standards-based Ethernet.

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 Blueprints are open reference workflows for agentic and generative AI use cases such as retrieval-augmented generation, video search and summarization, and research agents. Each provides sample code, deployment files and documentation built on NIM microservices and other NVIDIA libraries, meant to be adapted.

NVIDIA Dynamo is an open source framework that coordinates generative AI inference across many GPUs and nodes. It sits above engines such as vLLM, SGLang and TensorRT LLM and adds disaggregated serving, KV-cache-aware routing, cache offload and latency-driven autoscaling.

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 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.

Deutsche Telekom opened its Industrial AI Cloud in Munich on 4 February 2026, with nearly 10,000 NVIDIA Blackwell GPUs in DGX B200 systems and RTX PRO Servers. Telekom says it was over a third utilized at opening, with Agile Robots and PhysicsX among early users.

BMW Group plans and checks its car plants in a Virtual Factory built on NVIDIA Omniverse and OpenUSD. BMW says digital twins now cover more than 30 production sites, cut collision checks for new models from almost four weeks to about three days, and are projected to lower planning costs by up to 30 percent.

Gefion, operated by the Danish Centre for AI Innovation, is an NVIDIA DGX SuperPOD with 1,528 H100 GPUs, funded by the Novo Nordisk Foundation and Denmark's export and investment fund. It went live in October 2024, placed 21st on the November 2024 TOP500 list, and now serves researchers and companies.

A planned 30-minute conversation at NVIDIA GTC Berlin 2026 on how we rehearse the physical world before we build it: OpenUSD, digital twins of factories and AI factories, physical AI and twins beyond the factory. The recording slot is not yet confirmed.

A 30-minute conversation recorded at NVIDIA GTC Berlin 2026 on who builds on the platform next: NVIDIA Inception, compute access for European founders, open models and AI agents, and startup capital across Europe, the Middle East, Africa and India.

A 30-minute conversation recorded at NVIDIA GTC Berlin 2026 on how intelligence is now manufactured: AI factories, networking, token economics, safe AI agents, power and the grid, and Europe's AI factories.