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    BioNeMo

    AI-driven platform for life sciences research and discovery

    DGX Cloud

    NVIDIA’s AI factory in the cloud

    Overview

    NVIDIA technology is power powering solutions for every industry

    Agentic AI

    Build and run agentic AI workflows and microservices

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    AI Infrastructure Solutions

    AI Grids

    Scale AI-native applications by orchestrating workloads across geographically distributed AI infrastructure.

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    Overview

    Distributed Infrastructure for Generative, Agentic, and Physical AI

    Modern AI applications are real time, hyper‑personalized, and data intensive, serving millions of users, agents, and machines across the globe. Telecom operators are uniquely positioned to meet this demand by turning their existing infrastructure into AI grids, bringing AI closer to where intelligence is used.

    An AI grid is a distributed, interconnected, and orchestrated AI infrastructure platform that runs each workload where it performs best. It connects AI factories with regional hubs and edge sites, so data, models, and agents can move securely across distributed sites operating as a unified system. 

    NVIDIA provides the accelerated computing, networking, and software stack that powers AI grids, helping operators rapidly unlock distributed AI capacity and power new AI-native experiences

    NVIDIA and Telecom Leaders Build AI Grids to Optimize Inference on Distributed Networks

    As AI-native applications scale to more users, agents and devices, the telecommunications network is becoming the next frontier for distributing AI.

    Read the Blog

    Easily Build and Deploy AI Grids With NVIDIA

    The NVIDIA AI grid reference design gives operators a unified way to build, deploy, and orchestrate AI across distributed sites.

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    Benefits

    Run Every AI Workload in the Optimal Location

    Predictable Latency

    Predictable Latency

    Keep AI‑native services responsive by running inference on infrastructure closest to users, agents, and machines. This helps operators meet strict service-level agreements (SLAs) for real‑time voice, vision, and control experiences.

    Better Token Economics

    Better Token Economics

    Run token-intensive workloads on nodes with the most cost-efficient compute and networking, reducing data volume over the network and lowering egress costs without sacrificing quality of service.

    Higher Utilization and Resilience

    Higher Utilization and Resilience

    Treat many distributed sites as a single pool of AI capacity to drive up GPU utilization and reduce stranded resources. If a site fails, workloads are automatically rebalanced across the grid to maintain service continuity.

    Concurrency at Scale

    Concurrency at Scale

    Run AI‑native services across many distributed sites to handle massive bursts of concurrent users, applications, and agents, while maintaining consistent quality of experience and cost.

    Products

    The Building Blocks for an AI Grid

    NVIDIA offers a unified platform to equip distributed sites with full‑stack AI infrastructure, turning them into connected, orchestrated AI grids.

    NVIDIA Blackwell Architecture Inbound

    High-Performance GPUs

    In centralized AI factories, rack-scale systems such as the NVIDIA GB300 NVL72 deliver extreme throughput for training, fine‑tuning, and large‑scale reasoning workloads. Across distributed grid sites, NVIDIA RTX PRO™ 6000 Blackwell Server Edition GPUs provide a versatile and cost-effective inference platform that can fit into existing telco footprints with minimal retrofit.

    Explore the RTX PRO Family
    Spectrum-X Ethernet Networking Platform

    NVIDIA Spectrum-X Ethernet

    Through remote direct-memory access (RDMA) Over Converged Ethernet (RoCE) adaptive routing and optimized congestion control, NVIDIA Spectrum-X™ Ethernet accelerates storage performance by nearly 50 percent and reduces communication bottlenecks. With it, enterprises can efficiently scale AI applications while maximizing AI system utilization.

    Learn More About Spectrum-X
    NVIDIA BlueField-3 DPU

    NVIDIA BlueField DPUs

    NVIDIA® BlueField® DPUs offload, accelerate, and isolate infrastructure services across AI factories and globally distributed environments, enabling multiple tenants and workloads to securely and efficiently share a common high‑performance infrastructure. Telecom operators integrate BlueField to enhance security and optimize AI throughput and efficiency.

    Explore BlueField-3 DPUs
    NVIDIA TensorRT LLM

    TensorRT LLM

    NVIDIA TensorRT™ LLM is an open source library for high-performance, real-time large language model (LLM) inference on NVIDIA GPUs. With a modular Python runtime, PyTorch-native authoring, and a stable production API, it’s optimized to maximize throughput, minimize costs, and deliver fast user experiences.

    Learn More About TensorRT LLM
    NVIDIA Dynamo

    NVIDIA Dynamo

    NVIDIA Dynamo is a distributed inference-serving framework for deploying models in multi-node environments at AI factory scale. It streamlines distributed serving by disaggregating inference, optimizing routing, and extending memory through data caching to cost-effective storage tiers.

    Learn More About Dynamo
    AI Deployment With NVIDIA NIM

    NVIDIA NIM Microservices

    NVIDIA NIM™ is a set of easy-to-use inference microservices for accelerating the deployment of foundation models and keeping data secure. NVIDIA NIM is optimized for enterprise-scale inference.

    Learn More About NIM

    Use Cases

    Distributed Intelligence in Action

    Explore how NVIDIA-powered AI grids enable a new class of AI-native applications that demand real-time and cost-efficient access to intelligence at scale.

    1. Physical AI
    2. Real-Time AI
    3. Hyperpersonalization
    4. AI-Native Network Functions

    Physical AI

    Physical AI enables robots, vehicles, cameras, and IoT systems to perceive, reason, and act in the physical world. AI grids let NVIDIA Metropolis run city‑scale vision AI close to cameras for real‑time analytics, while autonomous robots offload heavier planning and reasoning to nearby sites when embedded compute falls short.

    Intelligent Vision AI for Smart Infrastructure

    Real-Time AI

    Interactive AI services like conversational AI assistants depend on tight end‑to‑end latency and jitter control to feel natural and responsive. AI grids execute these workloads on nodes physically close to the data, preserving latency headroom and routing each request to the best-available resources, even during demand spikes or partial outages.

    Conversational AI assistants

    Hyper-personalization

    Personalized AI assistants, media and sports experiences, and enterprise applications must adapt responses in real time for thousands or millions of concurrent sessions. On an AI grid, operators can cache user or tenant context at regional nodes and execute personalization logic and generation closer to users, improving tail latency while keeping the economics of always‑on personalization sustainable.

    Content Performance Analytics

    AI-Native Network Functions

    Network workloads such as RAN, traffic steering, and user‑plane optimization increasingly rely on AI to analyze flows and make real‑time decisions. AI grids run these AI‑native network functions on the same distributed infrastructure as applications, improving utilization and enabling smarter routing, policy enforcement, and quality of experience across the network.

    AI Grids power AI-native network functions

    Next Steps

    Ready to Get Started?

    Build AI grids at scale with the NVIDIA AI Grid reference design, providing a unified hardware and software stack that transforms distributed sites into connected, orchestrated AI infrastructure.

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