What is an AI factory?

An AI factory is a dedicated infrastructure environment optimized for the large-scale production, fine-tuning, and deployment of AI models. AI factories make AI workloads repeatable, scalable, and cost-effective. Learn more about how AI factories work, their key benefits, and use cases in this article.

What’s the difference between an AI factory and a data center?

Whereas a traditional data center is built to store, manage, and process data for general purpose workloads, such as web applications, email systems and batch processing jobs, an AI factory is designed specifically for AI workloads. This leads to significant differences in compute architecture, networking, and energy requirements.

Compute architecture

While traditional data centers rely on Central Processing Units (CPUs) for compute, AI factories use specialized silicon chips known as AI accelerators. These AI accelerators include:

These AI accelerators are designed for massive parallel processing, enabling them to handle the high volume, simultaneous calculations required for model training and inference.

Networking

AI factories require much lower latency between server nodes than traditional data centers. Large foundation models do not usually fit in a single server’s memory; instead, tasks run across tens of nodes as a single unit. This distributed setup makes minimizing latency critical to prevent “tail latency” from stalling the entire compute cluster.

Network traffic in a traditional data center typically moves in a “north-south” pattern between the user and the server, using standard Ethernet. In AI factories, traffic predominantly moves between GPUs in an “east-west” pattern, and the infrastructure relies on high-bandwidth fabrics such as Infiniband or specially optimized Ethernet that can rapidly move large data volumes without congestion.

Energy requirements

AI factories have higher power densities and cooling requirements than traditional data centers due to the compute-intensive nature of the AI accelerators. To address this challenge, AI factories are typically designed with specially optimized cooling systems to improve operational costs.

Learn how to build an AI factory with our guide to AI infrastructure ›

What can you do with an AI factory?

AI factories are designed to encompass the complete AI lifecycle. What makes this unique is how AI factories enable you to industrialize the processes of training and optimizing AI models, and unlock high-volume AI inference.

Some of the key things that you can do with an AI factory include:

Model training and fine-tuning

You can train new AI models with your data, or fine-tune existing models at scale to optimize them for your specific use cases. This gives you AI models that can deliver intelligence in real-time based on their training data, making this approach ideal when latency is critical.

Retrieval augmented generation (RAG)

You can take pretrained foundational models and augment their understanding with additional data at runtime. This way, models can access information they weren’t trained on and accurately answer questions they wouldn't otherwise be able to. RAG can be a good option when the data you are working with changes frequently, and it would be too costly or time-consuming to train it into your AI models.

Inference and agentic AI

The end goal of AI factories is using the AI models to perform inference and create AI agents at scale to generate value for users. AI agents are agentic AI systems that can autonomously handle complex tasks with minimal human oversight; and AI factories enable organizations to deliver agentic experiences far more quickly to a far larger number of users. Designed for low latency and high throughput, an AI factory can potentially serve tens of thousands of users simultaneously.

How does an AI factory work?

Just as factories take in raw materials and produce physical goods, an AI factory takes inputs and produces intelligence. Simplified, the operational workflow of an AI factory typically looks like this:

These steps involve various key components.

What are the main components of an AI factory?

An AI factory is broadly composed of five infrastructure layers.

Compute infrastructure

AI factories require a high volume of high performance compute to support large-scale AI workloads. This infrastructure is built using dense racks of GPUs or TPUs wired together into clusters. These chips are optimized for the massive parallel matrix multiplications that underpin neural network training and inference, and a hyperscaler AI factory may contain tens of thousands of them working in coordination.

Data pipelines

Before any model can learn, the AI factory ingests raw data. This data can be both structured or unstructured, and come from a variety of sources. Once data has entered the pipeline, it needs to undergo preprocessing before it can be used for training AI models. Preprocessing involves formatting, deduplication, and tokenization - converting raw text into smaller structured units called tokens, which machines can analyze and understand.

Training infrastructure

Once the data has been refined, it can be used in AI models. The AI factory provides the tools and compute power to train and fine-tune these models in an efficient, reproducible way. Orchestration systems (such as Kubernetes or Slurm) schedule jobs, manage failures, and coordinate gradient updates across nodes, ensuring the training run doesn't collapse when a single silicon chip fails.

MLOps tooling

MLOps tools automate the lifecycle of models in the AI factory. They are responsible for deploying models into production, monitoring the performance of deployed models, and feeding inference outputs back into the system for model retraining.

Inference engines

When trained models are deployed into production environments, they can perform inference on new data and generate new tokens. This is the “output” of the AI factory. Inference engines like vLLM or TensorRT-LLM optimize how models are loaded and executed, batching requests efficiently so the same hardware serves as many users as possible.

What are the benefits of an AI factory?

The core benefit of an AI factory is streamlining the end-to-end AI lifecycle, leading to faster deployment of AI models and enhanced decision-making through AI-driven insights. Other benefits include boosting the efficiency and scalability of AI workflows, and providing a controlled environment for security and compliance.

Innovation and decision-making

AI factories accelerate R&D by enabling rapid model training and evaluation, and real-time token generation. This, in turn, drives the generation of high-fidelity outputs that speed up AI-based workflows.

Operational efficiency and scalability

Building a dedicated AI stack with integrated MLOps tools and automation minimizes manual bottlenecks and enables workflow optimizations across the AI lifecycle. The infrastructure is designed to scale seamlessly as data and compute requirements evolve.

Security and compliance

Centralized governance provides a controlled, unified environment for managing data lineage, model versions, and access controls. This makes it significantly easier to adhere to global regulatory standards while protecting sensitive data and intellectual property.

What are the benefits of a private AI factory

AI factories can be deployed on either public cloud or private infrastructure, but private AI factories are emerging as the most popular option. Enterprises are choosing to build AI factories on private clouds for two key reasons:

Learn more about cloud sovereignty in our guide ›

What are the industry use cases for an AI factory?

AI factories can be utilized in any industry for any use case where organizations need to continuously generate actionable intelligence and agentic experiences at scale. Examples include:

Manufacturing

Autonomous vehicles

Healthcare

Financial services

Telecommunications

Productivity workflows

How does Canonical help with AI factories?

Canonical helps organizations build AI factories by delivering an integrated, end-to-end AI infrastructure stack with all the building blocks enterprises need to develop and deploy their project.

What full-stack AI infrastructure looks like, starting with silicon partners

Certified hardware

To build the most efficient, performant AI factory, it is important to mix and match different AI accelerators (GPUs, NPUs, and ASICs) from different vendors depending on the best option for different AI workloads. That’s why Canonical partners with leading silicon vendors to ensure that Ubuntu runs optimally across all major architectures, including AMD, Ampere, Arm, Intel, MediaTek, NVIDIA, Qualcomm, and RISC-V. This means that enterprises can always pick the optimal hardware for their AI factories without being locked into a single software ecosystem.

Ubuntu Certified Hardware accelerates time-to-market for organizations building and scaling AI factories, since they don’t need to spend months validating whether a given piece of hardware will work in their fleet. With Canonical providing standardized, pre-integrated secure boot enablement and firmware delivery, there is no need for enterprises to perform custom OS engineering for each new hardware deployment.

Hardware automation

Some degree of hardware failures and low-level software issues are inevitable, given the high density of compute deployed in AI factories. This can easily impact the uptime and ROI of the system. Canonical MAAS solves this through hardware automation, providing a way to manage bare metal infrastructure as if they were cloud resources. MAAS enables enterprises to provision, reconfigure, and redeploy physical servers through automated workflows rather than manual processes - drastically reducing potential downtime for expensive compute resources.

Read a blog about bare metal automation in AI factories ›

Operating system

Ubuntu is the operating system of choice for AI, and building your AI factory on Ubuntu gives you access to the broadest ecosystem of optimized machine learning tools and libraries. The host OS is also the foundation that directly manages the underlying hardware of the AI factory, and Ubuntu is engineered to run optimally across all major silicon architectures.

Orchestration software

Managing an AI factory requires sophisticated orchestration to allocate resources across virtual machines and containers at scale. Canonical OpenStack, backed by Sunbeam, provides an easy-to-consume and enterprise-grade cloud foundation for AI factories. Canonical Kubernetes is a performant, securely designed, opinionated, and CNCF conformant distribution of Kubernetes that enables organizations to seamlessly orchestrate cloud-native AI workloads.

Long-term security and stability

AI factories are large-scale, long-term investments that require the same stability, security, and lifecycle management as traditional mission-critical IT. Canonical delivers this long-term stability and reliability with Ubuntu Pro, a comprehensive service that provides up to 15 years of security updates for the entire AI/ML stack, ensuring the AI factory is maintained and patched against vulnerabilities for its entire lifecycle.

Learn about Ubuntu Pro ›

Explore Canonical’s AI infrastructure solutions ›