An AI factory is a purpose-built data center designed to support every stage of the AI lifecycle, from data ingestion and model training to deployment and inference (AI applying what it has learned to new data) at high volumes.
What Do AI Factories Contain?
AI factories are typically maintained in environments such as an AI data center or multitenant neocloud. These environments typically contain large GPU (Graphics Processing Units) clusters, which are faster and more versatile than traditional CPUs. In addition to GPUs, AI infrastructure can include orchestration software and connectivity resources needed to support AI workloads
AI Factories vs Traditional Data Centers
Notable differences between AI factories and traditional data centers include:
- Purpose: AI factories are purpose-built for AI workloads, while traditional data centers are more general-purpose facilities meant for storing and distributing data. As such, AI factories are built to easily support massive parallel processing (MPP), which leverage several processors to distribute AI workloads across multiple nodes at the same time (in parallel).
- Infrastructure: In most cases traditional data centers are not equipped with the hardware to efficiently handle AI workloads. In contrast, AI factories often use baremetal GPU servers for high-performance clusters. Baremetal deployments are often preferable to virtualized GPUs because baremetal deployments are faster and don’t require virtualization.
- Resource-consumption: As AI factories need to be faster and more powerful than traditional data centers to handle AI workloads, they also consume more resources and can be more difficult to manage.
Fundamental similarities among AI factories and traditional data centers include:
- High-bandwidth, low-latency connectivity requirements.
- Compliance with environmental and data-related mandates. These mandates can limit a data center manager’s options related to cooling and data storage.
AI Factory Infrastructure Management Challenges
Although AI factory data centers tend to be denser and more complex than traditional data centers, many of the infrastructure management challenges they face are similar, including:
- Capacity planning at scale.
- Hardware and asset lifecycle management.
- Sustainability and compliance.
- Lack of shared, trustworthy real-time data due to multi-vendor environments and data silos.
How DCIM Software Supports AI Factory Management
DCIM provides several benefits that address not only the challenges faced by traditional data centers, but also those commonly seen in AI factories. For example:
- A large pharmaceutical organization used DCIM to create a centralized asset management database
- An international hospitality chain used DCIM to track energy usage and monitor key environmental parameters, such as air temperature and humidity.
- A major telecommunications vendor used DCIM to improve sustainability in its global data centers.
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