Build AI factories that scale. Turn your data center into a high-performance AI factory with NVIDIA Enterprise Reference Architectures.
Minimum Criteria for Endorsement by NVIDIA's Design Review Board
- Nodes have passed NVIDIA-Certified for Reference Configuration
- Network design and topology aligned to Enterprise RA design points
- BOM for server vetted by NVIDIA
- BOM for cluster at max scale configuration vetted by NVIDIA
This whitepaper introduces NVIDIA Enterprise Reference Architectures (Enterprise RAs), which provide recommendations for building AI Factories for enterprise-class deployments, ranging from 32 to 256 GPUs. These architectures aim to simplify the deployment of AI infrastructure, reduce complexity, and accelerate time to value.
The NVIDIA RTX PRO AI Factory supports a range of enterprise workloads, including agentic AI inference, physical and industrial AI, visual computing, and high-performance computing for data analytics and simulation. This document outlines the hardware components that define this scalable and modular architecture. This includes guidance regarding the SU design and specifics of Ethernet fabric topologies.
The NVIDIA HGX AI Factory supports a range of enterprise workloads, including AI inference, AI training and fine‑tuning, and large‑scale GPU‑accelerated data analytics. It outlines the hardware components that define a scalable, modular architecture, including SU‑based design guidance and the specifics of the underlying network fabric topologies used to interconnect the cluster.
The NVIDIA NVL72 AI Factory supports the most intensive enterprise AI workloads, including large‑scale foundation model training, fine‑tuning, real‑time reasoning, and complex agentic AI pipelines. This document outlines the GB300 NVL72‑based rack‑scale building block, including guidance on node composition, NVLink domain design, and the network interfaces required to scale this architecture across the data center.
Presents the necessary components, including integrations from our ecosystem partners, automation tools, and deployment strategies. This design can be used by our enterprise partners for integrating accelerated computing, high-performance networking, and AI software for successfully building single tenant enterprise ready AI factories.
Provides an example infrastructure stack build that is geared towards OEMs and NVIDIA partners who intend to build systems that are ready for single-tenant production-grade AI workloads. While hardware components of the infrastructure stack can be modular, the software components of the infrastructure stack are consistent for various workloads, e.g. Inference, Finetuning, & Retrieval Augmented Generation.
This paper helps guide enterprises on how to pack more Inference models on a given set of NVIDIA GPUs using NVIDIA Run:ai, through intelligent scheduling, fractional GPUs, and dynamic resource management. We also explore the impact on performance with the Run:ai scheduler on utilizing fractional GPUs for NIM LLMs.
In this paper, we look at the NVIDIA AI-Q Research Agent blueprint, an agentic system that can generate detailed reports based on both internal and external data. We walk through how to deploy, how to scale and provide sizing guidance.
Offers a standardized and production-ready reference for implementing observability in enterprise AI or HPC environments. Built on top of NVIDIA’s AI infrastructure and Kubernetes-native platforms, this version of the guide specifically focuses on establishing advanced custom dashboard solutions for AI factories, providing administrators and enterprise customers with actionable insights into GPU, CPU, Kubernetes and applications.
Coming Soon.