What is AI infrastructure?

This technology is part of an AI stack, which also includes the frameworks, tools and services that support building and running AI solutions across the entire AI lifecycle. The right AI infrastructure enables developers to effectively create and deploy AI and machine learning (ML) applications such as virtual agents, facial and speech recognition and computer vision.

AI infrastructure has also become crucial for organizations seeking to adopt and scale agentic AI, generative AI (gen AI), AI for IT operations (AIOps) and other AI use cases at scale. A study from Statista shows that global spending on AI infrastructure is expected to almost triple by 2029. The market is projected to grow from USD 334 billion in 2025 to more than USD 900 billion by 2029.¹

AI infrastructure continues to evolve alongside the rapidly expanding end-to-end AI ecosystem. For instance, organizations are relying on a hybrid approach, combining the scalability of public cloud services for training with on-premises infrastructure for reliable high-volume AI inference.

In on-premises and private data center settings, AI accelerators built onto mainframes like the IBM Z® are helping speed developer productivity and modernization goals. This need is especially important for industries like finance and insurance, where strict regulations often dictate where data can be stored and processed.

At the end point of distributed hybrid infrastructure settings, edge AI enables AI models to run on local devices such as cameras and sensors. This approach allows organizations to generate immediate insights without relying on cloud infrastructure for processing.

Agentic AI is also transforming the AI infrastructure landscape. Unlike traditional AI tools that respond to individual queries, these autonomous AI systems can reason, plan and act. In an enterprise setting, agentic AI supports complex, multi-step workflows, prioritizing security, compliance and real-time decision-making. 

Data governance and data sovereignty are the main concerns these days, as the volumes of AI-driven data proliferate from many disparate sources. As a result, organizations are customizing their AI infrastructure to meet AI sovereignty goals, which allows them to control their AI models directly, ensuring organizational independence, security and compliance.

In an IBM Institute of Business Value (IBV) study, respondents predict that AI investment will grow approximately 150% between now and 2030. At the same time, 68% of executives surveyed worry their AI efforts will fail due to lack of integration with core business activities.  

The same study reveals that 57% of the business leaders surveyed believe that their competitive advantage will come primarily from the sophistication of their AI models. To that end, secure, purpose-built AI infrastructure has become essential as AI’s role in business continues to grow.

Enterprises of all different sizes and across a wide range of industries depend on AI infrastructure to help them realize their AI ambitions. Before getting deeper into AI infrastructure and how it works, it’s worth reviewing a few foundational technologies: artificial intelligence, machine learning (ML) and deep learning.

AI is a technology that allows computers to simulate the way humans think and solve problems. When combined with other technologies such as the internet, sensors and robotics, AI can perform tasks that typically require human input. These tasks include operating a vehicle, responding to questions or delivering insights from large volumes of data.

Many of AI’s most popular applications rely on machine learning models, an area of AI that focuses specifically on data and algorithms.

ML is a focus area of AI that uses data and algorithms to imitate the way humans learn, improving the accuracy of its answers over time. ML relies on a few main processes:

• A decision process to make a prediction or classify information
• An error function that evaluates the accuracy of its work
• A model optimization process that reduces discrepancies between known examples and model estimates

An ML algorithm repeats this “evaluate and optimize” process until a defined threshold accuracy for the model has been met.

A subset of ML, deep learning forms the foundation for large language models (LLMs) and other generative AI applications.

It consists of multilayered neural networks modeled after the human brain. These algorithms learn by continuously refining how they recognize complex patterns in unstructured data (for example, images, sound, text). This capability makes deep learning suitable for natural language processing (NLP), which powers chatbots, translation tools and predictive analytics for forecasting customer demands.

To learn more about the nuanced differences between these technologies, check out our blog, “AI versus machine learning versus deep learning versus neural networks: What’s the difference?”

IT infrastructure is a broad term that refers to hardware, software and networking resources enterprises need to manage and run their IT environments effectively.

Both IT infrastructure and AI infrastructure share underlying modern technologies, such as virtualization, hypervisors, containers, open source Kubernetes and microservices for deploying and orchestrating AI workloads at scale. While IT infrastructure consists of technologies that support general business applications, AI infrastructure relies on specialized hardware and software to run and train AI models.

As enterprises discover more ways to use AI, creating the necessary infrastructure to support its development has become paramount. Whether deploying ML to spur innovation in the supply chain or preparing to release generative AI-powered virtual agents, having the right infrastructure in place is crucial.

The primary reason AI projects require bespoke infrastructure is the sheer amount of power needed to run AI workloads. To achieve this kind of power, AI infrastructure depends on the low latency of cloud computing environments. It also relies on the processing power of graphics processing units (GPUs), rather than the more traditional central processing units (CPUs) typical of IT infrastructure environments.  

In addition, AI infrastructure concentrates on hardware and software specially designed for distributed hybrid architectures that support AI and ML tasks.   

AI infrastructure relies on a blend of modern hardware and software. This integrated stack includes compute, network and storage solutions and other resources that support the entire AI lifecycle, spanning model training, deployment and ongoing management.  

Here’s a detailed look at advanced AI infrastructure components.

• Specialized servers: AI infrastructure uses specialized servers and clusters of servers that support high-speed data movement and high-performance storage capabilities. This hardware ranges from on-premises AI-chip servers (for example, IBM Z with the Telum processor) to energy-efficient edge AI servers and cloud-based high-density servers.
• Compute resources: ML and AI tasks require large amounts of compute power. Well-designed AI infrastructure often includes specialized hardware like a graphics processing unit (GPU) and a tensor processing unit (TPU) to provide parallel processing capabilities and speed ML tasks.
• Graphics processing units (GPUs): GPUs, made by NVIDIA or Intel, are electronic circuits used to train and run AI models because of their unique ability to perform many operations at once. Typically, AI infrastructure includes GPU servers to speed matrix and vector computations that are common in AI tasks.
• Tensor processing units (TPUs): TPUs are AI accelerators custom built to speed tensor computations in AI workloads. Their high throughput and low latency make them ideal for many AI and deep learning applications.
• Data storage: AI applications need to train on large datasets to be effective. Enterprises looking to deploy AI products and services need to invest in scalable data storage and management solutions, such as on-premises or cloud-based databases, data warehouses, distributed file systems and data lakes.
• Networking: AI infrastructure incorporates AI networking systems that use AI and ML to support AI workloads at scale and improve network intelligence, performance and security. Key components include high-performance switches and routers, interconnects and compute accelerators for low latency and high-bandwidth performance.
• AI data centers: An AI data center is a facility that houses the specific IT infrastructure needed to train, deploy and deliver AI applications and services. These data centers are equipped to provide advanced computing power, network and storage systems, along with the energy and cooling capacity needed to handle AI workloads.

• Data preprocessing and filtering: Data ingestion from multiple sources first takes place in model training. From there, data processing frameworks and libraries like Pandas, SciPy and NumPy can process and clean large-scale data.
• Machine learning frameworks and libraries: ML frameworks provide specific resources that AI needs to design, train and deploy ML models. ML frameworks like TensorFlow and PyTorch support various capabilities required by AI applications. These functions include the speeding of GPU tasks and functionality critical to the three types of ML training: supervised, unsupervised and reinforcement learning. Such frameworks speed the process of machine learning and give developers the tools they need to develop and deploy AI applications.
• MLOps and AIOps platforms: MLOps (machine learning operations) is a process that involves a set of specific practices to help automate and speed machine learning. MLOps platforms aid developers and engineers in data collection and model training, through validation, troubleshooting and monitoring an application once it has been launched. These platforms underpin AI infrastructure functionality, helping data scientists, engineers and others successfully launch new AI tools, products and services. AIOps extends the MLOps process further, by applying AI and ML to intelligently automate resource deployment, scaling, continuous monitoring and observability and CI/CD pipelines designed for AI workflows.
• Security tools: AI infrastructure weaves in AI security tools with existing cybersecurity infrastructure, such as threat intelligence feeds and security information and event management (SIEM) systems. Encryption and access controls help organizations protect their AI systems and sensitive data across the entire attack surface.

Artificial intelligence as a service (AIaaS) refers to a service platform that delivers AI tools and capabilities with on-demand pricing. This cloud-based software gives users access to these capabilities without requiring them to build their own AI models.

Development and other teams and other users can access these tools through application programming interfaces (APIs) or software development kits (SDKs), which integrate AI functions into their applications and services. For instance, AIaaS can provide natural language processing tools that analyze customer sentiment, helping businesses improve their customer experience without building models.

In addition to supporting the development of cutting-edge applications for customers, enterprises investing in AI infrastructure typically see significant improvements to their processes and workflow.

Here are six of the most common benefits that businesses that develop strong AI infrastructure can expect:

• Increased scalability and flexibility
• Greater performance and speed 
• More collaboration
• Better compliance
• Reduced costs
• Enhanced generative AI and agentic AI capabilities

Because AI infrastructure is typically cloud-based or deployed at the edge, it’s both scalable and flexible. As the datasets needed to power AI applications become larger and more complex, AI infrastructure is designed to scale with them, empowering organizations to increase resources on an as-needed basis.

Flexible cloud and edge infrastructure is highly adaptable and can be scaled up or down more easily than traditional IT infrastructure as an enterprise’s requirements change.

AI infrastructure uses the latest high-performance computing (HPC) technologies available—such as GPUs, TPUs and supercomputing systems to power the ML algorithms that underpin AI capabilities. AI ecosystems have parallel processing capabilities, which significantly reduce the time needed to train ML models.

Because speed is crucial in many AI applications, such as high-frequency trading apps and driverless cars, the improvements in speed and performance are a critical feature of AI infrastructure.

Strong AI infrastructure isn’t just about hardware and software, it also provides developers and engineers with the systems and processes they need to work together more effectively when building AI apps.

Relying on MLOps, a lifecycle for AI development built to streamline and automate ML model creation, AI systems enable engineers to build, share and manage their AI projects more effectively.

As concerns around data privacy and AI have increased, the regulatory environment has become more complex, encompassing data residency and AI sovereignty concerns. As a result, robust AI infrastructure must ensure that privacy laws are observed strictly during data management and data processing in the development of new AI applications. 

AI infrastructure solutions ensure that enterprises closely follow all applicable laws and standards and enforce AI compliance. They also protect user data and guard against legal and reputational damage.

While investing in AI infrastructure can be expensive, the costs associated with trying to develop AI applications and capabilities on traditional IT infrastructure can be even higher. Often, this approach is less cost‑effective than investing in purpose‑built AI infrastructure.

AI infrastructure optimizes resources and applies the best available technology to develop and deploy AI projects. It also provides a better return on investment (ROI) on AI initiatives than trying to accomplish them on outdated, inefficient IT infrastructure.

Generative AI can create its own content (including text, images, video and computer code) from simple user prompts. This capability can increase productivity for both enterprises and individuals, as seen with programs like ChatGPT and Claude AI and in business use cases ranging from customer support to investment analysis. Agentic AI goes further, enabling AI systems to act autonomously in planning and executing multi-step tasks.

AI infrastructure with a solid framework around both generative and agentic AI can help businesses develop these capabilities safely and responsibly.

Here are six steps enterprises of all sizes and industries can take to build the enterprise AI infrastructure they need.