What Is AI Infrastructure?

AI infrastructure is designed to support the development, deployment, and management of AI models and applications. AI infrastructure includes both hardware and software technologies, purpose-built to enhance performance, scalability, and efficiency for AI workloads.

AI infrastructure requires a comprehensive full-stack approach that seamlessly integrates compute, data, software frameworks, operational pipelines, and networking. This ensures each stage of the AI lifecycle—from data ingestion and model development to inference and continuous improvement—can be efficiently deployed and managed, enabling faster innovation and scalable performance. These components may include:

• Accelerated compute resources:
  • High-performance CPUs and GPUs
  • On-premises servers and/or cloud-based compute instances
  • Edge computing devices for local inference in low-latency or bandwidth-constrained environments
• Energy Efficient Infrastructure:
  • Advanced photonics and lithography
  • Liquid cooling and heat rejection for greater energy and water efficiency
  • Data center electrical system innovation and design
  • Power grid mediation and flexibility
  • Integrated OT (Operational Technology) and  IT (Information Technology) management systems to monitor and operate power and cooling in harmony for the AI factory
• Data storage and management:
  • Data lakes and data warehouses for structured and unstructured data
  • Scalable storage systems (e.g., object stores, distributed file systems
  • Database solutions for specialized data handling
  • Data pipelines and ingestion frameworks
  • Versioning and cataloging of data for traceability and governance
• Networking and connectivity:
  • High effective bandwidth, low latency communication between GPUs
  • Lossless, Remote Direct Memory Access (RDMA) fabrics
  • Deterministic, predictable performance with low tail latency
  • Ethernet purpose-built for AI or InfiniBand
• Software development frameworks:
  • Deep learning libraries
  • Machine learning libraries
  • Distributed training frameworks
  • Data processing frameworks
  • Large language model and generative AI libraries
• Software for production inference at scale
  • Cluster management
  • Containers (e.g., Docker) and orchestration systems (e.g., Kubernetes)
  • Efficient and performant inference stack
• MLOps platforms:
  • Continuous integration/continuous delivery (CI/CD) pipelines, tailored to AI workflows
  • Model-serving platforms
  • Experiment tracking and versioning
  • Monitoring and observability tools for model performance
  • Automated model retraining and model drift detection solutions

AI infrastructure is purpose-built to handle the high-throughput, low-latency demands of training and inference workloads, using specialized hardware like GPUs, high-speed interconnects (e.g., InfiniBand or optical Ethernet), and optimized software stacks. When using high density compute with large power and cooling requirements, it requires mechanical, electrical, and liquid cooling systems with management software to run it all efficiently. In contrast, traditional IT infrastructure is designed for general-purpose computing, storage, and networking tasks—supporting applications like databases, email, and enterprise workloads—typically relying on CPUs and conventional Ethernet networks. Fundamentally, AI infrastructure is optimized for simultaneous execution of thousands of operations across many GPU cores, while IT infrastructure focuses on broad compatibility across single-server workloads.

AI factories operate through a series of interconnected processes and components, each designed to optimize the creation and deployment of AI models.

The required AI infrastructure for AI factories—particularly those running AI reasoning models—includes all of the components previously mentioned plus energy-efficient and fungible technologies. The software components are modular, scalable, and API-driven, integrating every part into a cohesive system. This combination ensures continuous updates and growth, enabling businesses to evolve as AI advances.

AI infrastructure for an AI factory is a tightly integrated stack of high-performance compute, storage, networking, and power and cooling components designed to support the full lifecycle of agentic AI, physical AI and HPC and AI workloads—from data ingestion and preprocessing to training, fine-tuning, and real-time inference. It typically includes GPU-accelerated servers, high-bandwidth, low-latency interconnects like InfiniBand or Ethernet, fast storage systems, power distribution systems, cooling systems, and orchestration software. Built for scalability and efficiency, this infrastructure forms the digital assembly line of an AI factory, enabling continuous iteration and deployment of increasingly intelligent models.

AI requires a departure from traditional corporate IT infrastructure, as it calls for specialized hardware, software, and AI algorithms that heavily rely on parallel processing and the power of accelerated computing. Conventional, non-accelerated data centers can't effectively handle the increasing demands of AI workloads, which often involve processing and analyzing vast amounts of data that can be accessed quickly. 

Modern AI infrastructure requires high-capacity, high-performance storage solutions capable of efficiently storing and retrieving large volumes of data. Consequently, it becomes imperative to build a dedicated storage infrastructure specifically tailored for AI, rather than trying to repurpose existing storage infrastructure. AI software purpose-built for accelerated infrastructure is necessary for saving costs while delivering the highest throughput across the AI pipeline.

Investing in infrastructure that’ll work with unknown, future workloads is a crucial part of a long-term AI strategy. And with accelerated computing—which uses parallel processing on GPUs—demanding applications are sped up while increasing energy efficiency and cost savings in the long run.

Cloud-based solutions offer a cost-effective way to start AI initiatives by reducing acquisition costs and shifting capital expenditures (CapEx) to operational expenditures (OpEx). Yet, while cloud solutions may have lower initial costs, long-term expenses can add up. IT leaders should evaluate the total cost of ownership (TCO) over time and consider factors such as data storage, compute resources, and ongoing maintenance.

In general, it’s important to consider return on investment (ROI) as a key metric, rather than the initial TCO. Building AI infrastructure requires dedicated resources, careful planning, and consideration of cloud and on-premises solutions. By using the right blend of full-stack optimized technology and strategy, organizations can navigate the challenges associated with building AI infrastructure and drive successful outcomes.

To get started, check out the Data Center and AI Infrastructure hub. There, you'll find resources to optimize your data center and AI factory with NVIDIA’s full-stack solutions.