Introduction#

This document outlines a software architecture (called the NVIDIA Inference Reference Architecture) intended to help NVIDIA Cloud Partners (NCPs)—sometimes called operators—build a performant, cost-effective solution for large-scale AI inference workloads. It is intended to provide NCPs and ISVs with a Northstar definition that will best serve AI practitioners and cloud operators alike.

The NVIDIA Inference Reference Architecture is focused on providing a modern stack that meets the evolving needs of AI inference infrastructure. While AI workloads have traditionally been dominated by training, the rapid emergence of generative AI and reasoning models has shifted the paradigm toward inference-centric operations. The increasing complexity of serving these models in distributed environments has strengthened the need for a specialized, cloud-native inference infrastructure. Thus, this document will describe how to build such a solution that enables high-throughput, low-latency inference serving for next-generation AI applications.

Specifically, the NVIDIA Inference Reference Architecture provides an overview of a disaggregated, infrastructure-native architecture that is desired for world-class AI inference infrastructure. This term refers to a CSP-like (Cloud Service Provider) elastic cloud environment where compute, networking, and storage resources are allocated in an on-demand model optimized for inference workloads, rather than statically allocated or monolithically structured.

The NVIDIA Inference Reference Architecture provides a suite of complementary components that deliver high-performance, scalable AI inference. Each component is designed to work standalone or integrate seamlessly with others, allowing you to adopt the full stack or select specific components that address your requirements.

This architecture addresses the complete inference lifecycle:

  • Model Optimization: Transform models for maximum GPU efficiency

  • Inference Serving: Handle requests for both traditional ML and GenAI workloads

  • Memory Management: Intelligent caching and high-speed data transfer across memory tiers

  • Cloud Orchestration: Kubernetes-native scaling, scheduling, and infrastructure management

  • Performance Tooling: Benchmarking, tuning, and configuration optimization