How to Design an AI Infrastructure & Key Components ['25]

AI infrastructure is the foundation of current AI applications, combining specialized hardware, software, and operating methods to meet AI needs.

Businesses across various industries utilize it to integrate AI into products and processes, such as chatbots (e.g., ChatGPT), facial/speech recognition, and computer vision.

This article explains how AI infrastructure works, its key components, and how it differs from traditional IT infrastructure.

What is AI Infrastructure?

AI (artificial intelligence) infrastructure, also known as an AI stack, refers to the integrated hardware and software environment required to develop, train, and deploy ML and AI applications.

Some examples of applications that rely on AI infrastructure include Google Translate, OpenAI’s GPT, and Google Assistant.

AI Infrastructure vs. traditional IT Infrastructure

Traditional IT systems are designed for general-purpose computing, whereas AI infrastructure is built explicitly for the high-performance computing demands of AI/ML tasks.

AI infrastructure relies on GPUs (Graphics Processing Units) and often TPUs (Tensor Processing Units) to handle the massive computations of model training. GPUs (and TPUs) offer parallel processing capabilities, making them well-suited for handling large-scale matrix multiplications.

Traditional IT environments typically use traditional central processing units (CPUs)for web, database, or ERP systems. These environments mostly handle tasks such as web traffic or storing data.

AI infrastructure stack comprises ML/DL frameworks (such as TensorFlow and PyTorch), libraries (NumPy and Pandas), and languages (Python and CUDA), as well as distributed computing frameworks (Spark and Hadoop) for managing models.

By contrast, traditional IT infrastructure typically runs general-purpose software (web servers, databases, business applications) and lacks these AI-specific libraries.

How AI Infrastructure supports Generative AI

Generative AI models, such as GPT-4 (LLM) or DALL-E (text-to-image models), create new data and demand an extraordinary level of computational infrastructure to develop and deploy.

Cloud providers (such as Azure, AWS, and Google Cloud) and AI-focused data centers build ultra-large GPU clusters to support large-scale AI workloads.

For example, Amazon’s “UltraCluster,” with over 20,000 GPUs, is designed to handle the massive computational requirements of modern AI and machine learning models, particularly those used in deep learning.¹

How AI Infrastructure works and key components

Data storage:

This may involve on-premises or cloud-based data lakes, distributed file systems, data warehouses, and scalable storage solutions. For example, SQL/NoSQL databases for structured data and Hadoop HDFS or cloud object storage for raw files.

Because data volumes are so large, AI storage often emphasizes not just capacity but low latency access using NVMe SSDs and parallel file systems to keep feeding the compute layer without bottlenecks.

Compute resources:

GPUs (Graphics Processing Units) are the most common compute engines for AI. AI servers typically contain multiple GPUs to scale out training jobs.

In addition to GPUs, some infrastructures use TPUs (Tensor Processing Units) for AI tensor computations. Other accelerators include FPGAs and ASICs, which are used for specific needs such as low-latency inference or energy-efficient edge AI.

Networking:

GPUs on different servers must synchronize model parameters frequently. AI infrastructure utilizes high-bandwidth, low-latency networks to facilitate the rapid transfer of large volumes of data.

AI libraries:

Machine learning frameworks, such as TensorFlow, PyTorch, MXNet, or JAX, offer programming interfaces that enable the definition of neural network models and the training of algorithms on the underlying hardware.

These frameworks are often integrated with the compute layer to use multiple GPUs transparently such as PyTorch’s distributed data parallel.

Orchestration and MLOps tools:

Orchestration tools help manage computing resources and workflows. For example, Kubernetes (with Kubeflow for AI) or Apache Spark’s cluster manager can schedule ML jobs across a cluster.

They include features for versioning datasets and models, tracking experiments, and continuous integration/delivery for ML. Traditional infrastructure lacks such ML-specific orchestration.

How to build AI Infrastructure

AI infrastructure can be likened to a stack with several levels, each of which plays a role in the pipeline that spans from data management to deploying AI models.

1. Cloud vs. on-premises: The initial decision is whether to use cloud infrastructure, develop on-premises, or take a hybrid strategy.

Cloud-based vs on-premises AI Infrastructure

The choice between cloud-based and on-premises depends on cost considerations, security requirements, and organizational capabilities.

Cloud services eliminate the significant upfront investment, while on-premises require purchasing expensive hardware (e.g., GPU servers) and investing in data center space. However, once purchased, on-prem hardware can be used at a fixed cost.

While cloud’s unit pricing is often higher, it offers flexibility; you pay only when needed and can shut down resources when idle. For example, the cost of an NVIDIA DGX H200, an 8-GPU on-premises AI system, ranges from $ 400,000 to $ 500,000.²

On-demand, the comparable cloud solution (AWS’s p5.48xlarge instance with 8 NVIDIA H100 GPUs) costs approximately $84 per hour. With constant use, that comes to about $ 735,000 annually; thus, the initial investment would be recouped in less than a year.

All major cloud providers support auto-scaling groups, and your AI service can automatically shrink based on load. On-premises infrastructure is limited to the servers and GPUs.

2. Key components: Building AI infrastructure means assembling the right combination of hardware and software components. On the hardware side, the central components are the compute accelerators, and supporting hardware includes high-memory servers and large-scale storage solutions.
3. Scalability: As AI projects and models become more complex, datasets expand. This means your AI infrastructure may require more powerful machines or GPUs, as well as additional nodes in your cluster. For example, using a scalable distributed file system that can grow in capacity.
4. Cost considerations: There are two major cost models: Capital Expenditure (CapEx) vs Operational Expenditure (OpEx). On-premises infrastructure entails capital expenditures (CapEx), such as purchasing hardware and building data center capacity. Cloud shifts costs to Operational Expenditure, offering an on-demand model. This enables users to avoid significant upfront costs and is efficient for variable or unpredictable workloads. For example, for constant heavy utilization, investing in on-premises solutions may be more cost-effective, whereas for experimental workloads, an on-demand cloud is ideal.

How web-scraped data enhances AI workflows

Many AI models rely on web-scraped text (and images), such as OpenAI’s GPT Series, Google’s LLMs, and Meta’s LLaMA. For instance, the GPT-3 training dataset included hundreds of billions of tokens from Common Crawl.³

Web-scraped corpora include informal social media language, multiple dialects and languages, current events, and historical text. This diversity helps models grasp different styles. Unlike curated datasets that might be static or domain-limited, continuous data scraping can feed AI systems real-time information.

External Links

• 1. How AWS engineers infrastructure to power generative AI. US About Amazon
• 2. NVIDIA DGX Components, Pricing, and other FAQs | TRG Datacenters. TRG Datacenters
• 3. https://facctconference.org/static/papers24/facct24-148.pdf#:~:text=size%2C%20diversity%2C%20and%20free%20of,training